Network Designed Vehicle v.s. Software Defined Vehicle, Ethernet(38)

Network Designed Vehicle: Software Defined Vehicleの本当の目標(goal)
https://qiita.com/kaizen_nagoya/items/0d07e32299dcc040a89e

Network Designed Vehicle , Ethernet(42)
https://qiita.com/kaizen_nagoya/items/a696f8a8cbd141215266

SDR:Software Defined Radioに習うSDV:Software Defined Vehicle, Ethernet(93)
https://qiita.com/kaizen_nagoya/items/e244bba74f2a15154c5d

Network Designed Vehicle v.s. Software Defined Vehicle, Ethernet(38)
https://qiita.com/kaizen_nagoya/items/978403a5c3905ef0478a

arXiv

Search "network defined vehicle".
There are 256 articles, but almost all are not exact "Network Defined Vehicle"
Some articles are significant to this project.

	Network Defined vehicle	Software Defined Vehicle
Arxiv	256	92

Search result on Software Defined Vehicle

MOVESTAR: An Open-Source Vehicle Fuel and Emission Model based on USEPA MOVES
https://arxiv.org/pdf/2008.04986.pdf

[1] United States Environmental Protection Agency. Sources of greenhouse gas emis- sions, Accessed: 2020-08-06. URL https://www.epa.gov/ghgemissions/ sources-greenhouse-gas-emissions.
[2] J. N. Barkenbus. Eco-driving: An overlooked climate change initiative. Energy policy, 38(2): 762–769, 2010.
[3] M. Sivak and B. Schoettle. Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy. Transport Policy, 22:96–99, 2012.
[4] M. Barth and K. Boriboonsomsin. Energy and emissions impacts of a freeway-based dynamic eco-driving system. Transportation Research Part D: Transport and Environment, 14(6):400 – 410, 2009. ISSN 1361-9209. doi:https://doi.org/10.1016/j.trd.2009.01.004. URL http:// www.sciencedirect.com/science/article/pii/S1361920909000121. The interaction of environmental and traffic safety policies.
[5] M. Barth, S. Mandava, K. Boriboonsomsin, and H. Xia. Dynamic eco-driving for arterial corridors. In 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pages 182–188, June 2011. doi:10.1109/FISTS.2011.5973594.
[6] K. Katsaros, R. Kernchen, M. Dianati, and D. Rieck. Performance study of a green light op- timized speed advisory (glosa) application using an integrated cooperative its simulation plat- form. In 2011 7th International Wireless Communications and Mobile Computing Conference, pages 918–923, July 2011. doi:10.1109/IWCMC.2011.5982524.
[7] O. D. Altan, G. Wu, M. J. Barth, K. Boriboonsomsin, and J. A. Stark. Glidepath: Eco-friendly automated approach and departure at signalized intersections. IEEE Transactions on Intelligent Vehicles, 2(4):266–277, Dec 2017. ISSN 2379-8904. doi:10.1109/TIV.2017.2767289.
[8] P.Hao,G.Wu,K.Boriboonsomsin,andM.J.Barth.Eco-approachanddeparture(ead)applica- tion for actuated signals in real-world traffic. IEEE Transactions on Intelligent Transportation Systems, 20(1):30–40, Jan 2019. ISSN 1524-9050. doi:10.1109/TITS.2018.2794509.
[9] Z. Wang, Y. Hsu, A. Vu, F. Caballero, P. Hao, G. Wu, K. Boriboonsomsin, M. J. Barth, A. Kailas, P. Amar, E. Garmon, and S. Tanugula. Early findings from field trials of heavy- duty truck connected eco-driving system. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pages 3037–3042, 2019.
[10] United States Environmental Protection Agency. MOVES and other mobile source emissions models, Accessed: 2020-08-04. URL https://www.epa.gov/moves.
[11] United States Environmental Protection Agency. MOVES2014a user interface reference man- ual, Accessed: 2020-08-06. URL https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey= P100Q3C1.pdf.
[12] United States Environmental Protection Agency. Using MOVES for estimating state and local inventories of onroad greenhouse gas emissions and energy consumption, Accessed: 2020-08-06. URL http://www.ourenergypolicy.org/wp-content/uploads/2016/ 09/P100OW0B.pdf.
[13] UnitedStatesEnvironmentalProtectionAgency.Exhaustemissionratesforheavy-dutyonroad vehicles in moves201x, Accessed: 2020-08-09. URL https://cfpub.epa.gov/si/si_ public_record_report.cfm?Lab=OTAQ&dirEntryId=328830.
[14] J. Warila, E. Nam, L. Landman, and A. Kahan. Light-duty exhaust emission rates in moves2010, Accessed: 2020-08-10. URL http://citeseerx.ist.psu.edu/viewdoc/ download;jsessionid=AAE023D542E3688088AD383D57B1BE19?doi=10.1.1.360. 5490&rep=rep1&type=pdf.
[15] MathWorks. Matlab, Accessed: 2020-08-10. URL https://www.mathworks.com/ products/matlab.html?s_tid=hp_products_matlab.
[16] PTV GROUP. PTV Vissim is the world’s most advanced and flexible traffic simulation software, Accessed: 2020-08-10. URL https://www.ptvgroup.com/en/solutions/ products/ptv-vissim/.
[17] Z. Wang, G. Wu, and M. J. Barth. Cooperative eco-driving at signalized intersections in a partially connected and automated vehicle environment. IEEE Transactions on Intelligent Transportation Systems, 21(5):2029–2038, 2020.

Energy-Aware Graph Task Scheduling in Software-Defined Air-Ground Integrated Vehicular Networks

[1] M. A. Messous, S. M. Senouci, H. Sedjelmaci, and S. Cherkaoui, “A game theory based efficient computation offloading in an UAV network,” IEEE Trans. Veh. Technol., vol. 68, no. 5, pp. 4964–4974, 2019.
[2] Z. Guan, N. Cen, T. Melodia, and S. M. Pudlewski, ”Distributed Joint Power, Association and Flight Control for Massive-MIMO Self-Organizing Flying Drones,” IEEE/ACM Trans. Netw., vol. 28, no. 4, pp. 1491–1505, 2020.
[3] A. Trotta, U. Muncuk, M. Di Felice, and K. R. Chowdhury, “Per- sistent Crowd Tracking Using Unmanned AerIal Vehicle Swarms: A Novel Framework for Energy and Mobility Management,” IEEE Veh. Technol. Mag.., vol. 15, no. 2, pp. 96–103, 2020.
[4] G. Secinti, A. Trotta, S. Mohanti, M. Di Felice, and K. R. Chowd- hury, ”FOCUS: Fog Computing in UAS Software-Defined Mesh Networks,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 6, pp. 2664- 2674, 2020.
[5] S. Jacek, “Fukushima Plants Radiation Levels Monitored with an UAV,” https://theaviationist. com/2014/01/29/fukushima- japan-uav/, Jan. 29. 2014.
[6] L. Abend, “Pilot: The mystery of Kobe Bryant’s chopper crash,” https://www. cnn. com/2020/01/28/opinions/kobe- bryant-helicopter-crash-abend/index. html, Jan. 28. 2020.
[7] M. Krznar, P.Piljek, D. Kotarski, and D. Pavkovic ́, “Modeling, Control System Design and Preliminary Experimental Verification of a Hybrid Power Unit Suitable for Multirotor UAVs,” Energies, vol. 14, no. 9, pp. 1–24, 2021.
[8] M. Liwang, Z. Gao, and X. Wang, “Let’s Trade in the Future! A Futures-Enabled Fast Resource Trading Mechanism in Edge Computing-Assisted UAV Networks,” IEEE J. Selected Areas in Commun., vol. 39, no. 11, pp. 3252–3270, 2021.
[9] M. Liwang, S. Hosseinalipour, Z. Gao, Y. Tang, L. Huang, and H. Dai, “Allocation of computation-intensive graph jobs over vehicular clouds in IoV,” IEEE Internet Things J., vol. 7, no. 1, pp. 311–324, 2019.
[10] M. Liwang, Z. Gao, S. Hosseinalipour, and H. Dai, “Multi-task offloading over vehicular clouds under graph-based representa- tion,” IEEE Int. Conf. Commun. (ICC), Dublin, Ireland, Jun. 2020, pp. 1–7.
[11] R. Florin, A. Ghazizadeh, P. Ghazizadeh, S. Olariu, and D. C. Marinescu, ”Enhancing Reliability and Availability through Re- dundancy in Vehicular Clouds,” IEEE Trans. Cloud Comput., vol. 9, no. 3, pp. 1061–1074, 2021.
[12] N. Zhang, S. Zhang, P. Yang, O. Alhussein, W. Zhuang, and X. Shen, “Software defined space-air-ground integrated vehicular networks: challenges and solutions,” IEEE Commun. Mag., vol. 55, no. 7, pp. 101–109, 2017.
[13] Y. Wang, W. Chen, T. H. Luan, Z. Su, Q. Xu, R. Li, and N. Chen ”Task Offloading for Post-Disaster Rescue in Un- manned Aerial Vehicles Networks,” IEEE/ACM Trans. Netw., DOI: 10.1109/TNET.2022.3140796, pp. 1–1, 2022.
[14] F. Lyu, P. Yang, H. Wu, C. Zhou, J. Ren, Y. Zhang, and X. Shen”Service-Oriented Dynamic Resource Slicing and Optimiza- tion for Space-Air-Ground Integrated Vehicular Networks,” IEEE Trans. Intell. Transp. Syst., pp.1–1, 2021.
[15] K. Wang, H. Yin, W. Quan, and G. Min, “Enabling collaborative edge computing for software defined vehicular networks,” IEEE Netw., vol. 32, no. 5, pp. 112–117, 2018.
[16] X. Hou, Z. Ren, J. Wang, W. Cheng, Y. Ren, K. C. Chen, and H. Zhang, ”Reliable Computation Offloading for Edge-Computing- Enabled Software-Defined IoV,” IEEE Internet Things J., vol. 7, no. 8, pp. 7097–7111, 2020.
[17] G. Luo, H. Zhou, N. Cheng, Q. Yuan, F. Yang, and X. Shen, “Software defined cooperative data sharing in edge comput- ing assisted 5G-VANET,” IEEE Trans. Mobile Comput., DOI: 10.1109/TMC.2019.2953163, pp. 1–1, 2019.
[18] J. Ghaderi, S. Shakkottai, and R. Srikant, “Scheduling storms and streams in the cloud,” ACM Trans. Modeling and Performance Eval. of Comput. Syst., vol. 1, no. 4, pp. 1–14, 2016.
[19] M. Shafiee and J. Ghaderi, ”Scheduling Coflows With Dependency Graph,” IEEE/ACM Trans. Netw., DOI: 10.1109/TNET.2021.3116133, pp.1–1, 2021.
[20] J. Yu, X. Hao, Z. Cui, P. He, and T. Liu, “Boosting Fairness for Masked Face Recognition,”Proc. IEEE/CVF Int. Conf. Computer Vision (ICCV) Workshops, Virtual, Oct. 2021, pp. 1531–1540.
[21] V. Carletti, P. Foggia, A. Saggese, and M. Vento, “Challenging the time complexity of exact subgraph isomorphism for huge and dense graphs with VF3,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 804–818, 2018.
[22] Z. Gao, M. Liwang, S. Hosseinalipour, H. Dai, and X. Wang, “A truthful auction for graph job allocation in vehicular cloud-assisted networks,” IEEE Trans. Mobile Comput., DOI: 10.1109/TMC.2021.3059803, pp. 1–1, 2021.
[23] T. Bai, J. Wang, Y. Ren, and L. Hanzo, “Energy-efficient compu- tation offloading for secure uav-edge-computing systems,” IEEE Trans. Veh. Technol., vol. 68, no. 6, pp. 6074–6087, 2019.
[24] A. Sacco, F. Esposito, G. Marchetto and P. Montuschi, ”A Self- Learning Strategy for Task Offloading in UAV Networks,” IEEE Trans. Veh. Technol., doi: 10.1109/TVT.2022.3144654, pp. 1–1, 2022.
[25] Z.Yan,P.Cheng,Z.Chen,B.Vucetic,andY.Li,”Two-Dimensional Task Offloading for Mobile Networks: An Imitation Learning Framework,” IEEE/ACM Trans. Netw., vol. 29, no. 6, pp. 2494-2507, 2021.
[26] H. Tout, A. Mourad, N. Kara and C. Talhi, ”Multi-Persona Mobil- ity: Joint Cost-Effective and Resource-Aware Mobile-Edge Com- putation Offloading,” IEEE/ACM Trans. Netw., vol. 29, no. 3, pp. 1408-1421, 2021.
[27] P. A. Apostolopoulos, E. E. Tsiropoulou and S. Papavassiliou, ”Risk-Aware Data Offloading in Multi-Server Multi-Access Edge Computing Environment,” IEEE/ACM Trans. Netw., vol. 28, no. 3, pp. 1405–1418, 2020.
[28] S. Hosseinalipour, A. Nayak, and H. Dai, “Power-aware allocation of graph jobs in geo-distributed cloud networks,” IEEE Trans. Parallel Distrib. Syst., vol. 31, no. 4, pp. 749–765, 2019.
[29] M. Liwang, Z. Gao, S. Hosseinalipour, H. Dai, and X. Wang, “Energy-aware allocation of graph jobs in vehicular cloud computing-enabled software-defined IoV,” IEEE Int. Conf. Comput. Commun. Workshops (INFOCOM WKSHPS), Toronto, ON, Canada, July. 2020, pp. 1–6.
[30] Y. Sahni, J. Cao, L. Yang, and Y. Ji, ”Multihop Offloading of Multiple DAG Tasks in Collaborative Edge Computing,” IEEE Internet Things J., vol. 8, no. 6, pp. 4893–4905, 2021.
[31] L. Shi, Z. Zhang, and T. Robertazzi, “Energy-aware scheduling of embarrassingly parallel jobs and resource allocation in cloud,” IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 6, pp. 1607–1620, 2017.
[32] M. Goudarzi, M. Zamani, and A. T. Haghighat, “A fast hybrid multi-site computation offloading for mobile cloud computing,” Journal of Netw. Comput. Appl., vol. 66, pp. 219–231, 2017.
[33] Y. Geng, Y. Yang, and G. Cao, “Energy-efficient computation offloading for multicore-based mobile devices,” IEEE Int. Conf. Comput. Commun. (INFOCOM), Honolulu, HI, USA, Apr. 2018, pp. 1–6.
[34] F. Sun, F. Hou, N. Cheng, M. Wang, H. Zhou, L. Gui, and X. Shen, “Cooperative task scheduling for computation offloading in vehicular cloud,” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp: 11049–11061, 2018.
[35] P. Kurp, “Green computing,” Commun. ACM, vol. 51, no. 10, pp. 11–13, 2008.
[36] X. Zhu, Y. Li, D. Jin, and J. Lu, “Contact-aware optimal resource allocation for mobile data offloading in opportunistic vehicular networks,” IEEE Trans. Veh. Technol., vol. 66, no. 8, pp. 7384–7399, 2017.
[37] Q. Song, F. C. Zheng, Y. Zeng, and J. Zhang, “Joint beamforming and power allocation for UAV-enabled full-duplex relay,” IEEE Trans. Veh. Technol., vol. 68, no. 2, pp. 1657–1671, 2018.
[38] A. A. Khuwaja, Y. Chen, N. Zhao, M. S. Alouini, and P. Dobbins, “A survey of channel modeling for uav communications,” IEEE Commun. Surveys Tut., vol. 20, no. 4, pp. 2804–2821, 2018.
[39] X. Chen, L. Jiao, W. Li, and X. Fu, “Efficient multi-user compu- tation offloading for mobile-edge cloud computing,” IEEE/ACM Trans. Netw., vol. 24, no. 5, pp. 2795–2808, 2016.
[40] A. Bazzi, C. Campolo, B. M. Masini, A. Molinaro, A. Zanella, and A. O. Berthet, “Enhancing cooperative driving in IEEE 802.11 ve- hicular networks through full-duplex radios,” IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 2402–2016, 2018.
[41] Y.Mao,J.Zhang,andK.B.Letaief,“Jointtaskoffloadingschedul- ing and transmit power allocation for mobile-edge computing systems,” IEEE Int. Conf. Commun. Netw. (WCNC), San Francisco, CA, USA, Mar. 2017, pp. 1–6.
[42] CVXResearch,Inc.,“CVX:Matlabsoftwarefordisciplinedconvex programming, version 2.0 (beta),” 2013.

On the Security of Networked Control Systems in Smart Vehicle and its Adaptive Cruise Control

[1] F. Farivar, S. Barchinezhad, M. Sayad Haghighi, and A. Jol- faei, “Detection and compensation of covert service-degrading intrusions in cyber physical systems through intelligent adaptive control,” in The IEEE International Conference on Industrial Technology, 2019.
[2] K. M. A. Alheeti, A. Gruebler, and K. D. McDonald-Maier, “An intrusion detection system against black hole attacks on the communication network of self-driving cars,” in 6th inter- national conference on emerging security technologies (EST), 2015, pp. 86–91.
[3] R. Mitchell and R. Chen, “Effect of intrusion detection and response on reliability of cyber physical systems,” IEEE Trans- actions on Reliability, vol. 62, no. 1, pp. 199–210, 2013.
[4] R. Mitchell and R. Chen, “Behavior rule specification-based intrusion detection for safety critical medical cyber physical systems,” IEEE Transactions on Dependable and Secure Com- puting, vol. 12, no. 1, pp. 16–30, 2014.
[5] S. Sridhar, A. Hahn, and M. Govindarasu, “Cyber–physical system security for the electric power grid,” Proceedings of the IEEE, vol. 100, no. 1, pp. 210–224, 2011.
[6] A. Mohammadali, M. S. Haghighi, M. H. Tadayon, and A. Mohammadi-Nodooshan, “A novel identity-based key estab- lishment method for advanced metering infrastructure in smart grid,” IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 2834– 2842, 2016.
[7] M. S. Haghighi and K. Mohamedpour, “Neighbor discovery: Security challenges in wireless ad hoc and sensor networks,” in Trends in Telecommunications Technologies. Intech, 2010.
[8] N. Toorchi, M. A. Attari, M. S. Haghighi, and Y. Xiang, “A markov model of safety message broadcasting for vehicular networks,” in IEEE Wireless Communications and Networking Conference (WCNC), 2013, pp. 1657–1662.
[9] M. Harris, “Researcher hacks self-driving car sensors,” IEEE Spectrum, vol. 9, p. , 2015.
[10] BBC News. (2015, accessed on 01.011.2019) Fiat chrysler recalls 1.4 million cars after jeep hack. [Online]. Available: https://www.bbc.com/news/technology- 33650491
[11] E. Woollacott. (2017, accessed on 01.011.2019) Could a hacker hijack your connected car? [Online]. Available: https://www.bbc.com/news/technology- 33650491
[12] M.-J. Kang and J.-W. Kang, “Intrusion detection system using deep neural network for in-vehicle network security,” PloS one, vol. 11, no. 6, p. e0155781, 2016.
[13] A. O. de Sa ́, L. F. R. da Costa Carmo, and R. C. Machado, “Covert attacks in cyber-physical control systems,” IEEE Trans- actions on Industrial Informatics, vol. 13, no. 4, pp. 1641–1651, 2017.
[14] P. Shakouri, A. Ordys, D. S. Laila, and M. Askari, “Adaptive cruise control system: comparing gain-scheduling pi and lq controllers,” IFAC Proceedings Volumes, vol. 44, no. 1, pp. 12 964–12 969, 2011.
[15] V. Tzovla and A. Mehta, “A simplified and integrated approach to model predictive control implementation,” Advances in In- strumentation and Control, p. , 2000.
[16] N.Falliere,L.O.Murchu,andE.Chien,“W32.stuxnetdossier,” White paper, Symantec Corp., Security Response, vol. 5, no. 6, p. 29, 2011.
[17] P. Jokar, H. Nicanfar, and V. C. Leung, “Specification-based intrusion detection for home area networks in smart grids,” in IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2011, pp. 208–213.
[18] Y. Liu, P. Ning, and M. K. Reiter, “False data injection attacks against state estimation in electric power grids,” ACM Trans- actions on Information and System Security (TISSEC), vol. 14, no. 1, p. 13, 2011.
[19] R. Deng, P. Zhuang, and H. Liang, “False data injection attacks against state estimation in power distribution systems,” IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 2871–2881, 2018.
[20] Y. Mo, E. Garone, A. Casavola, and B. Sinopoli, “False data injection attacks against state estimation in wireless sensor networks,” in 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010, pp. 5967–5972.
[21] M. Long, C.-H. Wu, and J. Y. Hung, “Denial of service attacks on network-based control systems: impact and mitigation,” IEEE Transactions on Industrial Informatics, vol. 1, no. 2, pp. 85–96, 2005.
[22] A. Teixeira, I. Shames, H. Sandberg, and K. H. Johansson, “Revealing stealthy attacks in control systems,” in 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012, pp. 1806–1813.
[23] C. Zimmer, B. Bhat, F. Mueller, and S. Mohan, “Time-based intrusion detection in cyber-physical systems,” in Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems. ACM, 2010, pp. 109–118.
[24] M. S. Haghighi, F. Farivar, A. Jolfaei, and M. H. Tadayon, “Intelligent robust control for cyber-physical systems of rotary gantry type under denial of service attack,” Journal of Super- computing, 2019.
[25] M. Sayad Haghighi, F. Farivar, and A. Jolfaei, “Automatic configuration of firewalls in industrial control systems based on the novel concept of z-classification,” under review, 2019.
[26] Z. Xiong, H. Sheng, W. Rong, and D. E. Cooper, “Intelligent transportation systems for smart cities: a progress review,” Science China Information Sciences, vol. 55, no. 12, pp. 2908– 2914, 2012.
[27] B. Mokhtar and M. Azab, “Survey on security issues in vehic- ular ad hoc networks,” Alexandria engineering journal, vol. 54, no. 4, pp. 1115–1126, 2015.
[28] M. Raya, P. Papadimitratos, and J.-P. Hubaux, “Securing vehic- ular communications,” IEEE wireless communications, vol. 13, no. 5, pp. 8–15, 2006.
[29] P. N. Darisini and N. S. Kumari, “A survey of routing protocols for vanet in urban scenarios,” in International Conference on Pattern Recognition, Informatics and Mobile Engineering. IEEE, 2013, pp. 464–467.
[30] D. Djenouri, L. Khelladi, and N. Badache, “Security issues of mobile ad hoc and sensor networks,” in IEEE Communications Surveys Tutorials, vol. 7, no. 4. IEEE Communications Society,2005, pp. 2–28.
[31] H. Hasrouny, A. E. Samhat, C. Bassil, and A. Laouiti, “Vanet
security challenges and solutions: A survey,” Vehicular Com-
munications, vol. 7, pp. 7–20, 2017.
[32] M. Sayad Haghighi and Z. Aziminejad, “Highly anonymous
mobility-tolerant location-based onion routing for vanets,” IEEE
Internet of Things Journal, in press, 2019.
[33] A. Jolfaei and K. Kant, “Privacy and security of con-
nected vehicles in intelligent transportation system,” in Annual IEEE/IFIP International Conference on Dependable Systems and Networks–Supplemental Volume (DSN-S). IEEE, 2019,pp. 9–10.
[34] S. Kumar and K. S. Mann, “Detection of multiple malicious
nodes using entropy for mitigating the effect of denial of service attack in vanets,” in 4th International Conference on Computing Sciences (ICCS). IEEE, 2018, pp. 72–79.
[35] J. Liang, J. Chen, Y. Zhu, and R. Yu, “A novel intrusion detection system for vehicular ad hoc networks (vanets) based on differences of traffic flow and position,” Applied Soft Com- puting, vol. 75, pp. 712–727, 2019.

Enable an Open Software Defined Mobility Ecosystem through VEC-OF

[1] www.autosar.org, Document ID
AUTOSAR_EXP_LayeredSoftwareArchitecture
[2] www.autosar.org, Explanation of Adaptive Platform Design AUTOSAR AP Release 17-10
[3] The Global Semiconductor Alliance (GSA) public webniar, Automotive technology | M/NEE 2019-03-31, Dr. Andreas Lock, Robert Bosch GmbH, “The Trends of Future E/E Architectures”
[4] AUTOWARE open source project, https://www.autoware.org/, https://www.autoware.auto/
[5] APOLLO open source project, https://apollo.auto/, https://github.com/ApolloAuto/apollo
[6] KubeEdge open source project, https://kubeedge.io
[7] Zenoh open source project, http://zenoh.io/, https://github.com/eclipse-zenoh
[8] Sasaki, Sato, Chishiro, et all “An Edge-Cloud Computing Model for Autonomous Vehicles” 2019,K. Elissa, “Title of paper if known,” unpublished
[9] Some-ip, http://some-ip.com/
[10] DDS open source project, https://www.omg.org/omg-dds-portal/
[11] Philipp Moritz, Robert Nishihara,Stepanie Wang, “Ray: Adistributed Framework for Emerging AI Applications”.
[12] GENIVI, genniv.org, Beyond the Linux IVI into Connected Vehicle
[13] "Vehicle-To-Vehicle Communication Technology For Light
Vehicles"(PDF).www.google.com. p.e10. Retrieved2019-12-02 (references) tions
[14] The Case for Cellular V2X for Safety and Cooperative Driving
(http://5gaa.org/wp-content/uploads/2017/10/5GAA-whitepaper-23-Nov-2016.pdf)
[15] Daniel Fremont, Xiangyu Yue, Tommaso Dreossi, “Scenic: Language- Based Scene Generation”.
[16] Carla open source Project, carla.org
[17] OpenCV open source project, opencv.org
[18] KUBERNETES CNCF open source project, https://kubernetes.io
[19] TENSORFLOW open source project, tensorflow.org
[20] SPARK open source project, spark.apache.org
[21] HADOOP open source project, hadoop.apache.org

A Language for Autonomous Vehicles Testing Oracles

[1] Baidu Apollo team. 2017. Apollo: Open Source Autonomous Driving. https: //github.com/ApolloAuto/apollo. Accessed: 2020-01-20.
[2] Carla. 2019. Carla Challenge at CVPR 2019. https://carlachallenge.org/.
[3] A. Censi, K. Slutsky, T. Wongpiromsarn, D. Yershov, S. Pendleton, J. Fu, and E. Frazzoli. 2019. Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks. In 2019 International Conference on Robotics and Automation (ICRA).
8536–8542. https://doi.org/10.1109/ICRA.2019.8794364
[4] W. E. Howden. 1978. Theoretical and Empirical Studies of Program Testing.
IEEE Transactions on Software Engineering SE-4, 4 (July 1978), 293–298. https:
//doi.org/10.1109/TSE.1978.231514
[5] ClaudioMenghi,ShivaNejati,KhouloudGaaloul,andLionelC.Briand.2019.Gen- erating Automated and Online Test Oracles for Simulink Models with Continuous and Uncertain Behaviors. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019). Association for Computing Machinery, New York, NY, USA, 27–38. https://doi.org/10.1145/3338906.3338920
[6] Udacity. 2019. CarND-Path-Planning-Project. https://github.com/udacity/CarND- Path- Planning- Project.

SD-FFR: Software Defined Fast Failure Recovery Mechanism in the Automatic Warehouse

[1] Y. Liu, K. F. Tong, X. Qiu, Y. Liu, and X. Ding, “Wireless Mesh Networks in IoT networks,” in 2017 International Workshop on Elec- tromagnetics: Applications and Student Innovation Competition, May. 2017, pp. 183–185.
[2] A. Karaagac, J. Haxhibeqiri, W. Joseph, I. Moerman, and J. Hoebeke, “Wireless industrial communication for connected shuttle systems in warehouses,” in 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS), May. 2017, pp. 1–4.
[3] E. Alizadeh Jarchlo, J. Haxhibeqiri, I. Moerman, and J. Hoebeke, “To Mesh or not to Mesh: Flexible Wireless Indoor Communication Among Mobile Robots in Industrial Environments,” vol. 9724, Jul. 2016, pp. 325–338.
[4] A.Fellan,C.Schellenberger,M.Zimmermann,andH.D.Schotten,“En- abling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0,” in 2018 International Conference on Information and Communication Technology Convergence (ICTC), Oct. 2018, pp. 171– 176.
[5] Q. Chen, X. J. Zhang, W. L. Lim, Y. S. Kwok, and S. Sun, “High Reliability, Low Latency and Cost Effective Network Planning for Indus- trial Wireless Mesh Networks,” IEEE/ACM Transactions on Networking, vol. 27, no. 6, pp. 2354–2362, Dec. 2019.
[6] K. C. Karthika, “Wireless mesh network: A survey,” in 2016 Interna- tional Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Mar. 2016, pp. 1966–1970.
K. Bao, J. D. Matyjas, F. Hu, and S. Kumar, “Intelligent Software- Defined Mesh Networks With Link-Failure Adaptive Traffic Balancing,” IEEE Transactions on Cognitive Communications and Networking, vol. 4, no. 2, pp. 266–276, Jun. 2018.
W. J. Lee, J. W. Shin, H. Y. Lee, and M. Y. Chung, “Testbed imple- mentation for routing WLAN traffic in software defined wireless mesh network,” in 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Jul. 2016, pp. 1052–1055.
P. Patil, A. Hakiri, Y. Barve, and A. Gokhale, “Enabling Software- Defined Networking for Wireless Mesh Networks in smart environ- ments,” in 2016 IEEE 15th International Symposium on Network Com- puting and Applications (NCA), Oct. 2016, pp. 153–157.
M. Labraoui, M. M. Boc, and A. Fladenmuller, “Software Defined Networking-assisted routing in wireless mesh networks,” in 2016 Inter- national Wireless Communications and Mobile Computing Conference (IWCMC), Sep. 2016, pp. 377–382.
A. Detti, C. Pisa, S. Salsano, and N. B. Melazzi, “Wireless Mesh Software Defined Networks (wmSDN),” in 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Com- munications (WiMob), Oct. 2013, pp. 89–95. S.Feng,Y.Wang,X.Zhong,J.Zong,X.Qiu,andS.Guo,“Aring-based single-link failure recovery approach in SDN data plane,” in NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, Jul. 2018, pp. 1–7. G.Saldamli,H.Mishra,N.Ravi,R.R.Kodati,S.A.Kuntamukkala,and L. Tawalbeh, “Improving link failure recovery and congestion control in SDNs,” in 2019 10th International Conference on Information and Communication Systems (ICICS), Jun. 2019, pp. 30–35.
M. Tanha, D. Sajjadi, and J. Pan, “Demystifying Failure Recovery for Software-Defined Wireless Mesh Networks,” in 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), Jun. 2018, pp. 488–493.
C. Huang, W. Chung, and C. Liu, “SCONN: Design and Implement Dual-Band Wireless Networking Assisted Fault Tolerant Data Transmis- sion in Intelligent Buildings,” in 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Aug. 2018, pp. 1–5. X.Zhangetal.,“LocalFastRerouteWithFlowAggregationinSoftware Defined Networks,” IEEE Communications Letters, vol. 21, no. 4, pp. 785–788, Dec. 2017.
B. Oh, S. Vural, N. Wang, and R. Tafazolli, “Priority-Based Flow Control for Dynamic and Reliable Flow Management in SDN,” IEEE Transactions on Network and Service Management, vol. 15, no. 4, pp. 1720–1732, Nov. 2018.
M. Nick et al., “OpenFlow: Enabling innovation in campus networks,” ACM SIGCOMM Computer Communication Review, vol. 38, no. 2, pp. 69–74, Mar. 2008.
D. B. Johnson, “A note on dijkstras shortest path algorithm,” J. ACM, vol. 20, no. 3, p. 385388, Jul. 1973

Thinging-Oriented Modeling of Unmanned Aerial Vehicles

[1] J. Culus, Y. Schellekens, and Y. Smeets, “A drone’s eye view,” PwC, Media centre, Brussels, Belgium, Report, May 2018. [Online]. Accessed august 12, 2019.
[2] B. T. Smith, “Ethics on the fly: toward a drone-specific code of conduct for law enforcement,” M.S. thesis, Dept. Abbrev., Naval Postgraduate School, Monterey, CA, USA, 2016.
[3] S. N. Akhtar, “The use of modern tools for modelling and simulation of UAV with haptic,” M.Sc.Res. thesis, Cranfield Univ., Shrivenham, U.K., 2017.
[4] P. G. Fahlstrom and T. J. Gleason, Introduction to UAV Systems, West Sussex, U.K.: Wiley, 2012.
[5] P. Kardasz, J. Doskocz, M. Hejduk, P. Wiejkut, and H. Zarzycki, “Drones and possibilities of their using,” J. Civil Environ. Eng., vol. 6, no. 3, pp. 1–7, January 2016.
[6] E. Pastor, J. Lopez, and P. Royo, “A hardware/software architecture for UAV payload and mission control,” in Proc. 25th Digit. Avionics Syst. Conf., Portland, Oregon/US: IEEE/AIAA, October 2006, pp. 5B4-1– 5B4-8.
[7] W. Sellars, “Philosophy and the scientific image of man,” in Frontiers of Science and Philosophy, R. Colodny, Ed. Pittsburgh: Univ. of Pittsburgh Press, 1962, pp. 35–78.
[8] M. Franssen, G.-J. Lokhorst, and I. van de Poel, “Philosophy of technology,” in Stanford Encyclopedia of Philosophy, February 20, 2009. [Online]. Available: https://plato.stanford.edu/entries/technology/
[9] Tettra Site, Technical Specification, 2019. Accessed October 5, 2019.
[10] H. Jaakkola and B. Thalheim, “Architecture-driven modelling methodologies,” in Proc. Conf. Inf. Model. Knowl. Bases XXII, A. Heimbürger et al., Eds. pp. 97–166, 2011.
[11] A. Renaul, “A model for assessing UAV system architectures,” Procedia Comput. Sci., vol. 61, pp. 160–167, 2015.
[12] Li B., “Study on modeling of communication channel of UAV,” in Proc. Int. Congr. Inf. Commun. Technol. 2017, in Procedia Comput. Sci., vol. 107, pp. 550–555, 2017.
[13] Ministry of Transport and Ministry for Business, “Drones: Benefits study: High level findings,” M.E. consulting report, Auckland, New Zealand, Jun. 2019.
[14] P. G. Diem, N. V. Hien, and N. P. Khanh, “An object-oriented analysis and design model to implement controllers for quadrotor UAVs by specializing MDA’s features with hybrid automata and real-time UML,” WSEAS Transactions on Systems, vol. 12, no. 10, pp. 483–496, Oct. 2013.
[15] OMG, Specifications of MDA, ver. 1.01, 2003. Accessed October 2, 2019.
[16] T. Wolf et al., “Choice as a principle in network architecture,” ACM Conf. Appl., Techn., Architectures, Protocols Comput. Commun., Helsinki, Finland, Aug. 13–17, pp. 105–106, 2012.
[17] M. Heidegger, “The thing,” in Poetry, Language, Thought, A. Hofstadter, Trans. New York: Harper & Row, pp. 161–184, 1975.
[18] K. Riemer, R. B. Johnston, D. Hovorka, and M. Indulska, “Challenging the philosophical foundations of modeling organizational reality: The case of process modeling,” Int. Conf. Inf. Syst., Milan, Italy, Dec. 15-18, 2013.
[19] S. Al-Fedaghi, “Five generic processes for behaviour description in software engineering,” Int. J. Comput. Sci. Inf. Secur., vol. 17, no. 7, pp. 120–131, July 2019.
[20] S. Al-Fedaghi, “Thing/machine-s (thimacs) applied to structural description in software engineering,” Int. J. Comput. Sci. Inf. Secur., vol. 17, no. 8, pp. 01–11. August 2019.
[21] S. Al-Fedaghi, “Toward maximum grip process modeling in software engineering,” Int. J. Comput. Sci. Inf. Secur., vol. 17, no. 6, pp. 8–18, June 2019.
[22] S. Al-Fedaghi and G. Aldamkhi, “Conceptual modeling of an IP phone communication system: A case study,” 18th Annu. Wireless Telecommun. Symp., New York, NY, USA, April 9–12, 2019.
[23] S. Al-Fedaghi and O. Alsumait, “Toward a conceptual foundation for physical security: Case study of an IT department,” Int. J. Saf. Secur. Eng., vol. 9, no. 2, pp. 137–156, 2019.
[24] S. Al-Fedaghi and M. BehBehani, “Thinging machine applied to information leakage,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 9, pp. 101–110, 2018d.
[25] S. Al-Fedaghi and J. Al-Fadhli, “Modeling an unmanned aerial vehicle as a thinging machine,” 5th Int. Conf. Control, Automat., and Robot., Beijing, China, April 19–22, 2019.
[26] G. N. Fandetti, “Method of drone delivery using aircraft,” 2015, uS Patent App. 14/817,356.
[27] A. Reyna, C. Martín, J.,Chen E. Solerand M.Díaz, On blockchain and its integration with IoT. Challenges and opportunities, Future Generation Computer Systems, Vol. 88, pp. 173-190, November 2018.
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 5, 2020
www.ijacsa.thesai.org
[28] RedStag Fulfillment, “The future of https://redstagfulfillment.com/the-future-of-distribution/ December 14, 2017).
distribution.” (accessed
[29] RedStag Fulfillment Site, “The future of distribution Part II: Product distribution in emerging markets,” Accessed December 21, 2017.
[30] H. Shakhatreh et al., “Unmanned aerial vehicles: a survey on civil applications and key research challenges,” IEEE Access 7, January 2019, Art. no. 8682048.

Energy-aware Allocation of Graph Jobs in Vehicular Cloud Computing-enabled Software-defined IoV

[1] S. Hosseinalipour, A. Nayak, and H. Dai, “Power-aware allocation of
graph jobs in geo-distributed cloud networks,” IEEE Trans. Parallel
Distrib. Syst., vol. 31, no. 4, pp. 749–765, 2019.
[2] M. LiWang, Z. Gao, S. Hosseinalipour, and H. Dai, “Multi-task of-
floading over vehicular clouds under graph-based representation,” arXiv
preprint arXiv:1912.06243, 2019.
[3] W.Quan,Y.Liu,H.Zhang,andS.Yu,“Enhancingcrowdcollaborations
for software defined vehicular networks,” IEEE Commun. Mag., vol. 55,
no. 8, pp. 80–86, 2017.
[4] K. Wang, H. Yin, W. Quan, and G. Min, “Enabling collaborative edge
computing for software defined vehicular networks,” IEEE Netw., vol.
32, no. 5, pp. 12–117, 2018.
[5] K. Z. Ghafoor, L. Kong, D. B. Rawat, and E. Hosseini, and A.S. Sadiq,
“Quality of service aware routing protocol in software-defined internet of vehicles,” IEEE Internet Things J., vol. 6, no. 2, pp. 2817–2828, 2018.
[6] J. Ghaderi, S. Shakkottai, and R. Srikant, “Scheduling storms and streams in the cloud,” ACM Trans. Modeling Performance Eval. of Comput. Syst., vol. 1, no. 4, pp. 1–14, 2016.
[7] D. Huang, P. Wang, and D. Niyato, “A dynamic offloading algorithm for mobile computing,” IEEE Trans. Wireless Commun., vol.11, no. 6, pp. 1991–1995, 2012.
[8] M. Goudarzi, M. Zamani, and A. T. Haghighat, “ A fast hybrid multi- site computation offloading for mobile cloud computing,” Journal of Netw. Computer Appl., vol. 80, pp. 219–231, 2017.
[9] L. Shi, Z. Zhang, and T. Robertazzi, “Energy-aware scheduling of embarrassingly parallel jobs and resource allocation in cloud,” IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 6, pp. 1607–1620, 2017.
[10] F. Sun, F. Hou, N. Cheng, M. Wang, H. Zhou, L. Gui, and X. Shen, “Cooperative task scheduling for computation offloading in vehicular cloud,” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp: 11049–11061, 2018.
[11] M.Jia,J.Cao,andL.Yang,“Heuristicoffloadingofconcurrenttasksfor computation intensive applications in mobile cloud computing,” IEEE Int. Conf Comp. Commun. Workshops (INFOCOM WKSHPS), Toronto, CA, Apr. 2014, pp. 352–357.
[12] M. LiWang, S. Hosseinalipour, Z. Gao, Y. Tang, L. Huang, and H. Dai, “Allocation of computation-intensive graph jobs over vehicular clouds in IoV,” IEEE Internet Things J., vol. 7, no. 1, pp. 311–324, 2019.
[13] J. Chen, and Q. Song, “A decentralized dynamic load power allocation strategy for fuel cell/supercapacitor-based APU of large more electric vehicles,” IEEE Trans. Ind. Electron., vol. 66, no. 2, pp. 865–875.
[14] X. Zhu, Y. Li, D. Jin, and J. Lu, “Contact-aware optimal resource allo- cation for mobile data offloading in opportunistic vehicular networks,” IEEE Trans. Veh. Technol., vol. 66, no. 8, pp. 7384–7399, 2017.
[15] Goldsmith A,“Wireless communications,” Cambridge University Press, 2005.
[16] Z. Ning, X. Wang, J. J. P. C. Rodrigues, and X. Feng, “Joint computation offloading, power allocation, and channel assignment for 5G-enabled traffic management systems,” IEEE Trans. Ind. Informat., vol. 15, no. 5, pp. 3058–3067, 2019.
[17] X. Chen, L. Jiao, W. Li, and X. Fu, “Efficient multi-user computation offloading for mobile-edge cloud computing,” IEEE/ACM Trans. Netw., vol. 24, no. 5, pp. 2795–2808, 2016.
[18] V. Carletti, P. Foggia, A. Saggese, and M. Vento, “Challenging the time complexity of exact subgraph isomorphism for huge and dense graphs with VF3,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 804–818.

Stress Testing Method for Scenario-Based Testing of Automated Driving Systems

[1] Kalra, Nidhi & Paddock, Susan. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle relia- bility?. Transportation Research Part A: Policy and Practice. 94. 182-193. 10.1016/j.tra.2016.09.010.
[2] W.Wachenfeld,H.Winner,DieFreigabedesautonomenFahrens.InLenz B, Winner H, Gerdes JC, Maurer M editors. Autonomes Fahren: tech- nische, rechtliche und gesellschaftliche Aspekte, Vol. 116.s.l. Heidelberg, Germany: Springer. p. 439–464.
[3] FelixBatsch,StratisKanarachos,MadelineCheah,RobertoPonticelliand Mike Blundell, "A taxonomy of validation strategies to ensure the safe op- eration of highly automated vehicles," Journal of Intelligent Transportation Systems, DOI: 10.1080/15472450.2020.1738231
[4] D. Nalic, T. Mihalj, M. Bäumler, M. Lehmann, A. Eichberger and S. Bernsteiner, "Scenario Based Testing of Automated Driving Systems: A Literature Survey," FISITA Web Congress, 2020, pp. xxx-xxx.
[5] T.Mugur,"EnhancingADASTestandValidationwithAutomatedSearch for Critical Situations", presented at Driving Simulation Conference & Exhibition 2015, Berlin, September 16-18, 2015.
[6] D. Nalic, A. Eichberger, G. Hanzl, M. Fellendorf and B. Rogic, "Devel- opment of a Co-Simulation Framework for Systematic Generation of Sce- narios for Testing and Validation of Automated Driving Systems*," 2019
IEEE Intelligent Transportation Systems Conference (ITSC), Auckland,
New Zealand, 2019, pp. 1895-1901, doi: 10.1109/ITSC.2019.8916839.
[7] S. Hallerbach, Y. Xia, U. Eberle, and F. Koester, "Simulation-based identi- fication of critical scenarios for cooperative and automated vehicles", SAE International Journal of Connected and Automated Vehicles, vol. 1, no.
2018-01-1066, pp. 93–106, 2018.
[8] T. Helmer, L. Wang, K. Kompass and R. Kates, "Safety Performance
Assessment of Assisted and Automated Driving by Virtual Experiments: Stochastic Microscopic Traffic Simulation as Knowledge Synthesis," 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, 2015, pp. 2019-2023, doi: 10.1109/ITSC.2015.327.
[9] D.Gruyer,S.Choi,C.BoussardandB.d’Andréa-Novel,"Fromvirtualto reality, how to prototype, test and evaluate new ADAS: Application to au- tomatic car parking," 2014 IEEE Intelligent Vehicles Symposium Proceed- ings, Dearborn, MI, 2014, pp. 261-267, doi: 10.1109/IVS.2014.6856525.
[10] StatisticAustria,Accessedat01.12.2020.[online]Available: https://www.statistik.at/web_en/statistics/index.html
[11] M. Fellendorf and P. Vortisch, "Microscopic traffic flow simulator VIS- SIM," in Fundamentals of Traffic Simulation, vol. 145, ser. International Series in Operations Research and Management Science, J. Barcel,Ed.New York, NY, USA: Springer-Verlag, 2010, pp. 63–93.
[12] J. Barceló, Ed.,Fundamentals of Traffic Simulation, vol. 145. New York,NY, USA: Springer, 2010.
[13] B. Wang, W. Chen, B. Zhang, Y. Zhao, "Regulation cooperative control for heterogeneous uncertain chaotic systems with time delay: A syn- chronization errors estimation framework," Automatica, Vol. 108, 2019, https://doi.org/10.1016/j.automatica.2019.06.038.
[14] C.DengandC.Wen,"DistributedResilientObserver-BasedFault-Tolerant Control for Heterogeneous Multiagent Systems Under Actuator Faults and DoS Attacks," in IEEE Transactions on Control of Network Systems, vol. 7, no. 3, pp. 1308-1318, Sept. 2020, doi: 10.1109/TCNS.2020.2972601.
[15] D. Nalic, A. Pandurevic, A., A. Eichberger and B. Rogic, "Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems,", Preprints 2020, 2020110252.
[16] R. Trapp, "Hinweise zur mikroskopischen Verkehrsflusssimulation- Grundlagen und Anwendung", Forschungsgesellschaft für strassen-und verkehrswesen (2006), Köln: FGSV Verlag GmbH.
[17] S. Seebacher, B. Datler, J. Erhart, M. Harrer, P. Hrassnig, A. Präsent, C. Schwarzl and M. Ullrich, "Infrastructure data fusion for validation and future enhancements of autonomous vehicles perception on Aus- trian motorways," IEEE International Conference on Connected Vehi- cles and Expo (ICCVE), Graz, Austria, 2019, pp. 1-7, doi: 10.1109/IC- CVE45908.2019.8965142.
[18] D.Nalic,A.Pandurevic,A.Eichberger,B.Rogic,"TestingofAutomated Driving Systems in a Dynamic Traffic Environment", submitted to Soft- wareX, [arXiv:cs.SE/2011.05798].
[19] Statistic Austria Accident Types, [online] Available: https://www.statistik.at/web_en/statistics/EnergyEnvironmentInnovationMobility/transport/ road/road_traffic_accidents/index.html
[20] M. Klischat and M. Althoff, "Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms," 2019 IEEE Intelli- gent Vehicles Symposium (IV), Paris, France, 2019, pp. 2352-2358, doi: 10.1109/IVS.2019.8814230.
[21] M. R. Zofka, F. Kuhnt, R. Kohlhaas, C. Rist, T. Schamm and J. M. Zöllner, "Data-driven simulation and parametrization of traffic scenarios for the development of advanced driver assistance systems," 2015 18th International Conference on Information Fusion (Fusion), Washington, DC, 2015, pp. 1422-1428.
[22] T. Menzel, G. Bagschik and M. Maurer, "Scenarios for Develop- ment, Test and Validation of Automated Vehicles," 2018 IEEE Intelli- gent Vehicles Symposium (IV), Changshu, 2018, pp. 1821-1827, doi: 10.1109/IVS.2018.8500406.
[23] European New Car Assessment Program (Euro-NCAP). Frontal Impact Testing Protocol, Version 4.3. Testing protocol, Euro-NCAP, February 2009.
[24] S. K. Gehrig and F. J. Stein, "Collision Avoidance for Vehicle-Following Systems," in IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 2, pp. 233-244, June 2007, doi: 10.1109/TITS.2006.888594.
[25] H. Winner, S. Hakuli and G. Wolf, "Handbuch Fahrerassistenzsysteme: Grundlagen, Komponenten und Systeme fur aktive Sicherheit und Kom- fort,", 2nd ed. (in German). Wiesbaden, Germany: Vieweg+Teubner, 2011.
[26] NationalHighwayandTrafficSafetyAdministration(NHTSA).Reportto Congress on the National Highway Traffic Safety Administration ITSPro-
gram. Program Progress During 1992-1996 and Strategic Plan for 1997-
2002. Technical report, NHTSA, 1997.
[27] D. Wallner, A. Eichberger, and W. Hirschberg. "A Novel Control Al-
gorithm for Integration of Active and Passive Vehicle Safety System- sin Frontal Collisions," In Proceedings of the 2nd International Multi- Conference on Engineering and Technological Innovation (IMETI), 10-13 July 2009. Orlando, USA.
[28] Akçelik, Rahmi and D. C. Biggs. "Acceleration profile models for vehicles in road traffic," Transportation Science, vol. 21, no. 1, pp: 36-54., 1987.
[29] A. K. Maurya and P. S. Bokare, "Study of deceleration behaviour of different vehicle types," International Journal for Traffic Transportation Engineering, vol. 2, no. 3, pp. 253–270, 2012.
[30] Bokare,P.S.andA.K.Maurya."Acceleration-decelerationbehaviourof various vehicle types," Transportation Research Procedia, vol. 5, pp: 4733- 474, 2017.
[31] Akçelik, Rahmi and Mark Besley. "Acceleration and deceleration mod- els," in 23nd Proceedings Conference of Australian Institutes of Transport Research (CAITR 2001), Vol. 10, pp: 1-9, Melbourne Australia, 2001.
[32] Kudarauskas, Nerijus. "Analysis of emergency braking of a vehi- cle," Transport, vol. 22, no. 3 pp: 154-159, 2007.
[33] A.NixandJ.Kemp,"Fullspeedrangeadaptivecruisecontrolsystem,"US Patent 20090254260A1, October, 8th, 2009.
[34] Xu, Jin, et al. "Acceleration and deceleration calibration of operating speed prediction models for two-lane mountain highways," Journal of Transportation Engineering, Part A, Systems, vol. 143, no. 7, July 2017, Art. no. 04017024.
[35] D.S.Panagiota,M.Quddus,A.AnvuurandS.Reed,"Analyzingandmod- eling drivers’ deceleration behavior from normal driving," Transportation research record, Vol. 2663, no. 1, pp: 134-141, 2017.
[36] J.Wang,K.K.Dixon,H.LiandJ.Ogle,"Normaldecelerationbehaviorof passenger vehicles at stop sign–controlled intersections evaluated with in- vehicle Global Positioning System data," Transportation research record, Vol. 1937, no. 1, pp:120-127, 2005.
[37] P.Tientrakool,Y.HoandN.F.Maxemchuk,"HighwayCapacityBenefits from Using Vehicle-to-Vehicle Communication and Sensors for Collision Avoidance," 2011 IEEE Vehicular Technology Conference (VTC Fall), San Francisco, CA, 2011, pp. 1-5, doi: 10.1109/VETECF.2011.6093130.
[38] A. Mehmood and S. M. Easa, "Modeling reaction time in car-following behaviour based on human factors," International Journal of Applied Science, Engineering and Technology, vol. 5, no. 14, pp: 93-101, 2009.
[39] F. You, R. Zhang,G. Lie, H. Wang, H. Wen and J. Xu, "Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system," Expert Systems with Applications, vol. 42, no. 14, pp: 5932-5946, 2015.
[40] S. Samiee, S. Azadi, R. Kazemi and A. Eichberger "Towards a decision- making algorithm for automatic lane change manoeuvre considering traffic dynamics," PROMET-Traffic and Transportation, vol. 28, no. 2, pp: 91- 103, 2016.
[41] B. Rogic, D. Nalic, A. Eichberger and S. Bernsteiner, "A Novel Approach to Integrate Human-in-the-Loop Testing in the Development Chain of Automated Driving: The Example of Automated Lane Change,", 21th IFAC World Congress, 2020.
[42] P. Junietz, J. Schneider, H. Winner, "Metrik zur Bewertung der Kritikalität von Verkehrssituationen und -szenarien," (in German) in 11th Workshop Fahrerassistenzsysteme, 2017.

Security of Connected and Automated Vehicles

Ali Alheeti KM, Gruebler A, McDonald-Maier K. 2016. Intel- ligent intrusion detection of grey hole and rushing attacks in self-driving vehicular networks. Computers 5(16).
Alnasser A, Sun H, Jiang J. 2019. Cyber security challenges and solutions for V2X communications: A survey. Com- puter Networks 151:52–67.
Aloqaily M, Otoum S, Al Ridhawi I, Jararweh Y. 2019. An intrusion detection system for connected vehicles in smart cities. Ad Hoc Networks 90:101842.
Alwakeel AM, Alnaim AK, Fernandez EB. 2018. A survey of network function virtualization security. Conf Proceedings, IEEE Southeastcon, Apr 19–22, St. Petersburg FL.
Anouar B, Mohammed B, Abderrahim G, Mohammed B. 2017. Vehicular navigation spoofing detection based on V2I calibration. IEEE Colloquium on Information Science and Technology, Oct 24–26, Tangier.
Basu S, Bardhan A, Gupta K, Saha P, Pa M, Bose M, Basu K, Chaudhury S, Sarkar P. 2018. Cloud computing security challenges & solutions: A survey. IEEE 8th Annual Com- puting and Communication Workshop and Conf, Jan 8–10, Las Vegas.
Cachin C, Vukolic ́ M. 2017. Blockchain consensus protocols in the wild. arXiv:1707.01873.
Chattopadhyay A, Lam K-Y. 2018. Autonomous vehicle: Security by design. arXiv:1810.00545.
Chien YR. 2015. Design of GPS anti-jamming systems using adaptive notch filters. IEEE Systems Journal 9(2):451–460. Chowdhury M, Rahman M, Rayamajhi A, Khan SM, Islam M, Khan Z, Martin J. 2018. Lessons learned from the real- world deployment of a connected vehicle testbed. Trans-
portation Research Record 2672(22):10–23.
Corbett C, Brunner M, Schmidt K, Schneider R, Dannebaum U. 2018. Leveraging hardware security to secure connected vehicles. WCX World Congress Experience, Apr 10–12,
Detroit.
Deka L, Khan SM, Chowdhury M, Ayres N. 2018. Transpor-
tation cyber-physical system and its importance for future mobility. In: Transportation Cyber-Physical Systems, eds Deka L, Chowdhury M. Cambridge MA: Elsevier.
Dennison C. 2019. How fiber protection is enabling next- generation automotive systems. PPC blog.
Darabseh A, Al-Ayyoub M, Jararweh Y, Benkhelifa E, Vouk M, Rindos A. 2015. SDSecurity: A software defined security experimental framework. 2015 IEEE International Conf on Communication, London.
Dey K, Fries R, Ahmed S. 2018. Future of transportation cyber-physical systems – Smart cities/regions. In: Transpor- tation Cyber-Physical Systems, eds Deka L, Chowdhury M. Cambridge MA: Elsevier.
Dinculeana ̆ D, Cheng X. 2019. Vulnerabilities and limitations of MQTT protocol used between IoT devices. Applied Sciences 9(5):848.
Dorri A, Steger M, Kanhere SS, Jurdak R. 2017. BlockChain: A distributed solution to automotive security and privacy. IEEE Communications 55(12):119–125.
Eykholt K, Evtimov I, Fernandes E, Li B, Rahmati A, Xiao C, Prakash A, Kohno T, Song D. 2018. Robust physical-world attacks on deep learning visual classification. 2018 IEEE/ CVF Conf on Computer Vision and Pattern Recognition, Jun 18–22, Salt Lake City.
Grewe D, Wagner M, Arumaithurai M, Psaras I, Kutscher D. 2017. Information-centric mobile edge computing for connected vehicle environments: Challenges and research directions. Proceedings, Workshop on Mobile Edge Com- munications, Aug 21, Los Angeles.
Han B, Gopalakrishnan V, Ji L, Lee S. 2015. Network func- tion virtualization: Challenges and opportunities for inno- vations. IEEE Communications 53(2):90–97.
Huo Y, Tu W, Sheng Z, Leung VCM. 2015. A survey of in- vehicle communications: Requirements, solutions and opportunities in IoT. IEEE 2nd World Forum on Internet of Things, Dec 14–16, Milan.
Intel. 2019. Intel Authenticate Technology: Hardware- enhanced security. Online at https://www.intel.com/content/www/us/en/security/authenticate/authenticate-is-hardware-enhanced-security.html.
Islam M, Chowdhury M, Li H, Hu H. 2018. Cybersecurity attacks in vehicle-to-infrastructure applications and their prevention. Transportation Research Record 2672(19):66–78.
Islam M, Chowdhury M, Li H, Hu H. 2019. Vision-based navigation of autonomous vehicle in roadway environments with unexpected hazards. arXiv:1810.03967.
ISO. 2018. ISO 26262-1:2018 - Road Vehicles—Functional Safety. Geneva: International Organization for Standardization.
Jaballah WB, Conti M, Lal C. 2019. A survey on software- defined VANETs: Benefits, challenges, and future directions. arXiv:1904.04577.
Jadhav S, Kshirsagar D. 2018. A survey on security in automotive networks. 4th International Conf on Computing Communication Control and Automation, Aug 16–18, Puna, India.
Jover RP, Marojevic V. 2019. Security and protocol exploit analysis of the 5G specifications. IEEE Access 7:24956–24963.
Jwo DJ, Chung FC, Yu KL. 2013. GPS/INS integration accuracy enhancement using the interacting multiple model nonlinear filters. Applied Research and Technology 11(4):496–509.
Kalogeiton E, Braun T. 2018. Infrastructure-assisted communication for NDN-VANETs. 19th IEEE International Sym- posium on a World of Wireless, Mobile and Multimedia Networks, Jun 12–15, Chania.
Kang J, Yu R, Huang X, Jonsson M, Bogucka H, Gjessing S, Zhang Y. 2016. Location privacy attacks and defenses in cloud-enabled internet of vehicles. IEEE Wireless Communications 23:52–59.
Khan Z, Chowdhury M, Islam M, Huang C-Y, Rahman M.
2019. In-vehicle false information attack detection and mitigation framework using machine learning and software defined networking. arXiv:1906.10203.
Koscher K, Czeskis A, Roesner F, Patel S, Kohno T. 2017. Experimental security analysis of a modern automobile. High Energy Physics 2017(11):1–16.
Levi M, Allouche Y, Kontorovich A. 2018. Advanced analytics for connected car cybersecurity. IEEE 87th Vehicular Technology Conf, Jun 3–6, Porto.
Li X, Liu J, Li X, Sun W. 2013. RGTE: A reputation-based global trust establishment in VANETs. Proceedings, 5th International Conf on Intelligent Networking and Collab- orative Systems, Sep 9–11, Xi�an.
Li S, Xu LD, Zhao S. 2018. 5G Internet of Things: A survey. Industrial Information Integration 10:1–9.
Liu J, Zhang S, Sun W, Shi Y. 2017. In-vehicle network attacks and countermeasures: Challenges and future direc- tions. IEEE Network 31(5):50–58.
Mayilsamy K, Ramachandran N, Raj VS. 2018. An integrated approach for data security in vehicle diagnostics over inter- net protocol and software update over the air. Computers & Electrical Engineering 71:578–593.
Modieginyane KM, Letswamotse BB, Malekian R, Abu- Mahfouz AM. 2018. Software defined wireless sensor networks application opportunities for efficient network management: A survey. Computers and Electrical Engi- neering 66:274–287.
NGMN [Next Generation Mobile Networks Alliance]. 2015. 5G White Paper. Frankfurt am Main.
Nguyen HN, Tavakoli S, Shaikh SA, Maynard O. 2019. Developing a QRNG ECU for automotive security: Expe- rience of testing in the real-world. 2019 IEEE International Conf on Software Testing, Verification and Validation Workshops, Apr 22–23, Xi�an, China
Nie S, Liu L, Du Y. 2017. Free-fall: Hacking Tesla from wire- less to CAN Bus. Black Hat USA, Jul 27.
Nobre JC, de Souza AM, Rosário D, Both C, Villas LA, Cerqueira E, Braun T, Gerla M. 2019. Vehicular software- defined networking and fog computing: Integration and design principles. Ad Hoc Networks 82:172–181.
Papernot N, McDaniel P, Goodfellow I, Jha S, Celik ZB, Swami A. 2017. Practical black-box attacks against machine learn- ing. Proceedings, 2017 ACM Asia Conf on Computer and Communications Security, Apr 2–6, Abu Dhabi.
Parkinson S, Ward P, Wilson K, Miller J. 2017. Cyber threats facing autonomous and connected vehicles: Future chal-
The BRIDGE
lenges. IEEE Transactions on Intelligent Transportation
Systems 18(11):2898–2915.
Petit J, Stottelaar B, Feiri M, Kargl F. 2015. Remote attacks
on automated vehicles sensors: Experiments on camera and
LiDAR. Black Hat Europe, Nov 10–13, Amsterdam.
Pike L, Sharp J, Tullsen M, Hickey PC, Bielman J. 2017. Secure automotive software: The next steps. IEEE Software
34(3):49–55.
Psiaki ML, O’Hanlon BW, Bhatti JA, Shepard DP, Humphreys
TE. 2013. GPS spoofing detection via dual-receiver corre- lation of military signals. IEEE Transactions on Aerospace and Electronic Systems 49(4):2250–2267.
Ravi K, Kulkarni SA. 2013. A secure message authentication scheme for VANET using ECDSA. 4th International Conf on Computing, Communications and Networking Tech- nologies, Jul 4–6, Tiruchengode, India.
SAE. 2016. Cybersecurity Guidebook for Cyber-Physical Vehicle Systems. SAE International.
Schwarting W, Alonso-Mora J, Rus D. 2018. Planning and decision-making for autonomous vehicles. Annual Review of Control, Robotics, and Autonomous Systems 1(1):187–210.
Shi W, Dustdar S. 2016. The promise of edge computing. Computer 49(5):78–81.
Singh M, Kim S. 2017. Intelligent vehicle-trust point: Reward based intelligent vehicle communication using Blockchain. arXiv:1707.07442.
Smedley P. 2018. Autonomous vehicles in the quantum age. Connected World, Apr 3.
Sugumar R, Rengarajan A, Jayakumar C. 2018. Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks 24(2):373–382.
Tuchinda C, Srivannaboon S, Lim HW. 2006. Photo- protection by window glass, automobile glass, and sun- glasses. Journal of the American Academy of Dermatology 54(5):845–854.
Twitchell RW. 2013. Virtual dispersive networking systems and methods. US Patent 9,071,607.
Wasicek A, Pesé MD, Weimerskirch A, Burakova Y, Singh K. 2017. Context-aware intrusion detection in automotive control systems. 5th ESCAR USA Conf, Jun 21–22.
Whyte W, Weimerskirch A, Kumar V, Hehn T. 2018. A security credential management system for V2V commu- nications. IEEE Transactions on Intelligent Transportation Systems 19(12):3850–3871.
Wyglinski AM, Huang X, Padir T, Lai L, Eisenbarth TR, Venkatasubramanian K. 2013. Security of autonomous systems employing embedded computing and sensors. IEEE Micro 33(1):80–86.

Application Layer Modeling in Vehicle Networks: Cooperative Maneuver Use Case

1. Marco Chiani, Andrea Giorgetti, and Enrico Paolini, “Sensor radar for object tracking,” Proceedings of IEEE, vol. 106, no. 6, pp. 1022-1041, June. 2018
2. Jesus Urena, et al, “Acoustic local positioning with encoded emissions beacons,” Proceedings of IEEE, vol. 106, no. 6, pp. 1042-1062, June. 2018
3. Michael Buehrer, Henk Wymeersch, and Reza Monir Vaghefi, “Collaborative sensor network localization: Algorithms and practical issues,” Proceedings of IEEE, vol. 106, no. 6, pp. 1089-1114, June. 2018
4. Musa Furkan, Ahmet Dundar Sezer, and Sinan Gezici, “Localization via visible light systems,” Proceedings of IEEE, vol. 106, no. 6, pp. 1042-1062, June. 2018.
5. K. Ranta-Aho, "Performance of 3GPP Rel-9 LTE positioning methods," in Proc. 2nd Invitational Workshop Opportunistic RF Localization Next Generation Wireless Devices, Jun. 2010, pp. 1-5.
6. Y. Shang, W. Ruml, Y. Zhang, and M. Fromherz, "Localization from mere connectivity," in Proc. Mobile Ad Hoc Netw. Comput. (MobilHoc), 2003, pp. 201-212.
7. “TheForeignMan”, “DIY Telematics Box”, https://www.instructables.com/id/DIY-Telematics-Box/, 28, May 2018.
8. Image: https://www.plugntrackgps.com/pages/quick-start, Accessed July 2018.
9. Robert Protzmann, Bjorn Schunemann, and Llja Radusch, "The Influences of Communications Models on the Simulated Effectiveness of V2X Applications", IEEE Communications Magazine, pp.149-155, November 2011.
10. Rimon Barr, "JiST - Java in Simulation Time User Guide," http://jist.ece.cornell.edu/docs/040319-jist-user.pdf, pp.1-34, March 19, 2004.
11. Rimon Barr, "SWANS - Scalable Wireless Ad Hoc Network Simulator User Guide," http://jist.ece.cornell.edu/docs/040319-swans-user.pdf, pp.1-15, March 19, 2004.
12. Konstantinos Katsaros et al, "Application of Vehicular Communications for Improving the Efficiency of Traffic in Urban Areas," ​Special issue on the selected papers of IWCMC 2011​, pp.1657-1667, December, 2011.
13. Florian Hausler, Emanuele Crisostomi, Arich Schlote, Llja Radusch, and Robert
    Shorten, "Stochastic park-and-charge balancing for fully electric and plug-in hybrid vehicles,". I​EEE Transactions on Intelligent Transportation Systems​ Volume: 15, Issue: 2, April 2014.
14. Charalambos Zinoiou, Konstantinos Katsaros, Ralf Kernchen, Mehrdad Dianati, "Performance Evaluation of an Adaptive Route Change Algorithm Using an Integrated Cooperative ITS Simulation Platform," Conference Proceedings of the Intl Wireless Computing and Mobile Computing Conf (IWCMC). December 2012​.
15. Jerome Herri et al, "Modeling and Simulating ITS Applications with iTetris," Proceedings of the 6th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks​. pp. 33-40, 31 October 2011.
16. Antonio Eduardo Fernandez et al, "Deliverable D2.1 5GCAR Scenarios, Use Cases, Requirements and KPIs," https://5gcar.eu/wp-content/uploads/2017/05/5GCAR_D2.1_v1.0.pdf, pp.1-87, 31 August, 2017.
17. 3GPP TR 22.885, “Study on LTE support for Vehicle-to-Everything (V2X) services”, 2015.
18. 3GPP TR 22.186, “Service requirements for enhanced V2X scenarios”, 2017.
19. ITU-R M.2083-0, “IMT Vision - “Framework and overall objectives of the future development of IMT for 2020 and beyond””, 2015.
20. ITU-R M.1890, “Intelligent Transport Systems - Guidelines and objectives”, 2011.
21. Karri Ranta-aho, “Performance of 3GPP Rel-9 LTE positioning methods,” Proceedings of the 2nd Opportunistic RF Localization for Next Generation Wireless Devices Conference. 13 June, 2010.
22. ETSI TR 102 638, “Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Definitions”, V1.1.1, 2009.
23. ETSI TR 103 298, “Intelligent Transport Systems (ITS); Platooning: pre-standardization study”, 2017.
    Application Modeling in Vehicle Networks Steven Platt
24. ETSI TR 103 299, “Intelligent Transport Systems (ITS); Cooperative Adaptive Cruise Control (C-ACC); Pre-standardization study”, 2017.
25. Bjoern Schuenemann, Llja Radusch, and Kay Massow, “Realistic Simulation of Vehicular Communication and Vehicle-2-X Applications”, Proceedings of the 1st international conference on Simulation tools and technique for communications, networks and systems & workshops, Article No 62, 2008.
26. Cyril Nguyen Van Phu, Nadir Farhi, Habib Haj-Salem, Jean-Patrick Lebacque, “A vehicle-to-infrastructure communication based algorithm for urban traffic control”, Proceedings of the ​5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)​, 2017.
27. Konstantinos Katsaros, Mehrdad Dianati, Karsten Roscher, "A Position-based Routing Module for Simulation of VANETs in NS-3," Proceedings of the 5th international ICST conference on Simulation tools and technique. pp. 343-352, March 2012.
28. Choudhury A., Maszczyk T., Dauwels J., Belagal Math C., Li H., "An Integrated simulation environment for testing V2X protocols and applications," Procedia Computer Science, Vol 80, pp. 2042-2052. 2016.
29. Bjoern Schuenemann, Llja Radusch, "V2X-Based Traffic Congestion Recognition and Avoidance," 10th International Symposium on Pervasive Systems, Algorithms, and Networks, Kaohsiung, pp. 637-641. 2009.

SD-VEC: Software-Defined Vehicular Edge Computing with Ultra-Low Latency

[1] “Connected Vehicle Pilot Deployment Program,” the US Department of Transportation (USDOT). [Online]. Available: https://www.its.dot.gov/pilots/
[2] M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “To- ward 6G networks: Use cases and technologies,” IEEE Communications Magazine, vol. 58, no. 3, pp. 55–61, 2020.
[3] 3GPP TSG RAN, “V2X services based on NR; user equipment (UE) radio transmission and reception,” TR38.886 V0.5.0, Release 16, Feb. 2020.
[4] IEEE Std 802.11p-2010, “Part 11: Wireless LAN medium access con- trol (MAC) and physical layer (PHY) specifications amendment 6: Wireless access in vehicular environments,” IEEE Standard for Infor- mation Technology– Local and Metropolitan Area Networks– Specific Requirements–, pp. 1–51, June 2010.
[5] A. Ndikumana, N. H. Tran, T. M. Ho, Z. Han, W. Saad, D. Niyato, and C. S. Hong, “Joint communication, computation, caching, and control in big data multi-access edge computing,” IEEE Transactions on Mobile Computing, vol. 19, no. 6, pp. 1359–1374, 2019
[6] W. Zhuang, Q. Ye, F. Lyu, N. Cheng, and J. Ren, “SDN/NFV- empowered future IoV with enhanced communication, computing, and caching,” Proceedings of the IEEE, vol. 108, no. 2, pp. 274–291, 2019.
[7] C.-Y. Lin, K.-C. Chen, D. Wickramasuriya, S.-Y. Lien, and R. D. Gitlin, “Anticipatory mobility management by big data analytics for ultra- low latency mobile networking,” in IEEE International Conference on Communications (ICC), 2018, pp. 1–7.
[8] T. G. Tadewos, L. Shamgah, and A. Karimoddini, “Automatic safe be- haviour tree synthesis for autonomous agents,” in IEEE 58th Conference on Decision and Control (CDC), 2019, pp. 2776–2781.
[9] C.-H. Zeng, K.-C. Chen, and D.-Z. Liu, “Two-stage ICI suppression in the downlink of asynchronous URLLC,” IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2785–2799, 2020.
[10] M. B. Shahab, R. Abbas, M. Shirvanimoghaddam, and S. J. Johnson, “Grant-free non-orthogonal multiple access for IoT: A survey,” IEEE Communications Surveys & Tutorials, 2020.
[11] C.-H. Lin, Y.-T. Lee, W.-H. Chung, S.-C. Lin, and T.-S. Lee, “Unsu- pervised resnet-inspired beamforming design using deep unfolding tech- nique,” in IEEE Global Telecommunications Conference (GLOBECOM), Dec. 2020.
[12] S.-C. Lin and K.-C. Chen, “Cognitive and opportunistic relay for QoS guarantees in machine-to-machine communications,” IEEE Transactions on Mobile Computing, vol. 15, no. 3, pp. 599–609, 2015.
[13] M. F. Pervej and S.-C. Lin, “Dynamic power allocation and virtual cell formation for throughput-optimal vehicular edge networks in highway transportation,” in IEEE International Conference on Communications (ICC) Workshop, June 2020.
[14] ——, “Eco-vehicular edge networks for connected transportation: A distributed multi-agent reinforcement learning approach,” in IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Oct. 2020.
[15] A. White, A. Karimoddini, and M. Karimadini, “Resilient fault diagnosis under imperfect observations - a need for industry 4.0 era,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 5, pp. 1279–1288, 2020.

Painting the Landscape of Automotive Software in GitHub

[1] National Aeronautics and Space Administration. 2004. NASA Software Safety Standard (NASA-STD-8719.13B). Technical Report.
[2] Harald Altinger, Franz Wotawa, and Markus Schurius. 2014. Testing methods used in the automotive industry: Results from a survey. In Proceedings of the 2014 Workshop on Joining AcadeMiA and Industry Contributions to Test Automation and Model-Based Testing. 1–6.
[3] Jorge Aranda and Gina Venolia. 2009. The secret life of bugs: Going past the errors and omissions in software repositories. In 2009 IEEE 31st International Conference on Software Engineering. IEEE, 298–308.
[4] Reinhard Bergmann, John Ludbrook, and Will PJM Spooren. 2000. Different out- comes of the Wilcoxon—Mann—Whitney test from different statistics packages.
The American Statistician 54, 1 (2000), 72–77.
[5] ManfredBroy,IngolfHKruger,AlexanderPretschner,andChristianSalzmann.
2007. Engineering automotive software. Proc. IEEE 95, 2 (2007), 356–373.
[6] Yanja Dajsuren and Mark van den Brand. 2019. Automotive Systems and Software
Engineering. Springer.
[7] Ali Dorri, Marco Steger, Salil S Kanhere, and Raja Jurdak. 2017. Blockchain: A
distributed solution to automotive security and privacy. IEEE Communications
Magazine 55, 12 (2017), 119–125.
[8] Christof Ebert and John Favaro. 2017. Automotive software. IEEE Software 34, 03
(2017), 33–39.
[9] Fabio Falcini, Giuseppe Lami, and Alessandra Mitidieri Costanza. 2017. Deep
learning in automotive software. IEEE Software 34, 3 (2017), 56–63.
[10] DanielleGonzalez,ThomasZimmermann,andNachiappanNagappan.2020.The State of the ML-universe: 10 Years of Artificial Intelligence & Machine Learn- ing Software Development on GitHub. In Proceedings of the 17th International
Conference on Mining Software Repositories. 431–442.
[11] Dip Goswami, Martin Lukasiewycz, Reinhard Schneider, and Samarjit
Chakraborty. 2012. Time-triggered implementations of mixed-criticality au- tomotive software. In 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1227–1232.
[12] Georgios Gousios. 2013. The GHTorrent dataset and tool suite. In Proceedings of the 10th Working Conference on Mining Software Repositories (MSR ’13). IEEE Press, Piscataway, NJ, USA, 233–236. http://dl.acm.org/citation.cfm?id=2487085. 2487132
[13] Georgios Gousios and Diomidis Spinellis. 2017. Mining software engineering data from GitHub. In 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C). IEEE, 501–502.
[14] AlirezaHaghighatkhah,AhmadBanijamali,Olli-PekkaPakanen,MarkkuOivo, and Pasi Kuvaja. 2017. Automotive software engineering: A systematic mapping study. Journal of Systems and Software 128 (2017), 25–55.
[15] Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, and Xin Xia. 2021. An Empirical Study of the Landscape of Open Source Projects in Baidu, Alibaba, and Tencent. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). IEEE, 298–307.
[16] Geir Kjetil Hanssen, Tor Stålhane, and Thor Myklebust. 2018. SafeScrum®-Agile Development of Safety-Critical Software. Springer.
[17] Abram Hindle, Neil A Ernst, Michael W Godfrey, and John Mylopoulos. 2011. Automated topic naming to support cross-project analysis of software mainte- nance activities. In Proceedings of the 8th Working Conference on Mining Software Repositories. 163–172.
[18] ISO. 2018. ISO 26262: 2018 - Road vehicles – Functional safety. Standard. Interna- tional Organization for Standardization.
[19] Eirini Kalliamvakou, Georgios Gousios, Kelly Blincoe, Leif Singer, Daniel M German, and Daniela Damian. 2016. An in-depth study of the promises and perils of mining GitHub. Empirical Software Engineering 21, 5 (2016), 2035–2071.
[20] DanielKästner,ChristophCullmann,GernotGebhard,SebastianHahn,Thomas Karos, Laurent Mauborgne, Stephan Wilhelm, and Christian Ferdinand. 2020. Safety-Critical Software Development in C++. In International Conference on Computer Safety, Reliability, and Security. Springer, 98–110.
[21] Brian Katumba and Eric Knauss. 2014. Agile development in automotive soft- ware development: Challenges and opportunities. In International Conference on Product-Focused Software Process Improvement. Springer, 33–47.
[22] John C Knight. 2002. Safety critical systems: challenges and directions. In Pro- ceedings of the 24th international conference on software engineering. 547–550.
[23] SangeethKochanthara,YanjaDajsuren,LoekCleophas,andMarkvandenBrand. 2022. Painting the Landscape of Automotive Software in GitHub. https://doi.org/ 10.5281/zenodo.5885013. [Online; accessed on 20-Jan-2022].
[24] SangeethKochanthara,GeoffreyNelissen,DavidPereira,andRahulPurandare. 2016. REVERT: A Monitor Generation Tool for Real-Time Systems. In IEEE Real-Time Systems Symposium.
[25] SangeethKochantharaandRahulPurandare.2016.REVERT:runtimeverification for real-time systems. (2016).
[26] SangeethKochanthara,NielsRood,LoekCleophas,YanjaDajsuren,andMark van den Brand. 2020. Semi-automatic architectural suggestions for the functional safety of cooperative driving systems. In 2020 IEEE International Conference on Software Architecture Companion (ICSA-C). IEEE, 55–58.
[27] SangeethKochanthara,NielsRood,ArashKhabbazSaberi,LoekCleophas,Yanja Dajsuren, and Mark van den Brand. 2021. A functional safety assessment method for cooperative automotive architecture. Journal of Systems and Software 179 (2021), 110991.
[28] SangeethKochanthara,NielsRood,ArashKhabbazSaberi,LoekCleophas,Yanja Dajsuren, and Mark van den Brand. 2021. Summary: A functional safety as- sessment method for cooperative automotive architecture. In CEUR Workshop Proceedings, Vol. 2978. CEUR-WS. org.
[29] Christian Josef Kreiner and Richard Messnarz. 2017. Integrated Assessment of AutomotiveSPICE 3.0, Functional Safety ISO 26262, Cybersecurity SAE J3061. In IIR Konferenz: ISO 26262.
MSR 2022, May 23–24, 2022, Pittsburgh, PA, USA
Kochanthara et al.
[30] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classifi- cation with deep convolutional neural networks. Advances in neural information processing systems 25 (2012), 1097–1105.
[31] Stefan Kugele, David Hettler, and Jan Peter. 2018. Data-centric communication and containerization for future automotive software architectures. In 2018 IEEE International Conference on Software Architecture (ICSA). IEEE, 65–6509.
[32] Stefan Kugele, Philipp Obergfell, Manfred Broy, Oliver Creighton, Matthias Traub, and Wolfgang Hopfensitz. 2017. On service-orientation for automotive software. In 2017 IEEE International Conference on Software Architecture (ICSA). IEEE, 193– 202.
[33] Tarald O Kvålseth. 1989. Note on Cohen’s kappa. Psychological reports 65, 1 (1989), 223–226.
[34] John A McDermid. 2013. Software engineer’s reference book. Elsevier.
[35] Nuthan Munaiah, Steven Kroh, Craig Cabrey, and Meiyappan Nagappan. 2017. Curating github for engineered software projects. Empirical Software Engineering
22, 6 (2017), 3219–3253.
[36] Emerson Murphy-Hill, Thomas Zimmermann, and Nachiappan Nagappan. 2014.
Cowboys, ankle sprains, and keepers of quality: How is video game development different from software development?. In Proceedings of the 36th International Conference on Software Engineering. 1–11.
[37] Philipp Obergfell, Stefan Kugele, and Eric Sax. 2019. Model-based resource analysis and synthesis of service-oriented automotive software architectures. In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 128–138.
[38] Dennis Kengo Oka, Toshiyuki Fujikura, and Ryo Kurachi. 2018. Shift left: Fuzzing earlier in the automotive software development lifecycle using hil systems. In Proc. 16th ESCAR Europe. 1–13.
[39] Annibale Panichella, Bogdan Dit, Rocco Oliveto, Massimilano Di Penta, Denys Poshynanyk, and Andrea De Lucia. 2013. How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms. In 2013 35th International Conference on Software Engineering (ICSE). IEEE, 522–531.
[40] Dhasarathy Parthasarathy, Karl Bäckstrom, Jens Henriksson, and Sólrún Einars- dóttir. 2020. Controlled time series generation for automotive software-in-the- loop testing using GANs. In 2020 IEEE International Conference On Artificial Intelligence Testing (AITest). IEEE, 39–46.
[41] Gregorio Robles and Jesus M Gonzalez-Barahona. 2005. Developer identification methods for integrated data from various sources. ACM SIGSOFT Software Engineering Notes 30, 4 (2005), 1–5.
[42] Rick Salay and Krzysztof Czarnecki. 2018. Using machine learning safely in auto- motive software: An assessment and adaption of software process requirements in ISO 26262. arXiv preprint arXiv:1808.01614 (2018).
[43] Johannes Schlatow, Mischa Möstl, Rolf Ernst, Marcus Nolte, Inga Jatzkowski, and Markus Maurer. 2017. Towards model-based integration of component-based automotive software systems. In IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 8425–8432.
[44] Miroslaw Staron. 2017. Automotive software architectures. Automot. Softw. Archit (2017), 33–39.
[45] Mairieli Wessel, Bruno Mendes De Souza, Igor Steinmacher, Igor S Wiese, Ivanil- ton Polato, Ana Paula Chaves, and Marco A Gerosa. 2018. The power of bots: Characterizing and understanding bots in oss projects. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 1–19.
[46] Markus Zoppelt and Ramin Tavakoli Kolagari. 2018. SAM: a security abstraction model for automotive software systems. In Security and Safety Interplay of Intelligent Software Systems. Springer, 59–74.

Search result on Network Defined Vehicle

Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles

[1] J.B.Kenney,“Dedicatedshort-rangecommunications(dsrc)standards in the united states,” Proceedings of the IEEE, vol. 99, no. 7, pp. 1162– 1182, July 2011.
[2] J. Rios-Torres and A. A. Malikopoulos, “A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 5, pp. 1066–1077, May 2017.
[3] J. Lee and B. Park, “Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 1, pp. 81–90, March 2012.
[4] T. Ort, L. Paull, and D. Rus, “Autonomous vehicle navigation in rural environments without detailed prior maps,” in ICRA 2018. IEEE, 2018, pp. 2040–2047.
[5] J. Ploeg, D. P. Shukla, N. van de Wouw, and H. Nijmeijer, “Controller synthesis for string stability of vehicle platoons,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, pp. 854–865, April 2014.
[6] K.Y.Liang,J.Ma ̊rtensson,andK.H.Johansson,“Heavy-dutyvehicle platoon formation for fuel efficiency,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 4, pp. 1051–1061, April 2016.
[7] S. Darbha, S. Konduri, and P. R. Pagilla, “Benefits of v2v commu- nication for autonomous and connected vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 5, pp. 1954–1963, 2019.
[8] M. Bansal, A. Krizhevsky, and A. S. Ogale, “Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst,” CoRR, vol. abs/1812.03079, 2018.
[9] A. Amini and I. Gilitschenski, “Learning robust control policies for end-to-end autonomous driving from data-driven simulation,” IEEE Robot. Autom. Lett., vol. 5, pp. 1–1, 01 2020.
[10] M. Henaff, Y. LeCun, and A. Canziani, “Model-predictive policy learning with uncertainty regularization for driving in dense traffic,” in 7th ICLR, 1 2019.
[11] K. Jang and E. Vinitsky, “Simulation to scaled city: Zero-shot policy transfer for traffic control via autonomous vehicles,” in 10th ICCPS, ser. ICCPS ’19, 2019, p. 291–300.
[12] L.Shapley,“Avalueforn-persongames,”ContributionstotheTheory of Games, vol. 2, no. 28, pp. 301–317, 1953.
[13] J. Wang, Y. Zhang, T.-K. Kim, and Y. Gu, “Shapley q-value: A local reward approach to solve global reward games,” in AAAI, 2020.
[14] K. Zhang and Z. Yang, “Multi-agent reinforcement learning: A selec-
tive overview of theories and algorithms,” arXiv:1911.10635, 2019.
[15] P. Sunehag and G. Lever, “Value-decomposition networks for cooper- ative multi-agent learning based on team reward,” in AAMAS, 2018,
pp. 2085–2087.
[16] T. Rashid, G. Farquhar, B. Peng, and S. Whiteson, “Weighted qmix:
Expanding monotonic value function factorisation for deep multi-agent
reinforcement learning,” in NeurIPS, December 2020.
[17] J. Foerster and G. Farquhar, “Counterfactual multi-agent policy gra-
dients,” in AAAI, 2018.
[18] M. Zhou, Z. Liu, P. Sui, Y. Li, and Y. Y. Chung, “Learning implicit
credit assignment for cooperative multi-agent reinforcement learning,”
in NeurIPS, vol. 33, 2020, pp. 11 853–11 864.
[19] R.LoweandY.I.Wu,“Multi-agentactor-criticformixedcooperative-
competitive environments,” in NeurIPS, 2017, pp. 6379–6390.
[20] S. Iqbal and F. Sha, “Actor-attention-critic for multi-agent reinforce-
ment learning,” in ICML, 2019, pp. 2961–2970.
[21] S. Sukhbaatar, A. Szlam, and R. Fergus, “Learning multiagent com-
municationwithbackpropagation,”inNeurIPS. RedHook,NY,USA:
Curran Associates Inc., 2016, p. 2252–2260.
[22] D.Kim,S.Moon,D.Hostallero,W.J.Kang,T.Lee,K.Son,andY.Yi,
“Learning to schedule communication in multi-agent reinforcement
learning,” in ICLR, 2019.
[23] C. Zhang and V. Lesser, “Coordinated multi-agent reinforcement
learning in networked distributed pomdps,” in AAAI, 2011.
[24] F. Wu, S. Zilberstein, and X. Chen, “Online planning for multi-agent systems with bounded communication,” Artificial Intelligence, vol.
175, no. 2, pp. 487 – 511, 2011.
[25] J. Foerster, I. A. Assael, N. de Freitas, and S. Whiteson, “Learning
to communicate with deep multi-agent reinforcement learning,” in NeurIPS, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, Eds. Curran Associates, Inc., 2016, pp. 2137–2145.
[26] J. Jiang and Z. Lu, “Learning attentional communication for multi- agent cooperation,” in NeurIPS. Red Hook, NY, USA: Curran Associates Inc., 2018, p. 7265–7275.
[27] A. Okada, “A noncooperative coalitional bargaining game with random proposers,” Games and Economic Behavior, vol. 16, no. 1, pp. 97–108, 1996.
[28] B. Moldovanu and E. Winter, “Order independent equilibria,” Games and Economic Behavior, pp. 21–35, 1995.
[29] I. Vakilinia and S. Sengupta, “Fair and private rewarding in a coali- tional game of cybersecurity information sharing,” IET Information Security, vol. 13, no. 6, pp. 530–540, 2019.
[30] T. W. Sandholm, “Distributed rational decision making,” Multiagent systems: a modern approach to distributed artificial intelligence, pp. 201–258, 1999.
[31] S.Kraus,O.Shehory,andG.Taase,“Theadvantagesofcompromising in coalition formation with incomplete information,” in AAMAS, vol. 4, 2004, pp. 588–595.
[32] G. Chalkiadakis and C. Boutilier, “Bayesian reinforcement learning for coalition formation under uncertainty,” in AAMAS, 2004, pp. 1090– 1097.
[33] K. Taywade, “Multi-agent reinforcement learning for decentralized coalition formation games,” in AAAI, vol. 35, no. 18, 2021, pp. 15 738– 15 739.
[34] A. Ghorbani and J. Zou, “Data shapley: Equitable valuation of data for machine learning,” in ICML. PMLR, 2019, pp. 2242–2251. [35] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting
model predictions,” NeurIPS, vol. 30, 2017.
[36] T. P. Michalak, K. V. Aadithya, P. L. Szczepanski, B. Ravindran,
and N. R. Jennings, “Efficient computation of the shapley value for game-theoretic network centrality,” J. Artif. Int. Res., vol. 46, no. 1, p. 607–650, Jan. 2013.
[37] G. Chalkiadakis, E. Elkind, and M. Wooldridge, “Computational aspects of cooperative game theory,” Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 5, no. 6, pp. 1–168, 2011.
[38] S. C. Littlechild and G. Owen, “A simple expression for the shapley value in a special case,” Management Science, vol. 20, no. 3, pp. 370– 372, 1973.
[39] A. Dosovitskiy and G. Ros, “CARLA: An open urban driving simu- lator,” in CoRL, 2017, pp. 1–16.
[40] S. Zhou, M. Xie, Y. Jin, F. Miao, and C. Ding, “An end-to-end multi- task object detection using embedded gpu in autonomous driving,” in 22ndInternationalSymposiumonQualityElectronicDesign. ISQED, 2021, pp. 122–128.
[41] Z. Qin, H. Wang, and X. Li, “Ultra fast structure-aware deep lane detection,” arXiv:2004.11757, 2020.
[42] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR, 2016, pp. 770–778.
[43] A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in CVPR, 2019, pp. 12 697–12 705.
[44] S. Li and O. Bastani, “Robust model predictive shielding for safe reinforcement learning with stochastic dynamics,” in ICRA, 2020, pp. 7166–7172.
[45] W. Zhang, O. Bastani, and V. Kumar, “Mamps: Safe multi-agent rein- forcement learning via model predictive shielding,” arXiv:1910.12639, 2019.
[46] S.Li,Y.Wu,X.Cui,H.Dong,F.Fang,andS.Russell,“Robustmulti- agent reinforcement learning via minimax deep deterministic policy gradient,” in AAAI, vol. 33, no. 01, 2019, pp. 4213–4220.
[47] J. Foerster, G. Farquhar, T. Afouras, N. Nardelli, and S. Whiteson, “Counterfactual multi-agent policy gradients,” in AAAI, vol. 32, no. 1, 2018.

Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study⋆

[1] Iea,Globalevoutlook2021–analysis(Apr2021).
URL https://www.iea.org/reports/global- ev- outlook- 20 21
[2] S.Casas,A.Sadat,R.Urtasun,Mp3:Aunifiedmodeltomap,perceive, predict and plan, in: Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, 2021, pp. 14403–14412.
[3] Workshoponautonomousdriving.
URL http://cvpr2021.wad.vision/
[4] Creatingtheautonomousfuturetakesexperienceandvision(2022). URL https://www.mobileye.com/
[5] Ces2020:Enginespoweringl2+tol4(mobileye)(Jan2020).
URL https://s21.q4cdn.com/600692695/files/doc_presen tations/2020/1/Mobileye- CES- 2020- presentation.pdf
[6] We’rebuildingtheworld’smostexperienceddrivertm(2022). URL https://waymo.com/
[7] Q. Xu, Y. Zhou, W. Wang, C. R. Qi, D. Anguelov, Spg: Unsupervised domain adaptation for 3d object detection via semantic point generation, in: Proceedings of the IEEE/CVF International Conference on Com- puter Vision, 2021, pp. 15446–15456.
[8] Omniverseplatformforvirtualcollaboration.
URL https://www.nvidia.com/en-us/omniverse/
[9] Self-drivingcarstechnology&solutionsfromnvidiaautomotive.
URL https://www.nvidia.com/en- us/self- driving- cars/
[10] L. Liu, C. Chen, Q. Pei, S. Maharjan, Y. Zhang, Vehicular edge com- puting and networking: A survey, Mobile Networks and Applications 26 (3) (2021) 1145–1168.
[11] Fatality facts 2019: Yearly snapshot.
URL https://www.iihs.org/topics/fatality- statistics/ detail/yearly- snapshot
[12] 2020 fatality data show increased traffic fatalities during pandemic (Jun 2021).
URL https://www.nhtsa.gov/press- releases/2020- fatali ty- data- show- increased- traffic- fatalities- during- pan demic
[13] Usdot releases new data showing that road fatalities spiked in first half of 2021 (Oct 2021).
URL https://www.nhtsa.gov/press- releases/usdot- relea ses- new- data- showing- road- fatalities- spiked- first- ha lf- 2021
[14] 10 facts about road safety.
URL https://www.who.int/news- room/facts- in- pictures/ detail/road- safety
[15] J. Xu, J. Min, J. Hu, Real-time eye tracking for the assessment of driver fatigue, Healthcare technology letters 5 (2) (2018) 54–58.
[16] A. S. Le, T. Suzuki, H. Aoki, Evaluating driver cognitive distraction by eye tracking: From simulator to driving, Transportation research inter- disciplinary perspectives 4 (2020) 100087.
[17] Y. Ba, W. Zhang, Q. Wang, R. Zhou, C. Ren, Crash prediction with be- havioral and physiological features for advanced vehicle collision avoid- ance system, Transportation Research Part C: Emerging Technologies 74 (2017) 22–33.
[18] U. Z. Abdul Hamid, H. Zamzuri, T. Yamada, M. A. Abdul Rahman, Y. Saito, P. Raksincharoensak, Modular design of artificial potential field and nonlinear model predictive control for a vehicle collision avoid- ance system with move blocking strategy, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232 (10) (2018) 1353–1373.
[19] How does the toyota pre-collision system work?: Toyota safety senseTM.
URL https://www.mossytoyota.com/toyota- pre- collision - system/
[20] T. J. Gordon, L. P. Kostyniuk, P. E. Green, M. A. Barnes, D. Blower, A. D. Blankespoor, S. E. Bogard, Analysis of crash rates and surrogate events: unified approach, Transportation research record 2237 (1) (2011) 1–9.
[21] A. Tarko, Measuring road safety with surrogate events, Elsevier, 2019.
[22] K.-F. Wu, P. P. Jovanis, Crashes and crash-surrogate events: Exploratory modeling with naturalistic driving data, Accident Analysis & Prevention
45 (2012) 507–516.
[23] S. S. Mahmud, L. Ferreira, M. S. Hoque, A. Tavassoli, Application of
proximal surrogate indicators for safety evaluation: A review of recent developments and research needs, IATSS research 41 (4) (2017) 153– 163.
[24] J. Wishart, S. Como, M. Elli, B. Russo, J. Weast, N. Altekar, E. James, Y. Chen, Driving safety performance assessment metrics for ads- equipped vehicles, Tech. rep., SAE Technical Paper (2020).
[25] C. Wang, Y. Xie, H. Huang, P. Liu, A review of surrogate safety mea- sures and their applications in connected and automated vehicles safety modeling, Accident Analysis & Prevention 157 (2021) 106157.
[26] R. Krajewski, J. Bock, L. Kloeker, L. Eckstein, The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems, in: 2018 21st Interna- tional Conference on Intelligent Transportation Systems (ITSC), IEEE, 2018, pp. 2118–2125.
[27] W. Liu, Z. Wang, B. Zhou, S. Yang, Z. Gong, Real-time signal light detection based on yolov5 for railway, in: IOP Conference Series: Earth and Environmental Science, Vol. 769, IOP Publishing, 2021, p. 042069.
[28] Z. Qin, W. Q. Yan, Traffic-sign recognition using deep learning, in: Ge- ometry and Vision: First International Symposium, ISGV 2021, Auck- land, New Zealand, January 28-29, 2021, Revised Selected Papers 1, Springer, 2021, pp. 13–25.
[29] S. Khazaee, A. Tourani, S. Soroori, A. Shahbahrami, C. Y. Suen, A real- time license plate detection method using a deep learning approach, in: International Conference on Pattern Recognition and Artificial Intelli- gence, Springer, 2020, pp. 425–438.
[30] R.-C. Chen, et al., Automatic license plate recognition via sliding- window darknet-yolo deep learning, Image and Vision Computing 87 (2019) 47–56.
[31] D. Neven, B. De Brabandere, S. Georgoulis, M. Proesmans, L. Van Gool, Towards end-to-end lane detection: an instance segmen- tation approach, in: 2018 IEEE intelligent vehicles symposium (IV), IEEE, 2018, pp. 286–291.
[32] B. Roberts, S. Kaltwang, S. Samangooei, M. Pender-Bare, K. Tertikas, J. Redford, A dataset for lane instance segmentation in urban environ- ments, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 533–549.
[33] K.-T. Nguyen, D.-T. Dinh, M. N. Do, M.-T. Tran, Anomaly detection in traffic surveillance videos with gan-based future frame prediction, in: Proceedings of the 2020 International Conference on Multimedia Re- trieval, 2020, pp. 457–463.
[34] P. Giannakeris, V. Kaltsa, K. Avgerinakis, A. Briassouli, S. Vrochidis, I. Kompatsiaris, Speed estimation and abnormality detection from surveillance cameras, in: Proceedings of the IEEE Conference on Com- puter Vision and Pattern Recognition Workshops, 2018, pp. 93–99.
[35] D. Y. Fu, W. Crichton, J. Hong, X. Yao, H. Zhang, A. Truong, A. Narayan, M. Agrawala, C. Ré, K. Fatahalian, Rekall: Specifying video events using compositions of spatiotemporal labels, arXiv preprint arXiv:1910.02993 (2019).
[36] S. Zhou, W. Shen, D. Zeng, M. Fang, Y. Wei, Z. Zhang, Spatial–temporal convolutional neural networks for anomaly detection and localization in crowded scenes, Signal Processing: Image Communication 47 (2016) 358–368.
[37] K. Doshi, Y. Yilmaz, Fast unsupervised anomaly detection in traffic videos, in: Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition Workshops, 2020, pp. 624–625.
[38] H. Ge, R. Xia, H. Sun, Y. Yang, M. Huang, Construction and simulation of rear-end conflicts recognition model based on improved ttc algorithm, IEEE Access 7 (2019) 134763–134771.
[39] L. Dimitriou, K. Stylianou, M. A. Abdel-Aty, Assessing rear-end crash potential in urban locations based on vehicle-by-vehicle interactions, ge- ometric characteristics and operational conditions, Accident Analysis & Prevention 118 (2018) 221–235.
[40] H. Harirforoush, L. Bellalite, G. B. Bénié, Spatial and temporal analysis of seasonal traffic accidents, American journal of traffic and transporta- tion engineering 4 (1) (2019) 7–16.
[41] A. K. Al-Aamri, G. Hornby, L.-C. Zhang, A. A. Al-Maniri, S. S. Pad- madas, Mapping road traffic crash hotspots using gis-based methods: A case study of muscat governorate in the sultanate of oman, Spatial Statistics 42 (2021) 100458.
[42] C. Wang, F. Chen, J. Cheng, W. Bo, P. Zhang, M. Hou, F. Xiao, Random- parameter multivariate negative binomial regression for modeling im- pacts of contributing factors on the crash frequency by crash types, Dis- crete Dynamics in Nature and Society 2020 (2020).
[43] H. Meng, X. Wang, X. Wang, Expressway crash prediction based on traffic big data, in: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning, 2018, pp. 11–16.
[44] T. Sipos, A. Afework Mekonnen, Z. Szabó, Spatial econometric analysis of road traffic crashes, Sustainability 13 (5) (2021) 2492.
[45] S. Mozaffari, O. Y. Al-Jarrah, M. Dianati, P. Jennings, A. Mouzakitis, Deep learning-based vehicle behavior prediction for autonomous driving applications: A review, IEEE Transactions on Intelligent Transportation Systems (2020).
[46] L. Claussmann, M. Revilloud, D. Gruyer, S. Glaser, A review of mo- tion planning for highway autonomous driving, IEEE Transactions on Intelligent Transportation Systems 21 (5) (2019) 1826–1848.
[47] L. Zheng, T. Sayed, F. Mannering, Modeling traffic conflicts for use in road safety analysis: A review of analytic methods and future directions, Analytic methods in accident research 29 (2021) 100142.
[48] J. Wu, Z. Gao, H. Sun, H. Huang, Urban transit system as a scale-free network, Modern Physics Letters B 18 (19n20) (2004) 1043–1049.
[49] Z. Gao, Z. Chen, Y. Liu, K. Huang, Study on the complex network char-
acteristics of urban road system based on gis, in: Geoinformatics 2007: Geospatial Information Technology and Applications, Vol. 6754, Inter- national Society for Optics and Photonics, 2007, p. 67540N.
[50] S. Boccaletti, G. Bianconi, R. Criado, C. I. Del Genio, J. Gómez- Gardenes, M. Romance, I. Sendina-Nadal, Z. Wang, M. Zanin, The
structure and dynamics of multilayer networks, Physics reports 544 (1)
(2014) 1–122.
[51] C. Klinkhamer, E. Krueger, X. Zhan, F. Blumensaat, S. Ukkusuri,
P. S. C. Rao, Functionally fractal urban networks: Geospatial co-location and homogeneity of infrastructure, arXiv preprint arXiv:1712.03883 (2017).
[52] D. J. Watts, S. H. Strogatz, Collective dynamics of ‘small- world’networks, nature 393 (6684) (1998) 440–442.
[53] A.-L. Barabási, R. Albert, Emergence of scaling in random networks, science 286 (5439) (1999) 509–512.
[54] M. Batty, Building a science of cities, Cities 29 (2012) S9–S16.
[55] M. Batty, The new science of cities, MIT press, 2013.
[56] R. Ding, N. Ujang, H. bin Hamid, M. S. Abd Manan, Y. He, R. Li,
J. Wu, Detecting the urban traffic network structure dynamics through the growth and analysis of multi-layer networks, Physica A: Statistical Mechanics and its Applications 503 (2018) 800–817.
[57] S. Wang, L. Zheng, D. Yu, The improved degree of urban road traffic network: A case study of xiamen, china, Physica A: Statistical Mechan- ics and its Applications 469 (2017) 256–264.
[58] Z. Ruan, C. Song, X.-h. Yang, G. Shen, Z. Liu, Empirical analysis of urban road traffic network: A case study in hangzhou city, china, Physica A: Statistical Mechanics and its Applications 527 (2019) 121287.
[59] G. F. Newell, A moving bottleneck, Transportation Research Part B: Methodological 32 (8) (1998) 531–537.
[60] M. F. Hasan, R. C. Baliban, J. A. Elia, C. A. Floudas, Modeling, sim- ulation, and optimization of postcombustion co2 capture for variable feed concentration and flow rate. 1. chemical absorption and membrane processes, Industrial & Engineering Chemistry Research 51 (48) (2012) 15642–15664.
[61] J. McCrea, S. Moutari, A hybrid macroscopic-based model for traf- fic flow in road networks, European Journal of Operational Research 207 (2) (2010) 676–684.
[62] J. Long, Z. Gao, H. Ren, A. Lian, Urban traffic congestion propagation and bottleneck identification, Science in China Series F: Information Sciences 51 (7) (2008) 948.
[63] M. Nakata, A. Yamauchi, J. Tanimoto, A. Hagishima, Dilemma game structure hidden in traffic flow at a bottleneck due to a 2 into 1 lane junction, Physica A: Statistical Mechanics and its Applications 389 (23) (2010) 5353–5361.
[64] C. Li, W. Yue, G. Mao, Z. Xu, Congestion propagation based bottleneck identification in urban road networks, IEEE Transactions on Vehicular Technology 69 (5) (2020) 4827–4841.
[65] Q.-k. Qu, F.-j. Chen, X.-j. Zhou, Road traffic bottleneck analysis for expressway for safety under disaster events using blockchain machine learning, Safety science 118 (2019) 925–932.
[66] S. Mohammadian, Z. Zheng, M. M. Haque, A. Bhaskar, Performance of continuum models for realworld traffic flows: Comprehensive bench- marking, Transportation Research Part B: Methodological 147 (2021) 132–167.
[67] Q. Lu, T. Tettamanti, D. Hörcher, I. Varga, The impact of autonomous vehicles on urban traffic network capacity: an experimental analysis by microscopic traffic simulation, Transportation Letters 12 (8) (2020) 540– 549.
[68] A. B. Parsa, R. Shabanpour, A. Mohammadian, J. Auld, T. Stephens, A data-driven approach to characterize the impact of connected and au- tonomous vehicles on traffic flow, Transportation Letters (2020) 1–9.
[69] I. Mavromatis, A. Tassi, R. J. Piechocki, M. Sooriyabandara, On urban traffic flow benefits of connected and automated vehicles, in: 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), IEEE, 2020, pp. 1–7.
[70] J. Zhang, Y. Zheng, D. Qi, R. Li, X. Yi, Dnn-based prediction model for spatio-temporal data, in: Proceedings of the 24th ACM SIGSPATIAL in- ternational conference on advances in geographic information systems, 2016, pp. 1–4.
[71] J. Zhang, Y. Zheng, D. Qi, Deep spatio-temporal residual networks for citywide crowd flows prediction, in: Thirty-first AAAI conference on artificial intelligence, 2017.
[72] Y. Li, R. Yu, C. Shahabi, Y. Liu, Diffusion convolutional recur- rent neural network: Data-driven traffic forecasting, arXiv preprint arXiv:1707.01926 (2017).
[73] X. Cheng, R. Zhang, J. Zhou, W. Xu, Deeptransport: Learning spatial-
temporal dependency for traffic condition forecasting, in: 2018 Interna- tional Joint Conference on Neural Networks (IJCNN), IEEE, 2018, pp. 1–8.
[74] B. Yu, H. Yin, Z. Zhu, Spatio-temporal graph convolutional net- works: A deep learning framework for traffic forecasting, arXiv preprint arXiv:1709.04875 (2017).
[75] J. J. Q. Yu, J. Gu, Real-time traffic speed estimation with graph convolu- tional generative autoencoder, IEEE Transactions on Intelligent Trans- portation Systems 20 (10) (2019) 3940–3951.
[76] B. Yu, Y. Lee, K. Sohn, Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (gcn), Transportation research part C: emerging tech- nologies 114 (2020) 189–204.
[77] J. Fu, W. Zhou, Z. Chen, Bayesian spatio-temporal graph convolutional network for traffic forecasting, arXiv preprint arXiv:2010.07498 (2020).
[78] G. Jin, Y. Cui, L. Zeng, H. Tang, Y. Feng, J. Huang, Urban ride- hailing demand prediction with multiple spatio-temporal information fu- sion network, Transportation Research Part C: Emerging Technologies
117 (2020) 102665.
[79] Z. Zhang, Q. He, H. Tong, J. Gou, X. Li, Spatial-temporal traffic flow
pattern identification and anomaly detection with dictionary-based com- pression theory in a large-scale urban network, Transportation Research Part C: Emerging Technologies 71 (2016) 284–302.
[80] Z. Zhang, L. Lin, Abnormal spatial-temporal pattern analysis for niagara frontier border wait times, arXiv preprint arXiv:1711.00054 (2017).
[81] T. Huang, C. Liu, A. Sharma, S. Sarkar, Traffic system anomaly de- tection using spatiotemporal pattern networks, International Journal of Prognostics and Health Management 9 (1) (2018).
[82] I. Pugachev, Y. Kulikov, G. Markelov, N. Sheshera, Factor analysis of traffic organization and safety systems, Transportation research procedia 20 (2017) 529–535.
[83] A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, V. Koltun, CARLA: An open urban driving simulator, in: Proceedings of the 1st Annual Confer- ence on Robot Learning, 2017, pp. 1–16.
[84] Simulation of urban mobility.
URL https://www.eclipse.org/sumo/
[85] F. Mannering, C. R. Bhat, V. Shankar, M. Abdel-Aty, Big data, tra- ditional data and the tradeoffs between prediction and causality in highway-safety analysis, Analytic methods in accident research 25 (2020) 100113.
[86] W. S. Meyers, Comparison of truck and passenger-car accident rates on limitedaccess facilities, Transportation Research Record 808 (1981) 48– 53.
[87] Y. Sugiyama, M. Fukui, M. Kikuchi, K. Hasebe, A. Nakayama, K. Nishinari, S.-i. Tadaki, S. Yukawa, Traffic jams without bottle- necks—experimental evidence for the physical mechanism of the for- mation of a jam, New journal of physics 10 (3) (2008) 033001.
[88] N. Wojke, A. Bewley, D. Paulus, Simple online and realtime tracking with a deep association metric, in: 2017 IEEE international conference on image processing (ICIP), IEEE, 2017, pp. 3645–3649.
[89] G. Jocher, A. Stoken, J. Borovec, NanoCode012, A. Chaurasia, TaoXie, L. Changyu, A. V, Laughing, tkianai, yxNONG, A. Hogan, loren- zomammana, AlexWang1900, J. Hajek, L. Diaconu, Marc, Y. Kwon, oleg, wanghaoyang0106, Y. Defretin, A. Lohia, ml5ah, B. Milanko, B. Fineran, D. Khromov, D. Yiwei, Doug, Durgesh, F. Ingham, ultra- lytics/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations (Apr. 2021). doi:10.5281/zenodo.4679653. URL https://doi.org/10.5281/zenodo.4679653
[90] R. W. Schafer, What is a savitzky-golay filter?[lecture notes], IEEE Sig- nal processing magazine 28 (4) (2011) 111–117.
[91] study: average car size is increasing - thezebra.com.
URL https://www.thezebra.com/resources/driving/avera ge- car- size/
[92] by the numbers - standard dimensions of a semi-truck.
URL https://www.summittruckgroup.com/blog/by- the- num bers- - - standard- dimensions- of- a- semi- truck- - 26281
[93] J. Sochor, A. Herout, J. Havel, Boxcars: 3d boxes as cnn input for im- proved fine-grained vehicle recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 3006– 3015.
[94] J. Sochor, J. Španˇhel, A. Herout, Boxcars: Improving fine-grained recognition of vehicles using 3-d bounding boxes in traffic surveillance, IEEE transactions on intelligent transportation systems 20 (1) (2018) 97–108.
[95] Z. Ma, D. Chang, J. Xie, Y. Ding, S. Wen, X. Li, Z. Si, J. Guo, Fine- grained vehicle classification with channel max pooling modified cnns, IEEE Transactions on Vehicular Technology 68 (4) (2019) 3224–3233.
[96] X. Ke, Y. Zhang, Fine-grained vehicle type detection and recognition based on dense attention network, Neurocomputing 399 (2020) 247– 257.
[97] J.J.Faraway,LinearmodelswithR,ChapmanandHall/CRC,2004.
[98] F. Afghah, A. Razi, R. Soroushmehr, H. Ghanbari, K. Najarian, Game theoretic approach for systematic feature selection; application in false
alarm detection in intensive care units, Entropy 20 (3) (2018) 190.
[99] M.M.Minderhoud,P.H.Bovy,Extendedtime-to-collisionmeasuresfor road traffic safety assessment, Accident Analysis & Prevention 33 (1)
(2001) 89–97.
[100] M.S.Elli,J.Wishart,S.Como,S.Dhakshinamoorthy,J.Weast,Evalu-
ation of operational safety assessment (osa) metrics for automated vehi-
cles in simulation, SAE Technical Paper (2021).
[101] SAE International (In development), SAE J3237 - operational safety
metrics for verification and validation (V&V) of automated driving sys-
tems (ads), Recommended Practice (2020).
[102] D.Gettman,L.Head,Surrogatesafetymeasuresfromtrafficsimulation
models, Transportation Research Record 1840 (1) (2003) 104–115.
[103] S. Herbel, L. Laing, C. McGovern, Highway safety improvement pro- gram (hsip) manual, US Department of Transportation, Federal High-
way Administration, Office of Safety, Washington, DC (2010).
[104] A. Hawkins, California’s self-driving car reports are imperfect, but
they’re better than nothing, The Verge (2019).
[105] S. Mohammadian, M. M. Haque, Z. Zheng, A. Bhaskar, Integrating
safety into the fundamental relations of freeway traffic flows: A conflict- based safety assessment framework, Analytic Methods in Accident Re- search 32 (2021) 100187.
[106] A.Torday,D.Baumann,A.-G.Dumont,Indicatorformicrosimulation- based safety evaluation, Tech. rep. (2003).

Secure Time-Sensitive Software-Defined Networking in Vehicles

[1] K. Matheus and T. Königseder, Automotive Ethernet. United Kingdom: Cambridge University Press, Jan. 2015.
Cambridge,
[2] S. Brunner, J. Roder, M. Kucera, and T. Waas, “Automotive E/E- architecture enhancements by usage of ethernet TSN,” in 2017 13th Workshop on Intelligent Solutions in Embedded Systems (WISES). IEEE, 2017, pp. 9–13.
[3] IEEE802.1WorkingGroup,“IEEEStandardforLocalandMetropolitan Area Network–Bridges and Bridged Networks,” Standard IEEE 802.1Q- 2018 (Revision of IEEE Std 802.1Q-2014), Jul. 2018.
[4] N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, and J. Turner, “OpenFlow: Enabling Innovation in Campus Networks,” ACM SIGCOMM Computer Communication Review, vol. 38, no. 2, pp. 69–74, 2008.
[5] K.Halba,C.Mahmoudi,andE.Griffor,“RobustSafetyforAutonomous Vehicles through Reconfigurable Networking,” in Proceedings of the 2nd International Workshop on Safe Control of Autonomous Vehicles, ser. Electronic Proceedings in Theoretical Computer Science, vol. 269. Open Publishing Association, 2018, pp. 48–58.
[6] T. Häckel, P. Meyer, F. Korf, and T. C. Schmidt, “Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication,” in 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring). IEEE, Apr. 2019, pp. 1–5.
[7] M.Haeberle,F.Heimgaertner,H.Loehr,N.Nayak,D.Grewe,S.Schildt, and M. Menth, “Softwarization of Automotive E/E Architectures: A Software-Defined Networking Approach,” in 2020 IEEE Vehicular Net- working Conference (VNC). IEEE, Dec. 2020, pp. 1–8.
[8] N. G. Nayak, F. Dürr, and K. Rothermel, “Time-sensitive Software- defined Network (TSSDN) for Real-time Applications,” in Proceedings of the 24th International Conference on Real-Time Networks and Systems, ser. RTNS ’16. ACM, 2016, pp. 193–202.
[9] P. Mundhenk, Security for Automotive Electrical/Electronic (E/E) Architectures. Göttingen: Cuvillier, Aug. 2017.
[10] C. Miller and C. Valasek, “Remote Exploitation of an Unaltered Passenger Vehicle,” Black Hat USA, vol. 2015, p. 91, 2015.
[11] S. Shin, L. Xu, S. Hong, and G. Gu, “Enhancing Network Security through Software Defined Networking (SDN),” in 2016 25th Int. Conf. on Computer Communication and Networks (ICCCN). IEEE, Aug. 2016.
[12] O. Yurekten and M. Demirci, “SDN-based cyber defense: A survey,” Future Gen. Computer Systems, vol. 115, pp. 126–149, Feb. 2021.
[13] T. Häckel, A. Schmidt, P. Meyer, F. Korf, and T. C. Schmidt, “Strategies
for Integrating Controls Flows in Software-Defined In-Vehicle Networks and Their Impact on Network Security,” in 2020 IEEE Vehicular Networking Conference (VNC). IEEE, Dec. 2020.
[14] M. Dibaei, X. Zheng, K. Jiang, R. Abbas, S. Liu, Y. Zhang, Y. Xiang, and S. Yu, “Attacks and defences on intelligent connected vehicles: a survey,” Digital Communications and Networks, vol. 6, no. 4, pp. 399– 421, Nov. 2020.
[15] AUTOSAR, “SOME/IP Protocol Specification,” AUTOSAR, Tech. Rep. 696, Dec. 2017.
[16] A. Kampmann, B. Alrifaee, M. Kohout, A. Wüstenberg, T. Woopen, M. Nolte, L. Eckstein, and S. Kowalewski, “A Dynamic Service- Oriented Software Architecture for Highly Automated Vehicles,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019, pp. 2101–2108.
[17] L. Lo Bello and W. Steiner, “A Perspective on IEEE Time-Sensitive Networking for Industrial Communication and Automation Systems,” Proceedings of the IEEE, vol. 107, no. 6, pp. 1094–1120, Jun. 2019.
[18] T. Steinbach, H.-T. Lim, F. Korf, T. C. Schmidt, D. Herrscher, and A. Wolisz, “Tomorrow’s In-Car Interconnect? A Competitive Evaluation of IEEE 802.1 AVB and Time-Triggered Ethernet (AS6802),” in 2012 IEEE Vehicular Technology Conference (VTC Fall). IEEE, Sep. 2012.
[19] V. Gavrilu ̧t, L. Zhao, M. L. Raagaard, and P. Pop, “AVB-Aware Routing and Scheduling of Time-Triggered Traffic for TSN,” IEEE Access, vol. 6, pp. 75 229–75 243, Nov. 2018.
[20] A. A. Syed, S. Ayaz, T. Leinmuller, and M. Chandra, “MIP-based Joint Scheduling and Routing with Load Balancing for TSN based In-vehicle Networks,” in 2020 IEEE Vehicular Networking Conference (VNC). IEEE, Dec. 2020.
[21] R. Enns, M. Bjorklund, J. Schoenwaelder, and A. Bierman, “Network Configuration Protocol (NETCONF),” IETF, RFC 6241, June 2011.
[22] M. Bjorklund, “YANG - A Data Modeling Language for the Network Configuration Protocol (NETCONF),” IETF, RFC 6020, October 2010. [23] D. Kreutz, F. M. V. Ramos, P. E. Veríssimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig, “Software-Defined Networking: A Com- prehensive Survey,” Proceedings of the IEEE, vol. 103, no. 1, pp. 14–76, Jan. 2015.
[24] Open Networking Foundation, “OpenFlow Switch Specification,” ONF,
Standard ONF TS-025, 2015.
[25] D. Thiele and R. Ernst, “Formal Analysis Based Evaluation of Software
Defined Networking for Time-Sensitive Ethernet,” in 2016 Design, Automation Test in Europe Conference Exhibition (DATE). IEEE, Mar. 2016, pp. 31–36.
[26] N. G. Nayak, F. Dürr, and K. Rothermel, “Incremental Flow Schedul- ing and Routing in Time-Sensitive Software-Defined Networks,” IEEE Transactions on Industrial Informatics, vol. 14, pp. 2066–2075, 2018.
[27] T. Gerhard, T. Kobzan, I. Blocher, and M. Hendel, “Software-defined Flow Reservation: Configuring IEEE 802.1Q Time-Sensitive Networks by the Use of Software-Defined Networking,” in 2019 24th IEEE Inter- national Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, Sep. 2019, pp. 216–223.
[28] S. Nam, H. Kim, and S.-G. Min, “Simplified Stream Reservation Protocol over Software-Defined Networks for In-vehicle Time-Sensitive Networking,” IEEE Access, pp. 1–12, Jun. 2021.
[29] R. Rotermund, T. Häckel, P. Meyer, F. Korf, and T. C. Schmidt, “Re- quirements Analysis and Performance Evaluation of SDN Controllers for Automotive Use Cases,” in 2020 IEEE Vehicular Networking Conference (VNC). IEEE, Dec. 2020.
[30] S. Checkoway, D. Mccoy, B. Kantor, D. Anderson, H. Shacham, S. Savage, K. Koscher, A. Czeskis, F. Roesner, and T. Kohno, “Comprehensive Experimental Analyses of Automotive Attack Surfaces,” in Proceedings of the 20th USENIX Security Symposium, vol. 4. USENIX Association, Aug. 2011, pp. 77–92.
[31] L. Wang, S. Jajodia, A. Singhal, P. Cheng, and S. Noel, “k-Zero Day Safety: A Network Security Metric for Measuring the Risk of Unknown Vulnerabilities,” IEEE Transactions on Dependable and Secure Comput- ing, vol. 11, no. 1, pp. 30–44, Jan. 2014.
[32] A. Ruddle, D. Ward, B. Weyl, S. Idrees, Y. Roudier, M. Friedewald, T. Leimbach, A. Fuchs, S. Gürgens, O. Henniger, R. Rieke, M. Ritscher, H. Broberg, L. Apvrille, R. Pacalet, and G. Pedroza, “Security Re- quirements For Automotive On-Board Networks Based On Dark-Side Scenarios,” Evita Deliverable 2.3, 2009.
[33] J.-P. Monteuuis, A. Boudguiga, J. Zhang, H. Labiod, A. Servel, and P. Urien, “SARA: Security Automotive Risk Analysis Method,” in Proceedings of the 4th ACM Workshop on Cyber-Physical System Security, ser. CPSS ’18. ACM, 2018, pp. 3–14.
[34] S. Longari, A. Cannizzo, M. Carminati, and S. Zanero, “A Secure-by- Design Framework for Automotive On-board Network Risk Analysis,” in 2019 IEEE Vehicular Networking Conf. (VNC). IEEE, Dec. 2019.
[35] V. L. L. Thing and J. Wu, “Autonomous Vehicle Security: A Taxonomy of Attacks and Defences,” in 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Commu- nications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, Dec. 2016.
[36] M. D. Pesé, K. Schmidt, and H. Zweck, “Hardware/Software Co-Design of an Automotive Embedded Firewall,” in SAE Technical Paper. SAE International, Mar. 2017.
[37] M. Rumez, A. Duda, P. Grunder, R. Kriesten, and E. Sax, “Integration of Attribute-based Access Control into Automotive Architectures,” in 2019 IEEE Intelligent Vehicles Symposium (IV). IEEE, Jun. 2019.
[38] S. Seifert and R. Obermaisser, “Secure Automotive Gateway - Secure Communication for Future Cars,” in 2014 12th IEEE International Conference on Industrial Informatics (INDIN), 2014, pp. 213–220.
[39] G. K. Rajbahadur, A. J. Malton, A. Walenstein, and A. E. Hassan, “A Survey of Anomaly Detection for Connected Vehicle Cybersecurity and Safety,” in 2018 IEEE Intelligent Vehicles Symp. (IV). IEEE, Jun. 2018.
[40] L. Yang, A. Moubayed, I. Hamieh, and A. Shami, “Tree-Based Intelli- gent Intrusion Detection System in Internet of Vehicles,” in 2019 IEEE Global Communications Conf. (GLOBECOM). IEEE, 2019, pp. 1–6.
[41] P. Meyer, T. Häckel, F. Korf, and T. C. Schmidt, “Network Anomaly Detection in Cars based on Time-Sensitive Ingress Control,” in 2020 IEEE Vehicular Tech. Conf. (VTC2020-Fall). IEEE, Nov. 2020, pp. 1–5.
[42] P. Waszecki, P. Mundhenk, S. Steinhorst, M. Lukasiewycz, R. Karri, and S. Chakraborty, “Automotive electrical and electronic architecture security via distributed in-vehicle traffic monitoring,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 11, pp. 1790–1803, Nov. 2017.
[43] F. Langer, F. Schüppel, and L. Stahlbock, “Establishing an Automotive Cyber Defense Center,” in 17th escar Europe : embedded security in cars, 2019.
[44] Q. Hu and F. Luo, “Review of Secure Communication Approaches for In-Vehicle Network,” International Journal of Automotive Technology, vol. 19, no. 5, pp. 879–894, Sep. 2018.
[45] S. Fassak, Y. E. H. E. Idrissi, N. Zahid, and M. Jedra, “A secure protocol for session keys establishment between ECUs in the CAN bus,” in 2017 International Conference on Wireless Networks and Mobile Communications (WINCOM). IEEE, Nov. 2017.
[46] D. Püllen, N. A. Anagnostopoulos, T. Arul, and S. Katzenbeisser, “Securing FlexRay-Based In-Vehicle Networks,” Microprocessors and Microsystems, p. 103144, Jun. 2020.
[47] IEEE, “IEEE Standard for Local and metropolitan area networks-Media Access Control (MAC) Security,” Std. IEEE 802.1AE-2018, Dec. 2018.
[48] M. Khodaei, H. Jin, and P. Papadimitratos, “SECMACE: Scalable and Robust Identity and Credential Management Infrastructure in Vehicular Communication Systems,” Trans. Intell. Transport. Sys., vol. 19, no. 5,
pp. 1430–1444, May 2018.
[49] M. Rumez, D. Grimm, R. Kriesten, and E. Sax, “An Overview of Au-
tomotive Service-Oriented Architectures and Implications for Security
Countermeasures,” IEEE Access, vol. 8, pp. 221 852–221 870, 2020.
[50] Open Networking Foundation, “OpenFlow Management and
Configuration Protocol(OF-Config 1.1.1),” Std. ONF TS-008, 2013.
[51] L. Leonardi, L. L. Bello, and G. Patti, “Bandwidth partitioning for Time-Sensitive Networking flows in automotive communications,” IEEE
Communications Letters, pp. 1–1, 2021.
[52] A.Kern,D.Reinhard,T.Streichert,andJ.Teich,“GatewayStrategiesfor
Embedding of Automotive CAN-Frames into Ethernet-Packets and Vice Versa,” in Architecture of Computing Systems - ARCS 2011. Springer Berlin Heidelberg, 2011, pp. 259–270.
[53] OpenSim Ltd., “OMNeT++ Discrete Event Simulator.” [Online]. Available: https://omnetpp.org/
[54] T. Häckel, P. Meyer, F. Korf, and T. C. Schmidt, “SDN4CoRE: A Simulation Model for Software-Defined Networking for Communication over Real-Time Ethernet,” in Proc. of the 6th Int. OMNeT++ Community Summit 2019, ser. EPiC Series in Computing, M. Zongo, et al., Eds., vol. 66. EasyChair, Dec. 2019, pp. 24–31.
[55] OpenSim Ltd., “INET Framework.” [Online]. Available: https: //inet.omnetpp.org/
[56] D. Klein and M. Jarschel, “An OpenFlow extension for the OMNeT++ INET framework,” in Proceedings of the 6th International ICST Confer- ence on Simulation Tools and Techniques, ser. SimuTools ’13. Brussels, BEL: ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2013, pp. 322–329.
[57] P. Meyer, F. Korf, T. Steinbach, and T. C. Schmidt, “Simulation of Mixed Critical In-vehicular Networks,” in Recent Advances in Network Simulation. Springer, 2019, pp. 317–345.
[58] T. Steinbach, H.-T. Lim, F. Korf, T. C. Schmidt, D. Herrscher, and A. Wolisz, “Beware of the Hidden! How Cross-traffic Affects Quality Assurances of Competing Real-time Ethernet Standards for In-Car Com- munication,” in 2015 IEEE Conference on Local Computer Networks (LCN), Oct. 2015, pp. 1–9
[59] P. Meyer, T. Steinbach, F. Korf, and T. C. Schmidt, “Extending IEEE 802.1 AVB with Time-triggered Scheduling: A Simulation Study of the Coexistence of Synchronous and Asynchronous Traffic,” in 2013 IEEE Vehicular Networking Conference (VNC). IEEE, Dec. 2013, pp. 47–54.

On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

[1] Gleifer Vaz Alves, Louise Dennis, and Michael Fisher. A double-level model checking approach for an agent-based autonomous vehicle and road junction regulations. Journal of Sensor and Actuator Networks, 10(3):41, 2021.
[2]A ́lvaroArcos-Garc ́ıa,JuanAAlvarez-Garcia,andLuisM Soria-Morillo. Deep neural network for traffic sign recog- nition systems: An analysis of spatial transformers and stochastic optimisation methods. Neural Networks, 99:158– 165, 2018.
[3] Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi-
ancarlo Baldan, and Oscar Beijbom. nuscenes: A multi- modal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11621–11631, 2020.
[4] Jinghao Cao, Junju Zhang, and Xin Jin. A traffic-sign detec- tion algorithm based on improved sparse r-cnn. IEEE Access, 9:122774–122788, 2021.
[5] Konstantin Clemens. Geocoding with openstreetmap data. GEOProcessing 2015, page 10, 2015.
[6] LucaCultrera,LorenzoSeidenari,FedericoBecattini,Pietro Pala, and Alberto Del Bimbo. Explaining autonomous driv- ing by learning end-to-end visual attention. In Proceedings
of the IEEE/CVF Conference on Computer Vision and Pat-
tern Recognition (CVPR) Workshops, June 2020.
[7] Isha Dua, Thrupthi Ann John, Riya Gupta, and CV Jawahar. Dgaze: Driver gaze mapping on road. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), pages 5946–5953. IEEE, 2020.
[8] Christian Ertler, Jerneja Mislej, Tobias Ollmann, Lorenzo
Porzi, Gerhard Neuhold, and Yubin Kuang. The mapillary traffic sign dataset for detection and classification on a global scale. In European Conference on Computer Vision, pages 68–84. Springer, 2020.
[9] Nathan Fulton, Nathan Hunt, Nghia Hoang, and Subhro Das. Formal verification of end-to-end learning in cyber- physical systems: Progress and challenges. arXiv preprint arXiv:2006.09181, 2020.
[10] Ross Greer, Nachiket Deo, and Mohan Trivedi. Trajectory prediction in autonomous driving with a lane heading auxil- iary loss. IEEE Robotics and Automation Letters, 6(3):4907– 4914, 2021.
[11] Dapeng Guo, Melody Moh, and Teng-Sheng Moh. Vision- based autonomous driving for smart city: a case for end-to- end learning utilizing temporal information. In International Conference on Smart Computing and Communication, pages 19–29. Springer, 2020.
[12] Yunfei Guo, Wei Feng, Fei Yin, Tao Xue, Shuqi Mei, and Cheng-Lin Liu. Learning to understand traffic signs. In Pro- ceedings of the 29th ACM International Conference on Mul- timedia, MM ’21, page 2076–2084, New York, NY, USA, 2021. Association for Computing Machinery.
[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
[14] LinLin Huang. Chinese traffic sign database.
[15] Iuliia Kotseruba and John K Tsotsos. Behavioral research and practical models of drivers’ attention. arXiv preprint
arXiv:2104.05677, 2021.
[16] Ninad Kulkarni, Akshay Rangesh, Jonathan Buck, Jeremy
Feltracco, Mohan M Trivedi, Nachiket Deo, Ross Greer, Saman Sarraf, and Suchitra Sathyanarayana. Lisa amazon- mlsl vehicle attributes (lava) dataset, Jun 2021.
[17] Fredrik Larsson and Michael Felsberg. Using fourier de- scriptors and spatial models for traffic sign recognition. In Scandinavian conference on image analysis, pages 238–249. Springer, 2011.
[18] FahadLateef,MohamedKas,andYassineRuichek.Saliency heat-map as visual attention for autonomous driving using generative adversarial network (gan). IEEE Transactions on Intelligent Transportation Systems, 2021.
[19] Chengxi Li, Stanley H Chan, and Yi-Ting Chen. Who make drivers stop? towards driver-centric risk assessment: Risk object identification via causal inference. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10711–10718. IEEE, 2020.
[20] Kaouther Messaoud, Nachiket Deo, Mohan M Trivedi, and Fawzi Nashashibi. Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation. arXiv preprint arXiv:2005.02545, 2020.
[21] Andreas Møgelmose, Dongran Liu, and Mohan M Trivedi. Traffic sign detection for us roads: Remaining challenges and a case for tracking. In 17th International IEEE Con- ference on Intelligent Transportation Systems (ITSC), pages 1394–1399. IEEE, 2014.
[22] Anwesan Pal, Sayan Mondal, and Henrik I Christensen. ” looking at the right stuff”-guided semantic-gaze for au- tonomous driving. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 11883–11892, 2020.
[23] Akshay Rangesh, Nachiket Deo, Ross Greer, Pujitha Gu- naratne, and Mohan M. Trivedi. Autonomous vehicles that alert humans to take-over controls: Modeling with real- world data. In 2021 IEEE International Intelligent Trans- portation Systems Conference (ITSC), pages 231–236, 2021.
[24] Akshay Rangesh and Mohan Manubhai Trivedi. No blind spots: Full-surround multi-object tracking for autonomous vehicles using cameras and lidars. IEEE Transactions on Intelligent Vehicles, 4(4):588–599, 2019.
[25] Ravi Kumar Satzoda and Mohan Manubhai Trivedi. Drive analysis using vehicle dynamics and vision-based lane se- mantics. IEEE Transactions on Intelligent Transportation Systems, 16(1):9–18, 2015.
[26] Vladislav Igorevich Shakhuro and AS Konouchine. Russian traffic sign images dataset. Computer optics, 40(2):294–300, 2016.
[27] J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Networks, (0):–, 2012.
[28] Jinming Su, Changqun Xia, and Jia Li. Exploring driving- aware salient object detection via knowledge transfer. In 2021 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE, 2021.
[29] D. Temel, M. Chen, and G. AlRegib. Traffic sign detection under challenging conditions: A deeper look into perfor- mance variations and spectral characteristics. IEEE Trans- actions on Intelligent Transportation Systems, pages 1–11, 2019.
[30] Jianming Zhang, Zhipeng Xie, Juan Sun, Xin Zou, and Jin Wang. A cascaded r-cnn with multiscale attention and im- balanced samples for traffic sign detection. IEEE Access, 8:29742–29754, 2020.
[31] Zehua Zhang, Ashish Tawari, Sujitha Martin, and David Crandall. Interaction graphs for object importance esti- mation in on-road driving videos. In 2020 IEEE Inter- national Conference on Robotics and Automation (ICRA), pages 8920–8927. IEEE, 2020.
[32] Xingyi Zhou, Dequan Wang, and Philipp Kra ̈henbu ̈hl. Ob- jects as points. arXiv preprint arXiv:1904.07850, 2019. [33] Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli
Li, and Shimin Hu. Traffic-sign detection and classification in the wild. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

A Comprehensive Survey on the Convergence of Vehicular Social Networks and Fog Computing

[1] Aadil,F.,Ahsan,W.,Rehman,Z.U.,Shah,P.A.,Rho,S.,Mehmood, I., 2018. Clustering algorithm for internet of vehicles (iov) based on dragonfly optimizer (cavdo). The Journal of Supercomputing 74, 4542–4567.
[2] Aazam,M.,Huh,E.N.,2015.Fogcomputingmicrodatacenterbased dynamic resource estimation and pricing model for iot, in: 2015 IEEE 29th International Conference on Advanced Information Net- working and Applications, IEEE. pp. 687–694.
[3] Abraham,A.,Das,S.,Roy,S.,2008.Swarmintelligencealgorithms for data clustering, in: Soft computing for knowledge discovery and data mining. Springer, pp. 279–313.
[4] Ai, B., Cheng, X., Yang, L., Zhong, Z.D., Ding, J.W., Song, H., 2014. Social network services for rail traffic applications. IEEE Intelligent Systems 29, 63–69.
[5] Alam, K.M., Saini, M., Ahmed, D.T., El Saddik, A., 2014. Vedi: A vehicular crowd-sourced video social network for vanets, in: 39th Annual IEEE Conference on Local Computer Networks Workshops, IEEE. pp. 738–745.
[6] Alghamdi, S.A., 2020. Novel path similarity aware clustering and safety message dissemination via mobile gateway selection in cel- lular 5g-based v2x and d2d communication for urban environment. Ad Hoc Networks , 102150.
[7] Ansari, K., 2018. Cloud computing on cooperative cars (c4s): An architecture to support navigation-as-a-service, in: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), IEEE. pp. 794–801.
[8] Arkian, H.R., Diyanat, A., Pourkhalili, A., 2017. Mist: Fog-based data analytics scheme with cost-efficient resource provisioning for iot crowdsensing applications. Journal of Network and Computer Applications 82, 152–165.
[9] Arnaboldi, V., Conti, M., Delmastro, F., 2014. Cameo: A novel context-aware middleware for opportunistic mobile social networks. Pervasive and Mobile Computing 11, 148–167.
[10] Atzori, L., Iera, A., Morabito, G., 2011a. Making things social- ize in the internet—does it help our lives?, in: Proceedings of ITU Kaleidoscope 2011: The Fully Networked Human?-Innovations for Future Networks and Services (K-2011), IEEE. pp. 1–8.
[11] Atzori,L.,Iera,A.,Morabito,G.,2011b.Siot:Givingasocialstruc- ture to the internet of things. IEEE communications letters 15, 1193– 1195.
[12] Atzori, L., Iera, A., Morabito, G., Nitti, M., 2012. The social inter- net of things (siot)–when social networks meet the internet of things: Concept, architecture and network characterization. Computer net- works 56, 3594–3608.
[13] Bai,F.,Sadagopan,N.,Helmy,A.,2003.Theimportantframework for analyzing the impact of mobility on performance of routing pro- tocols for adhoc networks. Ad hoc networks 1, 383–403.
[14] Benton, K., Camp, L.J., Small, C., 2013. Openflow vulnerability assessment, in: Proceedings of the second ACM SIGCOMM work- shop on Hot topics in software defined networking, pp. 151–152.
[15] Bigwood,G.,Henderson,T.,2011.Ironman:Usingsocialnetworks to add incentives and reputation to opportunistic networks, in: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Com- puting, IEEE. pp. 65–72.
[16] Bitam, S., Mellouk, A., Zeadally, S., 2015. Vanet-cloud: a generic cloud computing model for vehicular ad hoc networks. IEEE Wire- less Communications 22, 96–102.
[17] Bonomi, F., Milito, R., Zhu, J., Addepalli, S., 2012. Fog computing and its role in the internet of things, in: Proceedings of the first edi- tion of the MCC workshop on Mobile cloud computing, pp. 13–16.
[18] Box, D., Ehnebuske, D., Kakivaya, G., Layman, A., Mendelsohn, N., Nielsen, H.F., Thatte, S., Winer, D., 2000. Simple object access protocol (soap) 1.1.
[19] Bradai, A., Ahmed, T., 2014. Reviv: Selective rebroadcast mecha- nism for video streaming over vanet, in: 2014 IEEE 79th Vehicular Technology Conference (VTC Spring), IEEE. pp. 1–6.
[20] Bravo-Torres, J.F., López-Nores, M., Blanco-Fernández, Y., Pazos- Arias, J.J., 2013. Leveraging short-lived social networks in vehic- ular environments, in: Second International Conference on Future Generation Communication Technologies (FGCT 2013), IEEE. pp. 196–200.
[21] Brennand,C.A.,Boukerche,A.,Meneguette,R.,Villas,L.A.,2017. A novel urban traffic management mechanism based on fog, in: 2017 IEEE Symposium on Computers and Communications (ISCC), IEEE. pp. 377–382.
[22] Buyya,R.,Yeo,C.S.,Venugopal,S.,Broberg,J.,Brandic,I.,2009. Cloud computing and emerging it platforms: Vision, hype, and re- ality for delivering computing as the 5th utility. Future Generation computer systems 25, 599–616.
[23] Chakeres, I.D., Belding-Royer, E.M., 2004. Aodv routing protocol implementation design, in: 24th International Conference on Dis- tributed Computing Systems Workshops, 2004. Proceedings., IEEE. pp. 698–703.
[24] Cheng, H.T., Shan, H., Zhuang, W., 2011. Infotainment and road safety service support in vehicular networking: From a communi- cation perspective. Mechanical systems and signal processing 25, 2020–2038.
[25] Cheng, N., Lyu, F., Chen, J., Xu, W., Zhou, H., Zhang, S., Shen, X., 2018. Big data driven vehicular networks. IEEE Network 32, 160–167.
[26] Cheng, X., Chen, C., Zhang, W., Yang, Y., 2017. 5g-enabled coop- erative intelligent vehicular (5genciv) framework: When benz meets marconi. IEEE Intelligent Systems 32, 53–59.
[27] Consortium,O.,etal.,2017.Openfogreferencearchitectureforfog computing. Architecture Working Group , 1–162.
[28] Cruz,P.,daSilva,F.F.,Pacheco,R.G.,Couto,R.D.S.,Velloso,P.B., Campista, M.E.M., Costa, L.H.M.K., et al., 2018. Sensingbus: Us- ing bus lines and fog computing for smart sensing the city. IEEE Cloud Comput. 5, 58–69.
[29] Cunha, F.D., Vianna, A.C., Mini, R.A., Loureiro, A.A., 2013. How effective is to look at a vehicular network under a social perception?, in: 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), IEEE. pp. 154–159.
[30] Dastjerdi, A.V., Gupta, H., Calheiros, R.N., Ghosh, S.K., Buyya, R., 2016. Fog computing: Principles, architectures, and applications, in: Internet of things. Elsevier, pp. 61–75.
[31] Dedecker, J., Van Cutsem, T., Mostinckx, S., D’Hondt, T., De Meuter, W., 2006. Ambient-oriented programming in ambi- enttalk, in: European Conference on Object-Oriented Programming, Springer. pp. 230–254.
[32] Dillon, T., Wu, C., Chang, E., 2010. Cloud computing: issues and challenges, in: 2010 24th IEEE international conference on ad- vanced information networking and applications, Ieee. pp. 27–33.
[33] Din, S., Paul, A., Rehman, A., 2019. 5g-enabled hierarchical archi- tecture for software-defined intelligent transportation system. Com- puter Networks 150, 81–89.
[34] Dinh, H.T., Lee, C., Niyato, D., Wang, P., 2013. A survey of mobile cloud computing: architecture, applications, and approaches. Wire- less communications and mobile computing 13, 1587–1611.
[35] Duan, X., Liu, Y., Wang, X., 2017. Sdn enabled 5g-vanet: Adap- tive vehicle clustering and beamformed transmission for aggregated traffic. IEEE Communications Magazine 55, 120–127.
[36] Duan, X., Wang, X., Liu, Y., Zheng, K., 2016. Sdn enabled dual cluster head selection and adaptive clustering in 5g-vanet, in: 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), IEEE. pp. 1–5.
[37] Eichler, S., 2007. Performance evaluation of the ieee 802.11 p wave communication standard, in: 2007 IEEE 66th Vehicular Technology Conference, IEEE. pp. 2199–2203.
[38] Evans, D., 2011. The internet of things: How the next evolution of the internet is changing everything. CISCO white paper 1, 1–11.
[39] Feeley, M., Hutchinson, N., Ray, S., 2004. Realistic mobility for mobile ad hoc network simulation, in: International Conference on Ad-Hoc Networks and Wireless, Springer. pp. 324–329.
[40] Fire, M., Goldschmidt, R., Elovici, Y., 2014. Online social networks: threats and solutions. IEEE Communications Surveys & Tutorials 16, 2019–2036.
[41] Forman, G.H., Zahorjan, J., 1994. The challenges of mobile com- puting. Computer 27, 38–47.
[42] Gainaru, A., Dobre, C., Cristea, V., 2009. A realistic mobility model based on social networks for the simulation of vanets, in: VTC Spring 2009-IEEE 69th Vehicular Technology Conference, IEEE. pp. 1–5.
trends in cloud computing. Compusoft 8, 3146–3149.
[51] Han, S., Wang, X., Zhang, J.J., Cao, D., Wang, F.Y., 2018. Parallel vehicular networks: A cpss-based approach via multimodal big data
in iov. IEEE Internet of Things Journal 6, 1079–1089.
[52] Hao, P., Bai, Y., Zhang, X., Zhang, Y., 2017. Edgecourier: an edge- hosted personal service for low-bandwidth document synchroniza- tion in mobile cloud storage services, in: Proceedings of the Second
ACM/IEEE Symposium on Edge Computing, pp. 1–14.
[53] Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., Chen, S., 2016. Vehicular fog computing: A viewpoint of vehicles as the infrastructures. IEEE
Transactions on Vehicular Technology 65, 3860–3873.
[54] Hu, X., Wang, W., Leung, V.C., 2012. Vssa: A service-oriented ve- hicular social-networking platform for transportation efficiency, in: Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications, pp.31–38.
[55] Hu, Y.C., Patel, M., Sabella, D., Sprecher, N., Young, V., 2015. Mo-
bile edge computing—a key technology towards 5g. ETSI white
paper 11, 1–16.
[56] Huang, X., Yu, R., Kang, J., He, Y., Zhang, Y., 2017. Exploring
mobile edge computing for 5g-enabled software defined vehicular
networks. IEEE Wireless Communications 24, 55–63.
[57] Huang, Z., Ruj, S., Cavenaghi, M.A., Stojmenovic, M., Nayak, A., 2014. A social network approach to trust management in vanets.
Peer-to-Peer Networking and Applications 7, 229–242.
[58] Hung, S.C., Hsu, H., Lien, S.Y., Chen, K.C., 2015. Architecture har- monization between cloud radio access networks and fog networks.
IEEE Access 3, 3019–3034.
[59] Hussein, A., Elhajj, I.H., Chehab, A., Kayssi, A., 2017. Sdn
vanets in 5g: An architecture for resilient security services, in: 2017 Fourth International Conference on Software Defined Systems (SDS), IEEE. pp. 67–74.
[60] Iamnitchi, A., Blackburn, J., Kourtellis, N., 2012. The social hour- glass: An infrastructure for socially aware applications and services. IEEE Internet Computing 16, 13–23.
[61] Intharawijitr, K., Iida, K., Koga, H., 2016. Analysis of fog model considering computing and communication latency in 5g cellular networks, in: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), IEEE. pp. 1–4.
[62] Jalali, F., Hinton, K., Ayre, R., Alpcan, T., Tucker, R.S., 2016. Fog computing may help to save energy in cloud computing. IEEE Jour- nal on Selected Areas in Communications 34, 1728–1739.
[63] Karp, B., Kung, H.T., 2000. Gpsr: Greedy perimeter stateless rout- ing for wireless networks, in: Proceedings of the 6th annual inter- national conference on Mobile computing and networking, pp. 243–
[44] Ge, X., Cheng, H., Mao, G., Yang, Y., Tu, S., 2016. Vehicular com- munications for 5g cooperative small-cell networks. IEEE Transac- tions on Vehicular Technology 65, 7882–7894.
[45] Gerla, M., Tsai, J.T.C., 1995. Multicluster, mobile, multimedia radio network. Wireless networks 1, 255–265.
[46] Ghafoor, H., Gohar, N., Bulbul, R., 2012. Anchor-based connectiv- ity aware routing in vanets, in: 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, IEEE. pp. 1–6.
[47] Giang, N.K., Blackstock, M., Lea, R., Leung, V.C., 2015. Develop- ing iot applications in the fog: A distributed dataflow approach, in: 2015 5th International Conference on the Internet of Things (IOT), IEEE. pp. 155–162.
[48] Gong, H., Yu, L., Zhang, X., 2014. Social contribution-based rout- ing protocol for vehicular network with selfish nodes. International Journal of Distributed Sensor Networks 10, 753024.
[49] Gu, X., Tang, L., Han, J., 2014. A social-aware routing protocol based on fuzzy logic in vehicular ad hoc networks, in: 2014 In- ternational Workshop on High Mobility Wireless Communications, IEEE. pp. 12–16.
[50] Gupta, D., Gupta, K., Kumar, N., 2019. Emerging technologies and
[64] Kaushik, R.T., Bhandarkar, M., 2010. Greenhdfs: towards an energy-conserving, storage-efficient, hybrid hadoop compute clus- ter, in: Proceedings of the USENIX annual technical conference, p. 34.
[65] Khan, A.A., Abolhasan, M., Ni, W., 2018. 5g next generation vanets using sdn and fog computing framework, in: 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), IEEE. pp. 1–6.
[66] Khan, Z., Fan, P., Abbas, F., Chen, H., Fang, S., 2019. Two-level cluster based routing scheme for 5g v2x communication. IEEE Ac- cess 7, 16194–16205.
[67] Khattak, H.A., Farman, H., Jan, B., Din, I.U., 2019a. Toward in- tegrating vehicular clouds with iot for smart city services. IEEE Network 33, 65–71.
[68] Khattak, H.A., Islam, S.U., Din, I.U., Guizani, M., 2019b. Inte- grating fog computing with vanets: A consumer perspective. IEEE Communications Standards Magazine 3, 19–25.
[69] Konar, A., Chakraborty, I.G., Singh, S.J., Jain, L.C., Nagar, A.K., 2013. A deterministic improved q-learning for path planning of a mobile robot. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43, 1141–1153.
convergence of fog and vehicularsocial networks survey
[43] Gao, L., Luan, T.H., Liu, B., Zhou, W., Yu, S., 2017. Fog computing
and its applications in 5g, in: 5G Mobile Communications. Springer,
pp. 571–593. 254.
[70] Lai, Y., Yang, F., Su, J., Zhou, Q., Wang, T., Zhang, L., Xu, Y., 2018. Fog-based two-phase event monitoring and data gathering in vehicular sensor networks. Sensors 18, 82.
[71] Li, H., Dong, M., Ota, K., 2016a. Control plane optimization in software-defined vehicular ad hoc networks. IEEE Transactions on Vehicular Technology 65, 7895–7904.
[72] Li, H., Wang, B., Song, Y., Ramamritham, K., 2016b. Veshare: A d2d infrastructure for real-time social-enabled vehicle networks. IEEE Wireless Communications 23, 96–102.
[73] Liang, J., Wu, K., 2019. An extremely accurate time synchronization mechanism in fog-based vehicular ad-hoc network. IEEE Access 8, 253–268.
[74] Liu, J., Wan, J., Jia, D., Zeng, B., Li, D., Hsu, C.H., Chen, H., 2017a. High-efficiency urban traffic management in context-aware comput- ing and 5g communication. IEEE Communications Magazine 55, 34–40.
[75] Liu, K., Feng, L., Dai, P., Lee, V.C., Son, S.H., Cao, J., 2017b. Coding-assisted broadcast scheduling via memetic computing in sdn-based vehicular networks. IEEE Transactions on Intelligent Transportation Systems 19, 2420–2431.
[76] Liu, X., Li, Z., Li, W., Lu, S., Wang, X., Chen, D., 2012. Exploring social properties in vehicular ad hoc networks, in: Proceedings of the Fourth Asia-Pacific Symposium on Internetware, pp. 1–7.
[77] Lobo, F.L., Lima, M., Oliveira, H., El-Khatib, K., Harrington, J., 2017. Solve: A localization system framework for vanets using the cloud and fog computing, in: Proceedings of the 6th ACM Sym- posium on Development and Analysis of Intelligent Vehicular Net- works and Applications, pp. 17–22.
[78] Lu, N., Luan, T.H., Wang, M., Shen, X., Bai, F., 2012. Capacity and delay analysis for social-proximity urban vehicular networks, in: 2012 Proceedings IEEE INFOCOM, IEEE. pp. 1476–1484.
[79] Lu, N., Luan, T.H., Wang, M., Shen, X., Bai, F., 2013. Bounds of asymptotic performance limits of social-proximity vehicular net- works. IEEE/ACM transactions on networking 22, 812–825.
[80] Luan, T.H., Chen, C., Vinel, A., Cai, L., Chen, S., 2016. Guest editorial: Emerging technology for 5g enabled vehicular networks. IEEE Transactions on Vehicular Technology 65, 7827–7830.
[81] Luan, T.H., Gao, L., Li, Z., Xiang, Y., Wei, G., Sun, L., 2015. Fog computing: Focusing on mobile users at the edge. arXiv preprint arXiv:1502.01815 .
[82] Luo, G., Yuan, Q., Zhou, H., Cheng, N., Liu, Z., Yang, F., Shen, X.S., 2018. Cooperative vehicular content distribution in edge com- puting assisted 5g-vanet. China Communications 15, 1–17.
[83] Luo, G., Zhou, H., Cheng, N., Yuan, Q., Li, J., Yang, F., Shen, X.S., 2019. Software defined cooperative data sharing in edge computing assisted 5g-vanet. IEEE Transactions on Mobile Computing .
[84] Luo, J., He, Y., 2011. Geoquorum: Load balancing and energy effi- cient data access in wireless sensor networks, in: 2011 Proceedings IEEE INFOCOM, IEEE. pp. 616–620.
[85] Maglaras, L.A., Al-Bayatti, A.H., He, Y., Wagner, I., Janicke, H., 2016. Social internet of vehicles for smart cities. Journal of Sensor and Actuator Networks 5, 3.
[86] Maglaras, L.A., Katsaros, D., 2015. Social clustering of vehicles based on semi-markov processes. IEEE Transactions on Vehicular Technology 65, 318–332.
[87] Mahdi, M.A., Hasson, S.T., 2017. Grouping vehicles in vehicular social networks. Kurdistan Journal of Applied Research 2, 218–225.
[88] Mahmud, R., Koch, F.L., Buyya, R., 2018. Cloud-fog interoperabil- ity in iot-enabled healthcare solutions, in: Proceedings of the 19th international conference on distributed computing and networking,
pp. 1–10.
[89] Marquez-Barja, J.M., Ahmadi, H., Tornell, S.M., Calafate, C.T.,
Cano, J.C., Manzoni, P., DaSilva, L.A., 2014. Breaking the vehicu- lar wireless communications barriers: Vertical handover techniques for heterogeneous networks. IEEE Transactions on Vehicular Tech- nology 64, 5878–5890.
[90] McGeer, R., 2012. A safe, efficient update protocol for openflow networks, in: Proceedings of the first workshop on Hot topics in
software defined networks, pp. 61–66.
[91] Mell, P., Grance, T., et al., 2011. The nist definition of cloud com-
puting .
[92] Mohaien, A., Kune, D.F., Vasserman, E.Y., Kim, M., Kim, Y., 2013.
Secure encounter-based mobile social networks: Requirements, de- signs, and tradeoffs. IEEE Transactions on Dependable and Secure Computing 10, 380–393.
[93] Mumtaz, S., Huq, K.M.S., Ashraf, M.I., Rodriguez, J., Monteiro, V., Politis, C., 2015. Cognitive vehicular communication for 5g. IEEE Communications Magazine 53, 109–117.
[94] Naha, R.K., Garg, S., Georgakopoulos, D., Jayaraman, P.P., Gao, L., Xiang, Y., Ranjan, R., 2018. Fog computing: Survey of trends, architectures, requirements, and research directions. IEEE access 6, 47980–48009.
[95] Nanda, S., Goodman, D.J., Timor, U., 1991. Performance of prma: A packet voice protocol for cellular systems. IEEE transactions on vehicular technology 40, 584–598.
[96] Ni, M., Zhong, Z., Zhao, D., 2011. Mpbc: A mobility prediction- based clustering scheme for ad hoc networks. IEEE Transactions on Vehicular Technology 60, 4549–4559.
[97] Ning, Z., Hu, X., Chen, Z., Zhou, M., Hu, B., Cheng, J., Obaidat, M.S., 2017. A cooperative quality-aware service access system for social internet of vehicles. IEEE Internet of Things Journal 5, 2506– 2517.
[98] Ning, Z., Huang, J., Wang, X., 2019. Vehicular fog computing: En- abling real-time traffic management for smart cities. IEEE Wireless Communications 26, 87–93.
[99] Ning, Z., Wang, X., Huang, J., 2018. Mobile edge computing- enabled 5g vehicular networks: Toward the integration of commu- nication and computing. ieee vehicular technology magazine 14, 54–61.
[100] Nitti, M., Girau, R., Floris, A., Atzori, L., 2014. On adding the social dimension to the internet of vehicles: Friendship and middleware, in: 2014 IEEE international black sea conference on communications and networking (BlackSeaCom), IEEE. pp. 134–138.
[101] Niu, Z., Wu, Y., Gong, J., Yang, Z., 2010. Cell zooming for cost- efficient green cellular networks. IEEE communications magazine 48, 74–79.
[102] Nobre, J.C., de Souza, A.M., Rosário, D., Both, C., Villas, L.A., Cerqueira, E., Braun, T., Gerla, M., 2019. Vehicular software- defined networking and fog computing: Integration and design prin- ciples. Ad Hoc Networks 82, 172–181.
[103] Paranjothi, A., Khan, M.S., Atiquzzaman, M., 2018. Dfcv: a novel approach for message dissemination in connected vehicles using dy- namic fog, in: International Conference on Wired/Wireless Internet Communication, Springer. pp. 311–322.
[104] Paranjothi, A., Khan, M.S., Nijim, M., Challoo, R., 2016. Ma- vanet: Message authentication in vanet using social networks, in: 2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mo- bile Communication Conference (UEMCON), IEEE. pp. 1–8.
[105] Peng, M., Yan, S., Zhang, K., Wang, C., 2016. Fog-computing- based radio access networks: Issues and challenges. Ieee Network 30, 46–53.
[106] Preden, J.S., Tammemäe, K., Jantsch, A., Leier, M., Riid, A., Calis, E., 2015. The benefits of self-awareness and attention in fog and mist computing. Computer 48, 37–45.
[107] Qi, W., Song, Q., Wang, X., Guo, L., Ning, Z., 2018. Sdn-enabled social-aware clustering in 5g-vanet systems. IEEE Access 6, 28213– 28224.
[108] Rahim, A., Qiu, T., Ning, Z., Wang, J., Ullah, N., Tolba, A., Xia, F., 2019. Social acquaintance based routing in vehicular social net- works. Future Generation Computer Systems 93, 751–760.
[109] Ramachandran, K., Kokku, R., Zhang, H., Gruteser, M., 2008. Sym- phony: synchronous two-phase rate and power control in 802.11 wlans, in: Proceedings of the 6th international conference on Mo- bile systems, applications, and services, pp. 132–145.
[110] Raza, N., Jabbar, S., Han, J., Han, K., 2018. Social vehicle-to- everything (v2x) communication model for intelligent transportation
convergence of fog and vehicularsocial networks survey
Farimasadat Miri et al.: Preprint submitted to Elsevier
systems based on 5g scenario, in: Proceedings of the 2nd Interna- tional Conference on Future Networks and Distributed Systems, pp. 1–8.
Ren, J., Zhang, Y., Zhang, K., Shen, X., 2015. Exploiting mobile crowdsourcing for pervasive cloud services: challenges and solu- tions. IEEE Communications Magazine 53, 98–105.
Richardson, L., Ruby, S., 2008. RESTful web services. " O’Reilly Media, Inc.".
Rotsos, C., Sarrar, N., Uhlig, S., Sherwood, R., Moore, A.W., 2012. Oflops: An open framework for openflow switch evaluation, in: In- ternational Conference on Passive and Active Network Measure- ment, Springer. pp. 85–95.
Saito, Y., Kishiyama, Y., Benjebbour, A., Nakamura, T., Li, A., Higuchi, K., 2013. Non-orthogonal multiple access (noma) for cel- lular future radio access, in: 2013 IEEE 77th vehicular technology conference (VTC Spring), IEEE. pp. 1–5.
Sanaei, Z., Abolfazli, S., Gani, A., Buyya, R., 2013. Heterogeneity in mobile cloud computing: taxonomy and open challenges. IEEE Communications Surveys & Tutorials 16, 369–392.
Sarkar, S., Misra, S., 2016. Theoretical modelling of fog comput- ing: a green computing paradigm to support iot applications. Iet Networks 5, 23–29.
Satyanarayanan, M., 1996. Fundamental challenges in mobile com- puting, in: Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing, pp. 1–7.
Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N., 2009. The case for vm-based cloudlets in mobile computing. IEEE pervasive Computing 8, 14–23.
Schünemann, B., 2011. V2x simulation runtime infrastructure vsim- rti: An assessment tool to design smart traffic management systems. Computer Networks 55, 3189–3198.
Shah, S.S., Ali, M., Malik, A.W., Khan, M.A., Ravana, S.D., 2019. vfog: A vehicle-assisted computing framework for delay-sensitive applications in smart cities. IEEE Access 7, 34900–34909. Sharma, S., Purohit, G., 2014. Methods of tracking online commu- nity in social network, in: Social Networking. Springer, pp. 129– 146.
Shiraz, M., Gani, A., Khokhar, R.H., Buyya, R., 2012. A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing. IEEE Communications surveys & tutorials 15, 1294–1313.
Smailovic, V., Podobnik, V., 2012. Bfriend: Context-aware ad-hoc social networking for mobile users, in: 2012 Proceedings of the 35th International Convention MIPRO, IEEE. pp. 612–617.
Song, H., Fang, X., Yan, L., 2014. Handover scheme for 5g c/u plane split heterogeneous network in high-speed railway. IEEE Transac- tions on Vehicular Technology 63, 4633–4646.
Sornin, N., Luis, M., Eirich, T., Kramp, T., Hersent, O., 2015. Lo- rawan specification. LoRa alliance .
Sotomayor, B., Montero, R.S., Llorente, I.M., Foster, I., 2009. Vir- tual infrastructure management in private and hybrid clouds. IEEE Internet computing 13, 14–22.
Soua, A., Tohme, S., 2018. Multi-level sdn with vehicles as fog com- puting infrastructures: A new integrated architecture for 5g-vanets, in: 2018 21st Conference on Innovation in Clouds, Internet and Net- works and Workshops (ICIN), IEEE. pp. 1–8.
Srivastava, A., Gupta, D.J., et al., 2014. Social network analysis: Hardly easy, in: 2014 International Conference on Reliability Op- timization and Information Technology (ICROIT), IEEE. pp. 128– 135.
Storck, C.R., Duarte-Figueiredo, F., 2019. A 5g v2x ecosystem pro- viding internet of vehicles. Sensors 19, 550.
Sun, X., Ansari, N., 2016. Edgeiot: Mobile edge computing for the internet of things. IEEE Communications Magazine 54, 22–29. Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., Sabella, D., 2017. On multi-access edge computing: A survey of the emerging 5g network edge cloud architecture and orchestration. IEEE Com- munications Surveys & Tutorials 19, 1657–1681.
Taneja, M., Davy, A., 2016. Resource aware placement of data an- alytics platform in fog computing. Procedia Computer Science 97, 153–156.
Tisue, S., Wilensky, U., 2004. Netlogo: A simple environment for modeling complexity, in: International conference on complex sys- tems, Boston, MA. pp. 16–21.
Tomovic, S., Yoshigoe, K., Maljevic, I., Radusinovic, I., 2017. Software-defined fog network architecture for iot. Wireless Personal Communications 92, 181–196.
Vaquero, L.M., Rodero-Merino, L., 2014. Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM Computer Communication Review 44, 27–32. Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M., 2008. A break in the clouds: towards a cloud definition.
Vegni, A.M., Loscri, V., 2015. A survey on vehicular social net- works. IEEE Communications Surveys & Tutorials 17, 2397–2419. Vejlgaard, B., Lauridsen, M., Nguyen, H., Kovács, I.Z., Mogensen, P., Sorensen, M., 2017. Coverage and capacity analysis of sigfox, lora, gprs, and nb-iot, in: 2017 IEEE 85th vehicular technology con- ference (VTC Spring), IEEE. pp. 1–5.
Vladyko, A., Khakimov, A., Muthanna, A., Ateya, A.A., Kouch- eryavy, A., 2019. Distributed edge computing to assist ultra-low- latency vanet applications. Future Internet 11, 128.
Wang, G., Jiang, W., Wu, J., Xiong, Z., 2013. Fine-grained feature- based social influence evaluation in online social networks. IEEE Transactions on parallel and distributed systems 25, 2286–2296. Wang, M., Shan, H., Lu, R., Zhang, R., Shen, X., Bai, F., 2014. Real- time path planning based on hybrid-vanet-enhanced transportation system. IEEE Transactions on Vehicular Technology 64, 1664– 1678.
Watts, D.J., 2004. Small worlds: the dynamics of networks between order and randomness. Princeton university press.
Wu, C., Yoshinaga, T., Chen, X., Zhang, L., Ji, Y., 2018. Cluster- based content distribution integrating lte and ieee 802.11 p with fuzzy logic and q-learning. IEEE Computational Intelligence Mag- azine 13, 41–50.
Xia, F., Liu, L., Li, J., Ahmed, A.M., Yang, L.T., Ma, J., 2014. Bee- info: Interest-based forwarding using artificial bee colony for so- cially aware networking. IEEE Transactions on Vehicular Technol- ogy 64, 1188–1200.
Xiao, K., Liu, K., Wang, J., Yang, Y., Feng, L., Cao, J., Lee, V., 2019. A fog computing paradigm for efficient information services in vanet, in: 2019 IEEE Wireless Communications and Networking Conference (WCNC), IEEE. pp. 1–7.
Xu, S., Li, M., Chen, Y., Shu, L., Gu, X., 2013. A cooperation scheme based on reputation for opportunistic networks, in: 2013 In- ternational Conference on Computing, Management and Telecom- munications (ComManTel), IEEE. pp. 289–294.
Yacoub, M.D., Bautista, J.V., de Rezende Guedes, L.G., 1999. On higher order statistics of the nakagami-m distribution. IEEE Trans- actions on Vehicular Technology 48, 790–794.
Yasar, A.U.H., Mahmud, N., Preuveneers, D., Luyten, K., Coninx, K., Berbers, Y., 2010. Where people and cars meet: social inter- actions to improve information sharing in large scale vehicular net- works, in: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1188–1194.
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I., et al., 2010. Spark: Cluster computing with working sets. HotCloud 10, 95.
Zhang, T., . Fog computing brings new business opportunities and disruptions.
Zhou, H., Wu, J., Zhao, H., Tang, S., Chen, C., Chen, J., 2014. Incentive-driven and freshness-aware content dissemination in self- ish opportunistic mobile networks. IEEE Transactions on Parallel and Distributed Systems 26, 2493–2505.
Zia, K., Muhammad, A., Khalid, A., Din, A., Ferscha, A., 2019. Towards exploration of social in social internet of vehicles using an agent-based simulation. Complexity 2019.

Analyzing the performance of distributed conflict resolution among autonomous vehicles

[1] D. V. Dimarogonas, K. Kyriakopoulos, Inventory of Decentralized Conflict Detection and Resolution Systems in Air Traffic, Tech. rep., NTUA, deliverable D6.1 HYBRIDGE Project, FP5 European Com- mission (2003).
[2] L. Bakule, Decentralized control: An overview, Annual Reviews in Control 32 (1) (2008) 87–98. doi:10.1016/j.arcontrol.2008.03.004.
[3] Final Report on Free Flight Implementation, Tech. rep., Radio Technical Commission for Aeronautics, RTCA Task Force 3 (26-October-1995).
[4] J. M. Hoekstra, R. C. J. Ruigrok, R. N. H. W. Van Gent, Free flight in a crowded airspace?, Progress in Astronautics and Aeronautics 193 (June) (2001) 533–546.
[5] M. Vilaplana Ruiz, Co-operative conflict resolution in autonomous aircraft operations using a multi- agent approach, Ph.D. thesis, University of Glasgow (2002).
[6] H. Moniz, A. Tedeschi, N. F. Neves, M. Correia, A Distributed Systems Approach to Airborne Self- Separation, in: L. Weigang, A. de Barros, I. Romani de Oliveira (Eds.), Computational Models, Software Engineering, and Advanced Technologies in Air Transportation: Next Generation Applications, IGI Global, 2009, pp. 215–236. doi:10.4018/978-1-60566-800-0.ch011.
[7] H. A. P. Blom, G. J. Bakker, Safety Evaluation of Advanced Self-Separation Under Very High En Route Traffic Demand, Journal of Aerospace Information Systems 12 (6) (2015) 413–427. doi:10.2514/1.I010243.
[8] S. L. Waslander, K. Roy, R. Johari, C. J. Tomlin, Lump-Sum Markets for Air Traffic Flow Control With Competitive Airlines, Proceedings of the IEEE 96 (12) (2008) 2113–2130. doi:10.1109/JPROC.2008.2006197.
[9] L. Castelli, R. Pesenti, A. Ranieri, The design of a market mechanism to allocate air traffic flow man- agement slots, Transportation Research Part C: Emerging Technologies 19 (5) (2011) 931 – 943, Freight Transportation and Logistics (selected papers from ODYSSEUS 2009 - the 4th International Workshop on Freight Transportation and Logistics). doi:10.1016/j.trc.2010.06.003.
[10] J. Schummer, R. V. Vohra, Assignment of arrival slots, American Economic Journal: Microeconomics 5 (2) (2013) 164–185. doi:10.1257/mic.5.2.164.
[11] L. Cruciol, J.-P. Clarke, L. Weigang, Trajectory option set planning optimization under uncertainty in CTOP, in: Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on, 2015, pp. 2084–2089. doi:10.1109/ITSC.2015.337.
[12] A. G. Zellweger, G. L. Donohue, Collaborative decision making, in: Air Transportation Systems Engi- neering, Progress in Astronautics and Aeronautics, American Institute of Aeronautics and Astronautics, 2001, pp. 159–159.
[13] H. Blom, G. Bakker, Emergent Behaviour of Simulation Model; E.02. 39 EMERGIA D2.2 Report, Tech. rep., SESAR Joint Undertaking (2014).
[14] T. Roughgarden, E ́. Tardos, Bounding the inefficiency of equilibria in nonatomic congestion games, Games and Economic Behavior 47 (2002) 389–403.
[15] J. R. Correa, A. S. Schulz, N. E. Stier-Moses, On the inefficiency of equilibria in congestion games, in: Integer Programming and Combinatorial Optimization, Springer, 2005, pp. 167–181.
[16] C. F. Daganzo, The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory, Transportation Research Part B 28 (4) (1994) 269–287. doi:10.1016/0191- 2615(94)90002-7.
[17] A. Schadschneider, D. Chowdhury, K. Nishinari, Stochastic transport in complex systems: From molecules to vehicles, Elsevier, 2010.
[18] J. Tanimoto, Fundamentals of Evolutionary Game Theory and its Applications, Springer Japan, 2015. doi:10.1007/978-4-431-54962-8.
[19] A. Bayen, P. Grieder, G. Meyer, C. J. Tomlin, Langrangian delay predictive model for sector-based air traffic flow, Journal of Guidance, Control, and Dynamics 28 (5) (2005) 1015–1026. doi:10.2514/1.15242.
[20] D. Sun, A. M. Bayen, Multicommodity Eulerian-Lagrangian Large-Capacity Cell Transmission Model for En Route Traffic, Journal of Guidance, Control, and Dynamics 31 (3) (2008) 616–628. doi:10.2514/1.31717.
[21] M. Balin, ARMD Strategic Thrust 6: Assured Autonomy for Aviation Transformation, Vision and Roadmap, http://www.aeronautics.nasa.gov/pdf/ARMD-SIP-Thrust-6-508.pdf, accessed 07- September-2016.
[22] F. Dunke, Online optimization with lookahead, Ph.D. thesis, Karlsruher Institut fur Technologie (KIT) (2014).
[23] D. Gale, L. S. Shapley, College Admissions and the Stability of Marriage, American Mathematical Monthly 69 (1962) 9–14.
[24] P. Milgrom, I. Segal, Deferred-acceptance auctions and radio spectrum reallocation, in: Proceedings of the fifteenth ACM conference on Economics and computation - EC ’14, ACM Press, New York, New York, USA, 2014, pp. 185–186. doi:10.1145/2600057.2602834.
[25] G. J. J. Ruijgrok, Elements of airplane performance, VSSD, 2009.
[26] R. B. Myerson, Game Theory: Analysis of Conflict, 1997.
[27] F. Ciliberto, E. Tamer, Market Structure and Multiple Equilibria in Airline Markets, Econometrica 77 (6) (2009) 1791–1828. doi:10.3982/ECTA5368.
[28] D. R. Jones, C. D. Perttunen, B. E. Stuckman, Lipschitzian optimization without the Lipschitz constant, Journal of Optimization Theory and Applications 79 (1) (1993) 157–181. doi:10.1007/BF00941892.
[29] NLopt home page, http://ab-initio.mit.edu/wiki/index.php/NLopt, accessed 14-November-2015.
[30] T. Roughgarden, E ́. Tardos, Introduction to the Inefficiency of Equilibria, in: N. Nisan, T. Roughgarden, E ́. Tardos, V. V. Vazirani (Eds.), Algorithmic Game Theory, Cambridge University Press, New York, 2007, pp. 443–459.
[31] C. Gini, Measurement of inequality of incomes, The Economic Journal 31 (121) (1921) 124–126. URL http://www.jstor.org/stable/2223319
[32] A. Sen, On Economic Inequality, 2nd Edition, Oxford University Press, Oxford, 1977.
[33] S. Ramchurn, C. Sierra, L. Godo, N. R. Jennings, Devising a trust model for multi-agent interactions using confidence and reputation, International Journal of Applied Artificial Intelligence 18 (9-10) (2004) 833–852. doi:10.1080/0883951049050904509045.
[34] S. Ramchurn, T. Huynh, N. R. Jennings, Trust in Multiagent Systems, The Knowledge Engineering Review 19 (1) (2004) 1–25. doi:10.1017/S0269888904000116.
[35] D. Bertsimas, S. Stock-Patterson, The air traffic flow management problem with enroute capacities, Oper. Res. 46 (3) (1998) 406–422. doi:10.1287/opre.46.3.406.
[36] M. C. R. Mur ̧ca, C. Mu ̈ller, Control-based optimization approach for aircraft scheduling in a terminal area with alternative arrival routes, Transportation Research Part E: Logistics and Transportation Review 73 (2015) 96 – 113. doi:10.1016/j.tre.2014.11.004.
[37] J. L. Castro Fortes, D. A. Pamplona, C. Mu ̈ller, The use of integer programming as a planning tool for ATFM – INFRAERO’s airport network as a case study, Journal of the Brazilian Air Transportation Research Society (JBATS) 11 (1) (2015) 1–15.
[38] F. Asadi, A. Richards, Self-Organized Model Predictive Control for Air Traffic Management, in: 5th International Conference on Application and Theory of Automation in Command and Control Systems, Toulouse, France, September 2015.
[39] K. Amouris, Space-time division multiple access (STDMA) and coordinated, power-aware MACA for mobile ad hoc networks, in: Global Telecommunications Conference, 2001. GLOBECOM ’01. IEEE, Vol. 5, 2001, pp. 2890–2895 vol.5. doi:10.1109/GLOCOM.2001.965957.
[40] C. E. Garc ́ıa, D. M. Prett, M. Morari, Model predictive control: Theory and practice —- A survey, Automatica 25 (3) (1989) 335–348. doi:10.1016/0005-1098(89)90002-2.
[41] G. Chaloulos, P. Hokayem, J. Lygeros, Distributed hierarchical MPC for conflict resolution in air traffic control, in: American Control Conference (ACC), 2010, 2010, pp. 3945–3950. doi:10.1109/ACC.2010.5530640.
[42] E. Siva, J. Maciejowski, G. Chaloulos, J. Lygeros, G. Roussos, K. Kyriakopoulos, iFly, Work Package WP5, D5.4 Final Report Including Validation, Tech. rep., NTUA (October 2011).
[43] ́I. Romani de Oliveira, Atribui ̧ca ̃o de prioridades em separa ̧ca ̃o autˆonoma de tr ́afego a ́ereo, in: XI Simp ́osio de Transporte A ́ereo (SITRAER), Brasilia, 2012.
[44] D. V. Dimarogonas, E. Frazzoli, Analysis of decentralized potential field based multi-agent navigation via primal-dual Lyapunov theory, Proceedings of the IEEE Conference on Decision and Control (2010) 1215–1220doi:10.1109/CDC.2010.5717432.
[45] M. Rinaldi, C. M. Tamp`ere, An extended coordinate descent method for distributed anticipa- tory network traffic control, Transportation Research Part B: Methodological 80 (2015) 107–131. doi:10.1016/j.trb.2015.06.017.
[46] R. Hol􏰀yst, Challenges in thermodynamics: Irreversible processes, nonextensive entropies, and systems without equilibrium states, Pure and Applied Chemistry 81 (10) (2009) 1719–1726. doi:10.1351/PAC- CON-08-07-13.
[47] E. H. Lieb, J. Yngvason, The entropy concept for non-equilibrium states., Proceedings. Mathematical, physical, and engineering sciences / the Royal Society 469 (2158) (2013) 20130408. arXiv:1305.3912, doi:10.1098/rspa.2013.0408.
[48] D. Boyce, B. Janson, A discrete transportation network design problem with combined trip distri- bution and assignment, Transportation Research Part B: Methodological 14 (1-2) (1980) 147–154. doi:10.1016/0191-2615(80)90040-5.
[49] S. E. Christodolou, Traffic modeling and college-bus routing using entropy maximization, Journal of Transportation Engineering 136 (2) (2010) 102–109. doi:10.1061/(ASCE)TE.1943-5436.0000067.

TOWARDS FORMALIZATION AND MONITORING OF MICROSCOPIC TRAFFIC PARAMETERS USING TEMPORAL LOGIC

[1] A. M. de Souza, C. A. Brennand, R. S. Yokoyama, E. A. Donato, E. R. Madeira, and L. A. Villas, “Traffic management systems: A classification, review, challenges, and future perspectives,” International Journal of Distributed Sensor Networks, vol. 13, no. 4, p. 1550147716683612, Apr 2017.
[2] M. A. Khan, W. Ectors, T. Bellemans, D. Janssens, and G. Wets, “Unmanned aerial vehicle–based traffic analysis: Methodological framework for automated multivehicle trajectory extraction,” Transportation research record, vol. 2626, no. 1, pp. 25–33, 2017.
[3] S. Lu, V. L. Knoop, and M. Keyvan-Ekbatani, “Using taxi gps data for macroscopic traffic monitoring in large scale urban networks: calibration and mfd derivation,” Transportation research procedia, vol. 34, pp. 243–250, 2018.
[4] A. Sandt and H. Al-Deek, “Estimating fatality and injury savings because of deployment of advanced wrong-way driving countermeasures on a toll road network,” Transportation Research Record, p. 0361198120986573, 2021.
[5] N. K. Jain, R. K. Saini, and P. Mittal, “A review on traffic monitoring system techniques,” in Soft Computing: Theories and Applications, ser. Advances in Intelligent Systems and Computing, K. Ray, T. K. Sharma, S. Rawat, R. K. Saini, and A. Bandyopadhyay, Eds. Springer, 2019, p. 569–577.
[6] S. Mosier, E. Hohman, N. Trivedi, K. L. Jackson, and T. D. Rosner, “Vehicle-to-vehicle messages as an alternative to floating car data for traffic monitoring,” in Transportation Research Board 97th Annual Meeting, 2018. [Online]. Available: https://trid.trb.org/view/1494425
[7] A. Platzer, “Verification of cyberphysical transportation systems,” IEEE Intelligent Systems, vol. 24, no. 4, p. 10–13, Jul 2009.
Towards formalization and monitoring of microscopic traffic parameters using temporal logicA PREPRINT
[8] A. Fantechi, F. Flammini, and S. Gnesi, “Formal methods for intelligent transportation systems,” in Leveraging Applications of Formal Methods, Verification and Validation. Applications and Case Studies, ser. Lecture Notes in Computer Science, T. Margaria and B. Steffen, Eds. Springer, 2012, p. 187–189.
[9] M. Ma, J. A. Stankovic, and L. Feng, “Runtime monitoring of safety and performance requirements in smart cities,” in Proceedings of the 1st ACM Workshop on the Internet of Safe Things - SafeThings’17. ACM Press, 2017, p. 44–50. [Online]. Available: http://dl.acm.org/citation.cfm?doid=3137003.3137005
[10] S. Coogan, M. Arcak, and C. Belta, “Formal methods for control of traffic flow: Automated control synthesis from finite-state transition models,” IEEE Control Systems Magazine, vol. 37, no. 2, p. 109–128, Apr 2017.
[11] A. Rashid, M. Umair, O. Hasan, and M. H. Zaki, “Toward the formalization of macroscopic models of traffic flow using higher-order-logic theorem proving,” IEEE Access, vol. 8, p. 27291–27307, 2020.
[12] S. Shalev-Shwartz, S. Shammah, and A. Shashua, “On a formal model of safe and scalable self-driving cars,” arXiv:1708.06374 [cs, stat], Oct 2018, arXiv: 1708.06374. [Online]. Available: http://arxiv.org/abs/1708.06374
[13] M. H. ter Beek, S. Gnesi, and A. Knapp, “Formal methods for transport systems,” International Journal on Software Tools for Technology Transfer, vol. 20, no. 3, p. 237–241, Jun 2018.
[14] J. Mao and L. Chen, “Runtime monitoring for cyber-physical systems: A case study of cooperative adaptive cruise control,” in 2012 Second International Conference on Intelligent System Design and Engineering Application, Jan 2012, p. 509–515.
[15] S. Mitsch, S. M. Loos, and A. Platzer, “Towards formal verification of freeway traffic control,” in 2012 IEEE/ACM Third International Conference on Cyber-Physical Systems, Apr 2012, p. 171–180.
[16] S. Coogan and M. Arcak, “Freeway traffic control from linear temporal logic specifications,” in 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), Apr 2014, p. 36–47.
[17] E. S. Kim, M. Arcak, and S. A. Seshia, “Compositional controller synthesis for vehicular traffic networks,” in 2015 54th IEEE Conference on Decision and Control (CDC), Dec 2015, p. 6165–6171.
[18] A. Müller, S. Mitsch, and A. Platzer, “Verified traffic networks: Component-based verification of cyber-physical flow systems,” in 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Sep 2015, p. 757–764.
[19] S. Coogan, E. A. Gol, M. Arcak, and C. Belta, “Traffic network control from temporal logic specifications,” IEEE Transactions on Control of Network Systems, vol. 3, no. 2, p. 162–172, Jun 2016.
[20] ——, “Controlling a network of signalized intersections from temporal logical specifications,” in 2015 American Control Conference (ACC), Jul 2015, p. 3919–3924.
[21] N. Mehr, D. Sadigh, and R. Horowitz, “Probabilistic controller synthesis for freeway traffic networks,” in 2016 American Control Conference (ACC), Jul 2016, p. 880–880.
[22] S. Sadraddini, J. Rudan, and C. Belta, “Formal synthesis of distributed optimal traffic control policies,” ser. ICCPS ’17, Apr 2017, p. 15–24. [Online]. Available: https://doi.org/10.1145/3055004.3055011
[23] M. Ma, E. Bartocci, E. Lifland, J. Stankovic, and L. Feng, “Sastl: Spatial aggregation signal temporal logic for runtime monitoring in smart cities,” in 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), Apr 2020, p. 51–62.
[24] E. Bartocci, J. Deshmukh, A. Donze, G. Fainekos, O. Maler, D. Nickovic, and S. Sankaranarayanan, Specification- Based Monitoring of Cyber-Physical Systems: A Survey on Theory, Tools and Applications. Springer International Publishing, 2018, p. 135–175. [Online]. Available: https://doi.org/10.1007/978-3-319-75632-5_5
[25] O. Maler and D. Nickovic, “Monitoring temporal properties of continuous signals,” in Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems, ser. Lecture Notes in Computer Science, Y. Lakhnech and S. Yovine, Eds. Springer, 2004, p. 152–166.
[26] A. Donzé and O. Maler, “Robust satisfaction of temporal logic over real-valued signals,” in Formal Modeling and Analysis of Timed Systems, ser. Lecture Notes in Computer Science, K. Chatterjee and T. A. Henzinger, Eds. Springer, 2010, p. 92–106.
[27] N. Mehr, D. Sadigh, R. Horowitz, S. S. Sastry, and S. A. Seshia, “Stochastic predictive freeway ramp metering from signal temporal logic specifications,” in 2017 American Control Conference (ACC). IEEE, May 2017, p. 4884–4889. [Online]. Available: http://ieeexplore.ieee.org/document/7963711/
[28] M. Gueriau and I. Dusparic, “Quantifying the impact of connected and autonomous vehicles on traffic efficiency and safety in mixed traffic,” in 23rd IEEE International Conference on Intelligent Transportation Systems, 2020.
Towards formalization and monitoring of microscopic traffic parameters using temporal logicA PREPRINT
[29] C. Sommer, R. German, and F. Dressler, “Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis,” IEEE Transactions on Mobile Computing (TMC), vol. 10, no. 1, pp. 3–15, January 2011.
[30] M. H. Zaki, T. Sayed, and K. Shaaban, “Use of drivers’ jerk profiles in computer vision–based traffic safety evaluations,” Transportation Research Record, vol. 2434, no. 1, pp. 103–112, 2014.
[31] O. Bagdadi and A. Varhelyi, “Jerky driving—an indicator of accident proneness?” Accident Analysis & Prevention, vol. 43, no. 44, p. 1359–1363, Jul 2011.
[32] M. Zhu, X. Wang, and J. Hu, “Impact on car following behavior of a forward collision warning system with headway monitoring,” Transportation research part C: emerging technologies, vol. 111, pp. 226–244, 2020.
[33] A.Rizaldi,F.Immler,andM.Althoff,“Aformallyverifiedcheckerofthesafedistancetrafficrulesforautonomous vehicles,” in NASA Formal Methods, ser. Lecture Notes in Computer Science, S. Rayadurgam and O. Tkachuk,
Eds. Springer International Publishing, 2016, p. 175–190.
[34] P. Fernandes and U. Nunes, “Platooning with ivc-enabled autonomous vehicles: Strategies to mitigate communica- tion delays, improve safety and traffic flow,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 1, p. 91–106, Mar 2012.
[35] A. Kesting, Doctoral Thesis Microscopic Modeling of Human and Automated Driving: Towards Traffic-Adaptive Cruise Control, 2008.
[36] Z. Islam and M. Abdel-Aty, “Real-time vehicle trajectory estimation based on lane change detection using smartphone sensors,” Transportation Research Record, p. 0361198121990681, 2021.
[37] K. Kang and H. A. Rakha, “Modeling driver merging behavior: a repeated game theoretical approach,” Trans- portation research record, vol. 2672, no. 20, pp. 144–153, 2018.

Simulation-based Evaluation of a Synchronous Transaction Model for Time-Sensitive Software-Defined Networks

[1] J. Cui, S. Zhou, H. Zhong, Y. Xu, and K. Sha. Transaction-Based Flow Rule Conflict Detection and Resolution in SDN. In 2018 27th International Conference on Computer Communication and Networks (ICCCN), pages 1–9, July 2018.
[2] M. Curic, Z. Despotovic, A. Hecker, and G. Carle. Transactional Network Updates in SDN. In 2018 European Conference on Networks and Communications (EuCNC), pages 203–208, 2018.
[3] R. Enns, M. Bjorklund, J. Schoenwaelder, and A. Bierman. Network Configuration Protocol
(NETCONF). RFC 6241, IETF, June 2011.
[4] Timo Ha ̈ckel, Philipp Meyer, Franz Korf, and Thomas C. Schmidt. SDN4CoRE: A Simulation
Model for Software-Defined Networking for Communication over Real-Time Ethernet. In Meyo Zongo, Antonio Virdis, Vladimir Vesely, Zeynep Vatandas, Asanga Udugama, Koojana Kula- dinithi, Michael Kirsche, and Anna Fo ̈rster, editors, Proc. of the 6th Int. OMNeT++ Community Summit 2019, volume 66 of EPiC Series in Computing, pages 24–31. EasyChair, December 2019.
[5] Timo Ha ̈ckel, Philipp Meyer, Franz Korf, and Thomas C. Schmidt. Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication. In 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), pages 1–5, Piscataway, NJ, USA, April 2019. IEEE Press.
[6] IEEE 802.1 Working Group. IEEE Standard for Local and Metropolitan Area Network–Bridges and Bridged Networks. Std. 802.1Q-2018 (Revision of IEEE Std 802.1Q-2014), IEEE, July 2018.
Synchronous Transaction Model for TSSDNs Haugg et al.
[7] Dominik Klein and Michael Jarschel. An OpenFlow extension for the OMNeT++ INET frame- work. In Proceedings of the 6th International ICST Conference on Simulation Tools and Tech- niques, SimuTools ’13, pages 322–329, Brussels, BEL, 2013. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).
[8] D. Kreutz, F. M. V. Ramos, P. E. Ver ́ıssimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig. Software-Defined Networking: A Comprehensive Survey. Proceedings of the IEEE, 103(1):14–76, January 2015.
[9] Nick McKeown, Tom Anderson, Hari Balakrishnan, Guru Parulkar, Larry Peterson, Jennifer Rex- ford, Scott Shenker, and Jonathan Turner. OpenFlow: Enabling Innovation in Campus Networks. ACM SIGCOMM Computer Communication Review, 38(2):69–74, 2008.
[10] Philipp Meyer, Franz Korf, Till Steinbach, and Thomas C Schmidt. Simulation of Mixed Critical In-vehicular Networks. In Recent Advances in Network Simulation, pages 317–345. Springer, 2019.
[11] Maarten van Steen and Andrew S. Tanenbaum. Distributed systems. Pearson Education, 2017.

参考資料　ベクタージャパン

「はじめての」シリーズ　 ベクタージャパン　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/2e41634f6e21a3cf74eb

必須

「はじめてのCAN/CANFD 」 ベクタージャパン　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/1fee270be00ef90ca4ec

はじめてのAUTOSAR(classic platform)　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/696ad320f76f284664d7

「はじめての診断」 ベクタージャパン　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/36b5ab0fb163f2adea07

「はじめてのCANoe」 ベクタージャパン ＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/ec4eaafd381656e24117

「はじめての車載Ethernet 」 ベクタージャパン　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/97a6d755af9a2790e972

「はじめてのCANoe.Ethernet」 ベクタージャパン ＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/898a2deb94452c6d690b

推奨

「はじめてのAUTOSAR SecOC」 ベクタージャパン　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/c6513662968e97d4f65e

「はじめてのSOME/IP」 ベクタージャパン　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/2a7a5d1c797fd13b060f

「はじめてのXCP」 ベクタージャパン　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/7ec2e31efb99d39e900c

「はじめてのCAPL」 (Communication Access Programming Language) ベクタージャパン　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/990383db16051739ca12

「はじめてのLIN」ベクタージャパン　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/e7687a80c965b486ba0d

「はじめてのJ1939」 ベクタージャパン　＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/983c69c8f33ef24b7a3d

「はじめてのCANape」 ベクタージャパン ＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/ae44c217b2db1e1e7ec1

参考

「はじめてのCANalyzer」 ベクタージャパン ＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/83d6b8e494988c1da76e

「はじめてのFlexRay 」 ベクタージャパン＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/e6e97354734e5daaec8b

「はじめての単体試験」 ベクタージャパン＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/08a886f18de3e0d6179a

「はじめてのvectorCAST」 ベクタージャパン＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/cc30b72496aeaae53159

「はじめてのvTESTstudio」 ベクタージャパン ＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/c1cc5b091bfd76e72128

自己参照

詳解 車載ネットワーク CAN、CAN FD、LIN、CXPI、Ethernetの仕組みと設計のために
https://qiita.com/kaizen_nagoya/items/44a9e6b0f5363b4a5b35

詳解 車載ネットワーク CAN, CAN FD, LIN, CXPI, Ethernetの仕組みと設計のために(2)参考文献 ＜エンジニア夏休み企画>【読書感想文】
https://qiita.com/kaizen_nagoya/items/e156cbdd5fce9263776e

AUTOSAR 「完全に理解した」
https://qiita.com/kaizen_nagoya/items/51983798ad7902b33cb1

A big wrapping cloth with the miniature garden
https://qiita.com/kaizen_nagoya/items/96411f20632e7f3ff73a

Network Designed Vehicle
https://qiita.com/kaizen_nagoya/items/a696f8a8cbd141215266

Network Designed Vehicle v.s. Software Defined Vehicle
https://qiita.com/kaizen_nagoya/items/978403a5c3905ef0478a

AUTOSAR文書を読む前に知っているとよいこと。
https://qiita.com/kaizen_nagoya/items/87685d872431751b2d0c

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

権利と義務の前に。仮説（147）
https://qiita.com/kaizen_nagoya/items/47d4e992d0fd340403fd

一覧

物理記事　上位100
https://qiita.com/kaizen_nagoya/items/66e90fe31fbe3facc6ff

量子(0) 計算機, 量子力学
https://qiita.com/kaizen_nagoya/items/1cd954cb0eed92879fd4

数学関連記事１００
https://qiita.com/kaizen_nagoya/items/d8dadb49a6397e854c6d

言語・文学記事　１００
https://qiita.com/kaizen_nagoya/items/42d58d5ef7fb53c407d6

医工連携関連記事一覧
https://qiita.com/kaizen_nagoya/items/6ab51c12ba51bc260a82

自動車　記事　１００
https://qiita.com/kaizen_nagoya/items/f7f0b9ab36569ad409c5

通信記事１００
https://qiita.com/kaizen_nagoya/items/1d67de5e1cd207b05ef7

日本語（０）一欄
https://qiita.com/kaizen_nagoya/items/7498dcfa3a9ba7fd1e68

英語(0) 一覧
https://qiita.com/kaizen_nagoya/items/680e3f5cbf9430486c7d

転職(0)一覧
https://qiita.com/kaizen_nagoya/items/f77520d378d33451d6fe

仮説（0）一覧（目標100現在40）
https://qiita.com/kaizen_nagoya/items/f000506fe1837b3590df

Qiita(0)Qiita関連記事一覧（自分）
https://qiita.com/kaizen_nagoya/items/58db5fbf036b28e9dfa6

鉄道（０）鉄道のシステム考察はてっちゃんがてつだってくれる
https://qiita.com/kaizen_nagoya/items/26bda595f341a27901a0

安全（0）安全工学シンポジウムに向けて: 21
https://qiita.com/kaizen_nagoya/items/c5d78f3def8195cb2409

一覧の一覧( The directory of directories of mine.) Qiita(100)
https://qiita.com/kaizen_nagoya/items/7eb0e006543886138f39

Ethernet 記事一覧　Ethernet(0)
https://qiita.com/kaizen_nagoya/items/88d35e99f74aefc98794

Wireshark 一覧 wireshark(0)、Ethernet(48)
https://qiita.com/kaizen_nagoya/items/fbed841f61875c4731d0

線網（Wi-Fi）空中線(antenna)(0) 記事一覧(118/300目標)
https://qiita.com/kaizen_nagoya/items/5e5464ac2b24bd4cd001

OSEK OS設計の基礎　OSEK(100)
https://qiita.com/kaizen_nagoya/items/7528a22a14242d2d58a3

Error一覧 error(0)
https://qiita.com/kaizen_nagoya/items/48b6cbc8d68eae2c42b8

プログラマによる、プログラマのための、統計(0)と確率のプログラミングとその後
https://qiita.com/kaizen_nagoya/items/6e9897eb641268766909

官公庁・学校・公的団体（NPOを含む）システムの課題、官（０）
https://qiita.com/kaizen_nagoya/items/04ee6eaf7ec13d3af4c3

「はじめての」シリーズ　 ベクタージャパン　
https://qiita.com/kaizen_nagoya/items/2e41634f6e21a3cf74eb

AUTOSAR(0)Qiita記事一覧, OSEK(75)
https://qiita.com/kaizen_nagoya/items/89c07961b59a8754c869

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

LaTeX(0) 一覧　
https://qiita.com/kaizen_nagoya/items/e3f7dafacab58c499792

自動制御、制御工学一覧（０）
https://qiita.com/kaizen_nagoya/items/7767a4e19a6ae1479e6b

Rust(0) 一覧　
https://qiita.com/kaizen_nagoya/items/5e8bb080ba6ca0281927

小川清最終講義、最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53

＜この記事は個人の過去の経験に基づく個人の感想です。現在所属する組織、業務とは関係がありません。＞
This article is an individual impression based on the individual's experience. It has nothing to do with the organization or business to which I currently belong.

文書履歴(document history)

ver. 0.01 初稿 　20220713
ver. 0.02 add URL 20240328

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Thank you very much for reading to the last sentence.

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