[en] Emerging vehicular applications with strict latency and reliability requirements pose high computing requirements, and current vehicles’ computational resources are not adequate to meet these demands. In this scenario, vehicles can get help to process tasks from other resource-rich computing platforms, including nearby vehicles, fixed edge servers, and remote cloud servers. Nonetheless, different vehicular communication network (VCN) modes need to be utilized to access these computing resources, improving applications and networks’ performance and quality of service (QoS). In this paper, we present a comprehensive survey on the vehicular task offloading techniques under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). For the task/computation offloading, we present the classification of methods under the V2V, V2I, and V2X communication domains. Besides, the task/computation offloading categories are each sub-categorized according to their schemes’ objectives. Furthermore, the literature on vehicular task offloading is elaborated, compared, and analyzed from the perspectives of approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Ahmed, Manzoor
Raza, Salman
Mirza, Muhammad Ayzed
Aziz, Abdul
Khan, Manzzor Ahmed
Khan, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Li, Jianbo
Han, Zhu
External co-authors :
yes
Language :
English
Title :
A Survey on Vehicular Task Offloading: Classification, Issues, and Challenges
Alternative titles :
[en] A Survey on Vehicular Task Offloading: Classification, Issues, and Challenges
Publication date :
July 2022
Journal title :
Journal of King Saud University - Computer and Information Sciences
3GPP22.185, July 2020. Technical specification group services and system aspects; service requirements for v2x services; stage 1 (v16.0.0, release 16). 3GPP.
3GPP22.186, June 2019. Technical specification group services and system aspects; enhancement of 3gpp support for v2x scenarios; stage 1 (v16.2.0, rel. 16). 3GPP.
3GPP22.261, T., 2019. Service requirements for next generation new services and markets.
3GPP22.885, Dec. 2015. Technical specification group services and system aspects; study on lte support for vehicle to everything (v2x) services (v14.0.0, release 14). 3GPP.
3GPP22.886, Dec. 2018. Technical specification group services and system aspects; study on enhancement of 3gpp support for 5g v2x services (v16.2.0 release 16). 3GPP.
5GAA, Nov, 2020. 5g automotive association e.v. working group 5 (list of c-v2x devices). 5GAA Technical report https://5gaa.org/news/list-of-c-v2x-devices/.
Abbasi, I.A., Shahid Khan, A., A review of vehicle to vehicle communication protocols for VANETs in the urban environment. Future Internet 10 (2018), 14–28.
Abboud, K., Omar, H.A., Zhuang, W., Interworking of DSRC and cellular network technologies for V2X communications: A survey. IEEE Trans. Vehicular Technol. 65 (2016), 9457–9470.
Abdelhamid, S., Hassanein, H.S., Takahara, G., Vehicle as a resource (VaaR). IEEE Network 29 (2015), 12–17.
Abuelela, M., Olariu, S., Taking VANET to the clouds. Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia ACM, Paris, France, 2010, 6–13.
Ahmed, E., Gharavi, H., Cooperative vehicular networking: A survey. IEEE Trans. Intell. Transp. Syst. 19 (2018), 996–1014.
Ahmed, M., Li, Y., Waqas, M., Sheraz, M., Jin, D., Han, Z., A survey on socially aware device-to-device communications. IEEE Commun. Surveys Tutorials 20 (2018), 2169–2197.
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M., Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surveys Tutorials 17 (2015), 2347–2376.
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M., Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surveys Tutorials 17 (2015), 2347–2376.
Alhilal, A., Braud, T., Hui, P., 2020. Distributed vehicular computing at the dawn of 5G: a survey. arXiv preprint arXiv:2001.07077.
Aliyu, A., Abdullah, A.H., Kaiwartya, O., Cao, Y., Lloret, J., Aslam, N., Joda, U.M., Towards video streaming in IoT environments: Vehicular communication perspective. Comput. Commun. 118 (2018), 93–119.
Alliance, N., 2015. 5g white paper. Next generation mobile networks, white paper 1.
Amoozadeh, M., Ching, B., Chuah, C.N., Ghosal, D., Zhang, H.M., VENTOS: Vehicular network open simulator with hardware-in-the-loop support. Procedia Computer Science 151 (2019), 61–68.
Araniti, G., Campolo, C., Condoluci, M., Iera, A., Molinaro, A., LTE for vehicular networking: a survey. IEEE Commun. Magazine 51 (2013), 148–157.
Arena, F., Pau, G., An overview of vehicular communications. Future Internet, 11, 2019, 27.
Ashok, A., Steenkiste, P., Bai, F., Vehicular cloud computing through dynamic computation offloading. Comput. Commun. 120 (2018), 125–137.
Astely, D., Dahlman, E., Fodor, G., Parkvall, S., Sachs, J., LTE release 12 and beyond [accepted from open call]. IEEE Commun. Mag. 51 (2013), 154–160.
Bahreini, T., Brocanelli, M., Grosu, D., 2019. Energy-aware speculative execution in vehicular edge computing systems, in: Proceedings of the 2nd International Workshop on Edge Systems, Analytics and Networking, ACM, New York, USA. pp. 18–23.
Barbera, M.V., Kosta, S., Mei, A., Stefa, J., To offload or not to offload? the bandwidth and energy costs of mobile cloud computing. Proceedings IEEE Infocom, 2013, IEEE, Turin, Italy, 1285–1293.
Bian, K., Zhang, G., Song, L., Toward secure crowd sensing in vehicle-to-everything networks. IEEE Network 32 (2017), 126–131.
Boukerche, A., Soto, V., Computation offloading and retrieval for vehicular edge computing: algorithms, models, and classification. ACM Computing Surveys (CSUR) 53 (2020), 1–35.
Buyya, R., Vecchiola, C., Selvi, S.T., Mastering cloud computing: foundations and applications programming. 2013, Newnes.
Chen, C., Chen, L., Liu, L., He, S., Yuan, X., Lan, D., Chen, Z., Delay-optimized V2V-based computation offloading in urban vehicular edge computing and networks. IEEE Access 8 (2020), 18863–18873.
Chih-Lin, I., Kuklinski, S., Chen, T.C., Ladid, L.L., A perspective of o-ran integration with mec, son, and network slicing in the 5g era. IEEE Network 34 (2020), 3–4.
Choo, S., Kim, J., Pack, S., Optimal task offloading and resource allocation in software-defined vehicular edge computing. International Conference on Information and Communication Technology Convergence (ICTC), 2018, IEEE, Jeju, South Korea, 251–256.
Cui, Y., Liang, Y., Wang, R., Resource allocation algorithm with multi-platform intelligent offloading in D2D-enabled vehicular networks. IEEE Access 7 (2019), 21246–21253.
Dai, Y., Xu, D., Maharjan, S., Zhang, Y., Joint load balancing and offloading in vehicular edge computing and networks. IEEE Internet Things J. 6 (2018), 4377–4387.
De Souza, A.B., Rego, P.A.L., Carneiro, T., Rodrigues, J.D.C., Filho, P.P.R., De Souza, J.N., Chamola, V., De Albuquerque, V.H.C., Sikdar, B., Computation offloading for vehicular environments: A survey. IEEE Access 8 (2020), 198214–198243, 10.1109/ACCESS.2020.3033828.
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M.a., Senior, A., Tucker, P., Yang, K., Le, Q., Ng, A., 2012. Large scale distributed deep networks, in: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc. https://proceedings.neurips.cc/paper/2012/file/6aca97005c68f1206823815f66102863-Paper.pdf.
Deng, S., Zhao, H., Fang, W., Yin, J., Dustdar, S., Zomaya, A.Y., Edge intelligence: the confluence of edge computing and artificial intelligence. IEEE Internet Things J. 7 (2020), 7457–7469.
Deng, Z., Cai, Z., Liang, M., A multi-hop VANETs-assisted offloading strategy in vehicular mobile edge computing. IEEE Access 8 (2020), 53062–53071.
Dizdarević, J., Carpio, F., Jukan, A., Masip-Bruin, X., A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration. ACM Computing Surveys (CSUR) 51 (2019), 1–29.
Duhn, M., Parikh, G., Hourdos, J., 2019. I-94 connected vehicles testbed operations and maintenance.
Dziyauddin, R.A., Niyato, D., Luong, N.C., Izhar, M.A.M., Hadhari, M., Daud, S., 2019a. Computation offloading and content caching delivery in vehicular edge computing: A survey. arXiv preprint arXiv:1912.07803.
Dziyauddin, R.A., Niyato, D., Luong, N.C., Izhar, M.A.M., Hadhari, M., Daud, S.M., 2019b. Computation offloading and content caching delivery in vehicular edge computing: A survey. arXiv, arXiv–1912.
El-Sayed, H., Chaqfeh, M., Exploiting mobile edge computing for enhancing vehicular applications in smart cities. Sensors, 19, 2019, 1073.
Eltoweissy, M., Olariu, S., Younis, M., Towards autonomous vehicular clouds. International Conference on Ad Hoc Networks, 2010, Springer, Victoria, BC, Canada, 1–16.
Fettweis, G.P., The tactile internet: Applications and challenges. IEEE Veh. Technol. Mag. 9 (2014), 64–70.
Florin, R., Ghazizadeh, A., Ghazizadeh, P., Olariu, S., Marinescu, D.C., Enhancing reliability and availability through redundancy in vehicular clouds. IEEE Trans. Cloud Computing, 2019.
Gandikota, V., Kane, D., Maity, R.K., Mazumdar, A., vqsgd: Vector quantized stochastic gradient descent. International Conference on Artificial Intelligence and Statistics, PMLR, 2021, 2197–2205.
Ghafoor, K.Z., Kong, L., Zeadally, S., Sadiq, A.S., Epiphaniou, G., Hammoudeh, M., Bashir, A.K., Mumtaz, S., Millimeter-wave communication for internet of vehicles: Status, challenges, and perspectives. IEEE Internet Things J. 7 (2020), 8525–8546.
Guo, J., Song, B., He, Y., Yu, F.R., Sookhak, M., A survey on compressed sensing in vehicular infotainment systems. IEEE Commun. Surveys Tutorials 19 (2017), 2662–2680.
Hamdi, A.M.A., Hussain, F.K., Hussain, O.K., Task offloading in vehicular fog computing: State-of-the-art and open issues. Future Generation Computer Syst., 2022.
Han, B., Hui, P., Kumar, V.S.A., Marathe, M.V., Pei, G., Srinivasan, A., 2010. Cellular traffic offloading through opportunistic communications: a case study, in: CHANTS ’10.
He, X., Lu, H., Du, M., Mao, Y., Wang, K., Qoe-based task offloading with deep reinforcement learning in edge-enabled internet of vehicles. IEEE Trans. Intell. Transp. Syst., 2020.
Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., Chen, S., Vehicular fog computing: A viewpoint of vehicles as the infrastructures. IEEE Trans. Veh. Technol. 65 (2016), 3860–3873.
Hou, X., Ren, Z., Wang, J., Cheng, W., Ren, Y., Chen, K.C., Zhang, H., Reliable computation offloading for edge-computing-enabled software-defined iov. IEEE Internet Things J. 7 (2020), 7097–7111.
Hoymann, C., Astely, D., Stattin, M., Wikstrom, G., Cheng, J.F., Hoglund, A., Frenne, M., Blasco, R., Huschke, J., Gunnarsson, F., LTE release 14 outlook. IEEE Commun. Mag. 54 (2016), 44–49.
Hu, Y.C., Patel, M., Sabella, D., Sprecher, N., Young, V., Mobile edge computing-a key technology towards 5G. ETSI white paper 11 (2015), 1–16.
Huang, X., Xu, K., Lai, C., Chen, Q., Zhang, J., Energy-efficient offloading decision-making for mobile edge computing in vehicular networks. EURASIP J. Wireless Commun. Networking 2020 (2020), 2020–2035.
Huang, X., Yu, R., Kang, J., He, Y., Zhang, Y., Exploring mobile edge computing for 5G-enabled software defined vehicular networks. IEEE Wirel. Commun. 24 (2017), 55–63.
Huang, X., Yu, R., Liu, J., Shu, L., Parked vehicle edge computing: Exploiting opportunistic resources for distributed mobile applications. IEEE Access 6 (2018), 66649–66663.
IBM, NSK, 2013. IBM and nokia siemens networks announce world's first mobile edge computing platform.
Intel, 2014. Self-driving car technology and computing requirements. [Online]. Available: https://www.intel.com/content/ www/us/en/automotive/driving-safety-advanced-driver-assistancesystems- self-driving-technology-paper.html.
Ji, H., Alfarraj, O., Tolba, A., Artificial intelligence-empowered edge of vehicles: Architecture, enabling technologies, and applications. IEEE Access 8 (2020), 61020–61034.
Jiang, D., Taliwal, V., Meier, A., Holfelder, W., Herrtwich, R., Design of 5.9 GHz DSRC-based vehicular safety communication. IEEE Wireless Commun. 13 (2006), 36–43.
Jiang, Z., Zhou, S., Guo, X., Niu, Z., Task replication for deadline-constrained vehicular cloud computing: Optimal policy, performance analysis, and implications on road traffic. IEEE Internet Things J. 5 (2017), 93–107.
Joerger, M., Jones, C., Shuman, V., 2019. Testing connected and automated vehicles (CAVs): Accelerating innovation, integration, deployment and sharing results, in: Road Vehicle Automation 5. Springer, pp. 197–206.
Juniper, White paper: Mobile edge computing use cases & deployment options. [Online]. Available: https://www.juniper.net/ assets/us/en/local/pdf/whitepapers/2000642-en.pdf.
Karagiannis, G., Altintas, O., Ekici, E., Heijenk, G., Jarupan, B., Lin, K., Weil, T., Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. IEEE Commun. Surveys Tutorials 13 (2011), 584–616.
Ke, H., Wang, J., Deng, L., Ge, Y., Wang, H., Deep reinforcement learning-based adaptive computation offloading for mec in heterogeneous vehicular networks. IEEE Trans. Veh. Technol. 69 (2020), 7916–7929.
Kenney, J.B., Dedicated short-range communications (DSRC) standards in the united states. Proc. IEEE 99 (2011), 1162–1182.
Khan, W.U., Jameel, F., Sidhu, G.A.S., Ahmed, M., Li, X., Jäntti, R., Multiobjective optimization of uplink NOMA-enabled vehicle-to-infrastructure communication. IEEE Access 8 (2020), 84467–84478.
Khan, W.Z., Ahmed, E., Hakak, S., Yaqoob, I., Ahmed, A., Edge computing: A survey. Future Generation Computer Systems 97 (2019), 219–235.
Khanh, T.T., Tran, N.H., Huh, E.N., Hong, C.S., et al. Joint offloading and IEEE 802.11 p-based contention control in vehicular edge computing. IEEE Wireless Communications Letters., 2020.
Khayyat, M., Elgendy, I.A., Muthanna, A., Alshahrani, A.S., Alharbi, S., Koucheryavy, A., Advanced deep learning-based computational offloading for multilevel vehicular edge-cloud computing networks. IEEE Access 8 (2020), 137052–137062.
Kloeker, L., Kloeker, A., Thomsen, F., Erraji, A., Eckstein, L., Lamberty, S., Fazekas, A., Kalló, E., Oeser, M., Fléchon, C., et al., 2021. Corridor for new mobility aachen-d⧹usseldorf: Methods and concepts of the research project accord. arXiv preprint arXiv:2107.14048.
Li, J., Cheng, H., Guo, H., Qiu, S., Survey on artificial intelligence for vehicles. Automotive Innovation 1 (2018), 2–14.
Li, L., Zhou, H., Xiong, S.X., Yang, J., Mao, Y., Compound model of task arrivals and load-aware offloading for vehicular mobile edge computing networks. IEEE Access 7 (2019), 26631–26640.
Li, M., Gao, J., Zhao, L., Shen, X., Deep reinforcement learning for collaborative edge computing in vehicular networks. IEEE Trans. Cognitive Commun. Netw. 6 (2020), 1122–1135.
Li, W., Ma, X., Wu, J., Trivedi, K.S., Huang, X.L., Liu, Q., Analytical model and performance evaluation of long-term evolution for vehicle safety services. IEEE Trans. Veh. Technol. 66 (2016), 1926–1939.
Li, Y., An, Z., Wang, Z., Zhong, Y., Chen, S., Feng, C., 2022. V2x-sim: A virtual collaborative perception dataset for autonomous driving. arXiv preprint arXiv:2202.08449.
Liang, H., Zhang, X., Hong, X., Zhang, Z., Li, M., Hu, G., Hou, F., Reinforcement learning enabled dynamic resource allocation in internet of vehicles. IEEE Trans. Industr. Inf., 2020.
Lin, X., Li, J., Yang, W., Wu, J., Zong, Z., Wang, X., Vehicle-to-cloudlet: Game-based computation demand response for mobile edge computing through vehicles. IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019, IEEE, Kuala Lumpur, Malaysia, 1–6.
Liu, J., Ahmed, M., Mirza, M.A., Khan, W.U., Xu, D., Li, J., Aziz, A., Han, Z., Rl/drl meets vehicular task offloading using edge and vehicular cloudlet: A survey. IEEE Internet Things J. 9:11 (2022), 8315–8338, 10.1109/JIOT.2022.3155667.
Liu, L., Chen, C., Pei, Q., Maharjan, S., Zhang, Y., Vehicular edge computing and networking: A survey. Mobile Netw. Appl. 26 (2021), 1145–1168.
Liu, L., Chen, C., Pei, Q., Maharjan, S., Zhang, Y., Vehicular edge computing and networking: A survey. Mobile Netw. Appl. 26 (2021), 1145–1168.
Liu, Q., Su, Z., Hui, Y., Computation offloading scheme to improve QoE in vehicular networks with mobile edge computing. 10th International Conference on Wireless Communications and Signal Processing (WCSP), 2018, IEEE, Hangzhou, China, 1–5.
Liu, Y., Wang, S., Huang, J., Yang, F., A computation offloading algorithm based on game theory for vehicular edge networks. IEEE International Conference on Communications (ICC), 2018, IEEE, Kansas City, MO, USA, 1–6.
Liu, Y., Wang, S., Zhao, Q., Du, S., Zhou, A., Ma, X., Yang, F., Dependency-aware task scheduling in vehicular edge computing. IEEE Internet Things J. 7 (2020), 4961–4971, 10.1109/JIOT.2020.2972041.
LiWang, M., Dai, S., Gao, Z., Du, X., Guizani, M., Dai, H., A computation offloading incentive mechanism with delay and cost constraints under 5g satellite-ground iov architecture. IEEE Wirel. Commun. 26 (2019), 124–132.
LiWang, M., Dai, S., Gao, Z., Tang, Y., Dai, H., A truthful reverse-auction mechanism for computation offloading in cloud-enabled vehicular network. IEEE Internet Things J. 6 (2018), 4214–4227.
Liwang, M., Wang, J., Gao, Z., Du, X., Guizani, M., Game theory based opportunistic computation offloading in cloud-enabled IoV. IEEE Access 7 (2019), 32551–32561.
Lu, S., Shi, W., The emergence of vehicle computing. IEEE Internet Comput. 25 (2021), 18–22, 10.1109/MIC.2021.3066076.
Ma, L., Yi, S., Li, Q., Efficient service handoff across edge servers via docker container migration. Proceedings of the Second ACM/IEEE Symposium on Edge Computing, 2017, 1–13.
Mach, P., Becvar, Z., Mobile edge computing: A survey on architecture and computation offloading. IEEE Commun. Surveys Tutorials 19 (2017), 1628–1656.
Maleki, H., Başaran, M., Durak-Ata, L., Reinforcement learning-based decision-making for vehicular edge computing. 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, 1–4, 10.1109/SIU53274.2021.9478026.
Malinverno, M., Mangues-Bafalluy, J., Casetti, C.E., Chiasserini, C.F., Requena-Esteso, M., Baranda, J., An edge-based framework for enhanced road safety of connected cars. IEEE Access 8 (2020), 58018–58031.
Manvi, S.S., Tangade, S., A survey on authentication schemes in VANETs for secured communication. Vehicular Communications 9 (2017), 19–30.
Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B., A survey on mobile edge computing: The communication perspective. IEEE Commun. Surveys Tutorials 19 (2017), 2322–2358.
Mell, P., Grance, T., 2011. The nist definition of cloud computing.
Mu, S., Zhong, Z., Ni, M., Multi-destination computation offloading in vehicular networks. 14th International Wireless Communications & Mobile Computing Conference (IWCMC), 2018, IEEE, Limassol, Cyprus, 446–451.
Nguyen, Q.H., Morold, M., David, K., Dressler, F., Car-to-pedestrian communication with mec-support for adaptive safety of vulnerable road users. Comput. Commun. 150 (2020), 83–93.
Ning, Z., Dong, P., Wang, X., Guo, L., Rodrigues, J.J., Kong, X., Huang, J., Kwok, R.Y., Deep reinforcement learning for intelligent internet of vehicles: An energy-efficient computational offloading scheme. IEEE Trans. Cognitive Commun. Networking 5 (2019), 1060–1072.
Ning, Z., Hu, X., Hu, B., Dong, P., Wang, X., Guo, L., Huang, J., Obaidat, M., Li, Y., Guo, Y., When deep reinforcement learning meets 5g-enabled vehicular networks: A distributed offloading framework for traffic big data. IEEE Trans. Industr. Inf. 16 (2020), 1352–1361.
Olariu, S., A survey of vehicular cloud research: Trends, applications and challenges. IEEE Trans. Intell. Transp. Syst. 21 (2020), 2648–2663, 10.1109/TITS.2019.2959743.
Ouyang, Y., Task offloading algorithm of vehicle edge computing environment based on dueling-dqn. J. Phys.: Conf. Ser., IOP Publishing., 2021, 012046.
Parvini, M., 2021. Aoi-aware resource allocation for platoon-based c-v2x networks via multi-agent multi-task reinforcement learning. https://dx.doi.org/10.21227/3kfr-ct25, 10.21227/3kfr-ct25.
Pasha, M., et al. Opportunistic task offloading in vehicular networks. Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2017, IEEE, Chennai, India, 510–514.
Peng, H., Shen, X., Multi-agent reinforcement learning based resource management in mec- and uav-assisted vehicular networks. IEEE J. Sel. Areas Commun. 39 (2021), 131–141, 10.1109/JSAC.2020.3036962.
Qayyum, A., Usama, M., Qadir, J., Al-Fuqaha, A., Securing connected amp; autonomous vehicles: Challenges posed by adversarial machine learning and the way forward. IEEE Commun. Surveys Tutorials 22 (2020), 998–1026, 10.1109/COMST.2020.2975048.
Qi, Q., Wang, J., Ma, Z., Sun, H., Cao, Y., Zhang, L., Liao, J., Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach. IEEE Trans. Veh. Technol. 68 (2019), 4192–4203.
Raza, S., Liu, W., Ahmed, M., Anwar, M.R., Mirza, M.A., Sun, Q., Wang, S., An efficient task offloading scheme in vehicular edge computing. J. Cloud Computing 9 (2020), 1–14.
Raza, S., Wang, S., Ahmed, M., Anwar, M.R., 2019. A survey on vehicular edge computing: Architecture, applications, technical issues, and future directions. Wireless Communications and Mobile Computing 2019.
Raza, S., Wang, S., Ahmed, M., Anwar, M.R., Mirza, M.A., Khan, W.U., Task offloading and resource allocation for iov using 5g nr-v2x communication. IEEE Internet Things J., 2021, 10.1109/JIOT.2021.3121796 1–1.
Rebecchi, F., Dias de Amorim, M., Conan, V., Passarella, A., Bruno, R., Conti, M., Data offloading techniques in cellular networks: A survey. IEEE Commun. Surveys Tutorials 17 (2015), 580–603, 10.1109/COMST.2014.2369742.
Rodrigues, T.G., Suto, K., Nishiyama, H., Kato, N., Hybrid method for minimizing service delay in edge cloud computing through VM migration and transmission power control. IEEE Trans. Comput. 66 (2016), 810–819.
Santa, J., Gómez-Skarmeta, A.F., Sánchez-Artigas, M., Architecture and evaluation of a unified V2V and V2I communication system based on cellular networks. Comput. Commun. 31 (2008), 2850–2861.
Satyanarayanan, M., The emergence of edge computing. Computer 50 (2017), 30–39.
Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N., The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing 8 (2009), 14–23.
Shah, S.S., Ali, M., Malik, A.W., Khan, M.A., Ravana, S.D., vfog: A vehicle-assisted computing framework for delay-sensitive applications in smart cities. IEEE Access 7 (2019), 34900–34909.
Sheraz, M., Ahmed, M., Hou, X., Li, Y., Jin, D., Han, Z., Artificial intelligence for wireless caching: Schemes, performance, and challenges. IEEE Commun. Surveys Tutorials, 2020 1–1.
Shi, J., Du, J., Wang, J., Yuan, J., Distributed v2v computation offloading based on dynamic pricing using deep reinforcement learning. 2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020, IEEE, 1–6.
Silva, C., Silva, L., Santos, L., Sarubbi, J., Pitsillides, A., Broadening understanding on managing the communication infrastructure in vehicular networks: Customizing the coverage using the delta network. Future Internet 11 (2019), 1–19.
Singh, P.K., Nandi, S.K., Nandi, S., A tutorial survey on vehicular communication state of the art, and future research directions. Vehicular Commun., 18, 2019, 100164.
Skondras, E., Michalas, A., Vergados, D.D., Mobility management on 5g vehicular cloud computing systems. Vehicular Commun. 16 (2019), 15–44.
Sommer, C., Eckhoff, D., Brummer, A., Buse, D.S., Hagenauer, F., Joerer, S., Segata, M., Veins: The open source vehicular network simulation framework. Recent Advances in Network Simulation, 2019, Springer, 215–252.
Sommer, C., Härri, J., Hrizi, F., Schünemann, B., Dressler, F., Simulation tools and techniques for vehicular communications and applications. Vehicular ad hoc Networks, 2015, Springer, 365–392.
Spinelli, F., Mancuso, V., Towards enabled industrial verticals in 5g: a survey on mec-based approaches to provisioning and flexibility. IEEE Commun. Surveys Tutorials, 2020.
Spinelli, F., Mancuso, V., Toward enabled industrial verticals in 5g: A survey on mec-based approaches to provisioning and flexibility. IEEE Commun. Surveys Tutorials 23 (2021), 596–630, 10.1109/COMST.2020.3037674.
Storck, C.R., Duarte-Figueiredo, F., A 5G V2X ecosystem providing internet of vehicles. Sensors, 19, 2019, 550.
Su, Z., Hui, Y., Luan, T.H., Distributed task allocation to enable collaborative autonomous driving with network softwarization. IEEE J. Sel. Areas Commun. 36 (2018), 2175–2189.
Sun, F., Cheng, N., Zhang, S., Zhou, H., Gui, L., Shen, X., Reinforcement learning based computation migration for vehicular cloud computing. IEEE Global Communications Conference (GLOBECOM), 2018, IEEE, Abu Dhabi, UAE, 1–6.
Sun, F., Hou, F., Cheng, N., Wang, M., Zhou, H., Gui, L., Shen, X., Cooperative task scheduling for computation offloading in vehicular cloud. IEEE Trans. Veh. Technol. 67 (2018), 11049–11061.
Sun, J., Gu, Q., Zheng, T., Dong, P., Valera, A., Qin, Y., Joint optimization of computation offloading and task scheduling in vehicular edge computing networks. IEEE Access 8 (2020), 10466–10477.
Sun, W., Liu, J., Zhang, H., When smart wearables meet intelligent vehicles: Challenges and future directions. IEEE Wireless Commun. 24 (2017), 58–65.
Sun, Y., Guo, X., Zhou, S., Jiang, Z., Liu, X., Niu, Z., Learning-based task offloading for vehicular cloud computing systems. IEEE International Conference on Communications (ICC), 2018, IEEE, Kansas City, MO, USA, 1–7.
Sun, Y., Song, J., Zhou, S., Guo, X., Niu, Z., Task replication for vehicular edge computing: A combinatorial multi-armed bandit based approach. 2018 IEEE Global Communications Conference (GLOBECOM), 2018, IEEE, 1–7.
Szendrei, Z., Varga, N., Bokor, L., A sumo-based hardware-in-the-loop V2X simulation framework for testing and rapid prototyping of cooperative vehicular applications. Vehicle and Automotive Engineering, 2018, Springer, 426–440.
Tan, L.T., Hu, R.Q., Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning. IEEE Trans. Veh. Technol. 67 (2018), 10190–10203.
Tang, D., Zhang, X., Li, M., Tao, X., Adaptive inference reinforcement learning for task offloading in vehicular edge computing systems. 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020, IEEE, 1–6.
Tang, W., Li, S., Rafique, W., Dou, W., Yu, S., An offloading approach in fog computing environment. IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2018, IEEE, Guangzhou, China, 857–864.
Tokody, D., Albini, A., Ady, L., Rajnai, Z., Pongrácz, F., Safety and security through the design of autonomous intelligent vehicle systems and intelligent infrastructure in the smart city. Interdisciplinary Description of Complex Systems: INDECS 16 (2018), 384–396.
Tong, W., Hussain, A., Bo, W.X., Maharjan, S., Artificial intelligence for vehicle-to-everything: A survey. IEEE Access 7 (2019), 10823–10843.
Uhlemann, E., Connected-vehicles applications are emerging [connected vehicles]. IEEE Veh. Technol. Mag. 11 (2016), 25–96.
Vahdat-Nejad, H., Ramazani, A., Mohammadi, T., Mansoor, W., A survey on context-aware vehicular network applications. Vehicular Commun. 3 (2016), 43–57.
Vegni, A.M., Biagi, M., Cusani, R., et al. Smart vehicles, technologies and main applications in vehicular ad hoc networks. Vehicular Technologies-deployment Appl., 2013, 3–20.
Wang, H., Li, X., Ji, H., Zhang, H., Dynamic offloading scheduling scheme for MEC-enabled vehicular networks. IEEE/CIC International Conference on Communications in China (ICCC Workshops), 2018, IEEE, Beijing, China, 206–210.
Wang, H., Li, X., Ji, H., Zhang, H., Federated offloading scheme to minimize latency in MEC-enabled vehicular networks. IEEE Globecom Workshops (GC Wkshps), 2018, IEEE, Abu Dhabi, UAE, 1–6.
Wang, J., Liu, Y., Jiao, Y., Building a trusted route in a mobile ad hoc network considering communication reliability and path length. J. Network Computer Appl. 34 (2011), 1138–1149.
Wang, L., Zhang, Q., Li, Y., Zhong, H., Shi, W., Mobileedge: Enhancing on-board vehicle computing units using mobile edges for CAVs. IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), 2019, IEEE, Tianjin, China, 470–479.
Wang, S., Huang, J., Zhang, X., Demystifying millimeter-wave v2x: Towards robust and efficient directional connectivity under high mobility. Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, 2020.
Wang, S., Zhang, X., Zhang, Y., Wang, L., Yang, J., Wang, W., A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access 5 (2017), 6757–6779.
Wang, X., Mao, S., Gong, M.X., An overview of 3GPP cellular vehicle-to-everything standards. GetMobile: Mobile Computing Commun. 21 (2017), 19–25.
Wang, X., Ning, Z., Wang, L., Offloading in internet of vehicles: A fog-enabled real-time traffic management system. IEEE Trans. Industr. Inf. 14 (2018), 4568–4578.
Wang, Z., Zhong, Z., Ni, M., Application-aware offloading policy using smdp in vehicular fog computing systems. IEEE International Conference on Communications Workshops (ICC Workshops), 2018, IEEE, Kansas City, MO, USA, 1–6.
Wang, Z., Zhong, Z., Zhao, D., Ni, M., Vehicle-based cloudlet relaying for mobile computation offloading. IEEE Trans. Veh. Technol. 67 (2018), 11181–11191.
Wu, S., Xia, W., Cui, W., Chao, Q., Lan, Z., Yan, F., Shen, L., An efficient offloading algorithm based on support vector machine for mobile edge computing in vehicular networks. 10th International Conference on Wireless Communications and Signal Processing (WCSP), 2018, IEEE, Hangzhou, China, 1–6.
Xiong, K., Leng, S., Chen, X., Huang, C., Yuen, C., Guan, Y.L., 2020. Communication and computing resource optimization for connected autonomous driving. arXiv preprint arXiv:2006.15875.
Xu, D., Li, Y., Chen, X., Li, J., Hui, P., Chen, S., Crowcroft, J., A survey of opportunistic offloading. IEEE Commun. Surveys Tutorials 20 (2018), 2198–2236.
Xu, X., Xue, Y., Li, X., Qi, L., Wan, S., A computation offloading method for edge computing with vehicle-to-everything. IEEE Access 7 (2019), 131068–131077.
Yacoub, A., 2020. Measurement data for compact mimo antenna systems for sub-6ghz 5g and v2x communications. https://dx.doi.org/10.21227/g0xs-gr79, 10.21227/g0xs-gr79.
Yacoub, A., Khalifa, M., Aloi, D., 2020. measurement data for wide bandwidth low profile pifa antenna for vehicular sub-6ghz 5g and v2x wireless systems. https://dx.doi.org/10.21227/9dvd-yt95, 10.21227/9dvd-yt95.
Yao, C., Wang, X., Zheng, Z., Sun, G., Song, L., Edgeflow: Open-source multi-layer data flow processing in edge computing for 5G and beyond. IEEE Network 33 (2018), 166–173.
Ye, Q., Shi, W., Qu, K., He, H., Zhuang, W., Shen, X., Joint ran slicing and computation offloading for autonomous vehicular networks: A learning-assisted hierarchical approach. IEEE Open J. Veh. Technol. 2 (2021), 272–288, 10.1109/OJVT.2021.3089083.
Yu, H., Luo, Y., Shu, M., Huo, Y., Yang, Z., Shi, Y., Guo, Z., Li, H., Hu, X., Yuan, J., Nie, Z., 2022. Dair-v2x: A large-scale dataset for vehicle-infrastructure cooperative 3d object detection, in: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR).
Zhan, W., Luo, C., Wang, J., Wang, C., Min, G., Duan, H., Zhu, Q., Deep-reinforcement-learning-based offloading scheduling for vehicular edge computing. IEEE Internet Things J. 7 (2020), 5449–5465.
Zhang, E., Masoud, N., V2xsim: A v2x simulator for connected and automated vehicle environment simulation. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020, 1–6, 10.1109/ITSC45102.2020.9294660.
Zhang, K., Mao, Y., Leng, S., He, Y., Zhang, Y., Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading. IEEE Veh. Technol. Mag. 12 (2017), 36–44.
Zhang, K., Mao, Y., Leng, S., Maharjan, S., Vinel, A., Zhang, Y., Contract-theoretic approach for delay constrained offloading in vehicular edge computing networks. Mobile Networks Appl. 24 (2019), 1003–1014.
Zhang, K., Mao, Y., Leng, S., Maharjan, S., Zhang, Y., Optimal delay constrained offloading for vehicular edge computing networks. IEEE International Conference on Communications (ICC), 2017, IEEE, Paris, France, 1–6.
Zhang, K., Mao, Y., Leng, S., Vinel, A., Zhang, Y., Delay constrained offloading for mobile edge computing in cloud-enabled vehicular networks. 8th International Workshop on Resilient Networks Design and Modeling (RNDM), 2016, IEEE, Halmstad, Sweden, 288–294.
Zhang, M., Polese, M., Mezzavilla, M., Zhu, J., Rangan, S., Panwar, S., Zorzi, M., Will TCP work in mmwave 5G cellular networks?. IEEE Commun. Mag. 57 (2019), 65–71.
Zhang, S., Chen, J., Lyu, F., Cheng, N., Shi, W., Shen, X., Vehicular communication networks in the automated driving era. IEEE Commun. Mag. 56 (2018), 26–32.
Zhang, Y., Lopez, J., Wang, Z., Mobile edge computing for vehicular networks [from the guest editors]. IEEE Veh. Technol. Mag. 14 (2019), 27–108.
Zhao, J., Li, Q., Gong, Y., Zhang, K., Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks. IEEE Trans. Veh. Technol. 68 (2019), 7944–7956.
Zhao, J., Wang, L., Wong, K.K., Tao, M., Mahmoodi, T., 2018. Energy and latency control for edge computing in dense v2x networks. arXiv preprint arXiv:1807.02311.
Zhou, J., Tian, D., Wang, Y., Sheng, Z., Duan, X., Leung, V.C., Reliability-oriented optimization of computation offloading for cooperative vehicle-infrastructure systems. IEEE Signal Process. Lett. 26 (2018), 104–108.
Zhou, J., Wu, F., Zhang, K., Mao, Y., Leng, S., Joint optimization of offloading and resource allocation in vehicular networks with mobile edge computing. 10th International Conference on Wireless Communications and Signal Processing (WCSP), 2018, IEEE, Hangzhou, China, 1–6.
Zhou, Z., Liu, P., Chang, Z., Xu, C., Zhang, Y., Energy-efficient workload offloading and power control in vehicular edge computing. IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2018, IEEE, Barcelona, Spain, 191–196.
Zhou, Z., Liu, P., Feng, J., Zhang, Y., Mumtaz, S., Rodriguez, J., Computation resource allocation and task assignment optimization in vehicular fog computing: A contract-matching approach. IEEE Trans. Veh. Technol. 68 (2019), 3113–3125.
Zhu, C., Tao, J., Pastor, G., Xiao, Y., Ji, Y., Zhou, Q., Li, Y., Ylä-Jääski, A., Folo: Latency and quality optimized task allocation in vehicular fog computing. IEEE Internet Things J. 6 (2018), 4150–4161.
Zhu, C., Zhou, H., Leung, V.C., Wang, K., Zhang, Y., Yang, L.T., Toward big data in green city. IEEE Comm. Magazine 55 (2017), 14–18.