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The Internet of Vehicles (IoV) plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information. Generally, collected information is transmitted to a centralized resource-intensive cloud platform for service implementation. Edge Computing (EC) that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users. Additionally, many measures are adopted to optimize the performance of EC-enabled IoV, but they hardly help make dynamic decisions according to real-time requests. Artificial Intelligence (AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically. Although extensive research has employed AI to optimize EC performance, summaries with relative concepts or prospects are quite few. To address this gap, we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV. Firstly, we establish the general condition and relative concepts about IoV, EC, and AI. Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading. Finally, we discuss a number of open issues in optimizing edge services with AI.


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Artificial Intelligence for Edge Service Optimization in Internet of Vehicles: A Survey

Show Author's information Xiaolong XuHaoyuan LiWeijie XuZhongjian LiuLiang YaoFei Dai( )
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology and Engineering, Nanjing 210044, China
School of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming 650233, China
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China

Abstract

The Internet of Vehicles (IoV) plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information. Generally, collected information is transmitted to a centralized resource-intensive cloud platform for service implementation. Edge Computing (EC) that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users. Additionally, many measures are adopted to optimize the performance of EC-enabled IoV, but they hardly help make dynamic decisions according to real-time requests. Artificial Intelligence (AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically. Although extensive research has employed AI to optimize EC performance, summaries with relative concepts or prospects are quite few. To address this gap, we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV. Firstly, we establish the general condition and relative concepts about IoV, EC, and AI. Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading. Finally, we discuss a number of open issues in optimizing edge services with AI.

Keywords: artificial intelligence, internet of vehicles, edge service

References(104)

[1]
M. Shafi, A. F. Molisch, P. J. Smith, T. Haustein, P. Y. Zhu, P. De Silva, F. Tufvesson, A. Benjebbour, and G. Wunder, 5G: A tutorial overview of standards, trials, challenges, deployment, and practice, IEEE Journal on Selected Areas in Communications, vol. 35, no. 6, pp. 1201-1221, 2017.
[2]
W. Y. Zhong, X. C. Yin, X. Y. Zhang, S. C. Li, W. C. Dou, R. L. Wang, and L. Y. Qi, Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment, Computer Communications, vol. 157, pp. 116-123, 2020.
[3]
X. L. Xu, X. Zhang, X. H. Liu, J. L. Jiang, L. Y. Qi, and M. Z. A. Bhuiyan, Adaptive computation offloading with edge for 5G-envisioned internet of connected vehicles, IEEE Transactions on Intelligent Transportation Systems, .
[4]
C. J. Zhou, A. L. Li, A. H. Hou, Z. W. Zhang, Z. X. Zhang, P. F. Dai, and F. S. Wang, Modeling methodology for early warning of chronic heart failure based on real medical big data, Expert Systems with Applications, vol. 151, p. 113361, 2020.
[5]
X. L. Xu, Q. Wu, L. Y. Qi, W. C. Dou, S. B Tsai, and M. Z. A. Bhuiyan, Trust-aware service offloading for video surveillance in edge computing enabled internet of vehicles, IEEE Transactions on Intelligent Transportation Systems, .
[6]
H. Yang, F. Li, D. X. Yu, Y. F. Zou, and J. G. Yu, Reliable data storage in heterogeneous wireless sensor networks by jointly optimizing routing and storage node deployment, Tsinghua Science and Technology, vol. 26, no. 2, pp. 230-238, 2020.
[7]
P. Mach and Z. Becvar, Mobile edge computing: A survey on architecture and computation offloading, IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1628-1656, 2017.
[8]
S. G. Wang, Y. L. Zhao, J. Xu, J. Yuan, and C. H. Hsu, Edge server placement in mobile edge computing, Journal of Parallel and Distributed Computing, vol. 127, pp. 160-168, 2019.
[9]
H. Yin, X. Zhang, H. H. Liu, Y. Luo, C. Tian, S. Y. Zhao, and F. Li, Edge provisioning with flexible server placement, IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 4, pp. 1031-1045, 2017.
[10]
Y. Y. Mao, J. Zhang, and K. B. Letaief, Dynamic computation offloading for mobile-edge computing with energy harvesting devices, IEEE Journal on Selected Areas in Communications, vol. 34, no. 12, pp. 3590-3605, 2016.
[11]
X. L. Xu, B. W. Shen, X. C. Yin, M. R. Khosravi, H. M. Wu, L. Y. Qi, and S. H. Wan, Edge server quantification and placement for offloading social media services in industrial cognitive IoV, IEEE Transactions on Industrial Informatics, .
[12]
C. M. Wang, C. C. Liang, F. R. Yu, Q. B. Chen, and L. Tang, Computation offloading and resource allocation in wireless cellular networks with mobile edge computing, IEEE Transactions on Wireless Communications, vol. 16, no. 8, pp. 4924-4938, 2017.
[13]
A. Samanta and Y. Li, Latency-oblivious incentive service offloading in mobile edge computing, presented at 2018 IEEE/ACM Symp. Edge Computing (SEC), Seattle, WA, USA, 2018, pp. 351-353.
[14]
Q. He, G. M. Cui, X. Y. Zhang, F. F. Chen, S. G. Deng, H. Jin, Y. H. Li, and Y. Yang, A game-theoretical approach for user allocation in edge computing environment, IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 3, pp. 515-529, 2020.
[15]
X. Z. Chen, H. M. Ma, J. Wan, B. Li, and T. Xia, Multi-view 3D object detection network for autonomous driving, presented at 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 6526-6534.
[16]
X. Y. Huang, X. J. Cheng, Q. C. Geng, B. B. Cao, D. F. Zhou, P. Wang, Y. Q. Lin, and R. G. Yang, The ApolloScape dataset for autonomous driving, presented at 2018 IEEE/CVF Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, 2018, pp. 1067-1076.
[17]
A. Samanta and Z. Chang, Adaptive service offloading for revenue maximization in mobile edge computing with delay-constraint, IEEE Internet of Things Journal, vol. 6, no. 2, pp. 3864-3872, 2019.
[18]
Q. C. Cao, W. L. Zhang, and Y. H. Zhu, Deep learning-based classification of the polar emotions of “moe”-style cartoon pictures, Tsinghua Science and Technology, vol. 26, no. 3, pp. 275-286, 2020.
[19]
E. Li, L. K. Zeng, Z. Zhou, and X. Chen, Edge AI: On-demand accelerating deep neural network inference via edge computing, IEEE Transactions on Wireless Communications, vol. 19, no. 1, pp. 447-457, 2020.
[20]
Y. N. Malek, M. Najib, M. Bakhouya, and M. Essaaidi, Multivariate deep learning approach for electric vehicle speed forecasting, Big Data Mining and Analytics, vol. 4, no. 1, pp. 56-64, 2021.
[21]
Z. Zou, Y. Jin, P. Nevalainen, Y. X. Huan, J. Heikkonen, and T. Westerlund, Edge and fog computing enabled AI for IoT-An overview, presented at 2019 IEEE Int. Conf. Artificial Intelligence Circuits and Systems (AICAS), Hsinchu, China, 2019, pp. 51-56.
[22]
A. H. Sodhro, S. Pirbhulal, and V. H. C. de Albuquerque, Artificial intelligence-driven mechanism for edge computing-based industrial applications, IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4235-4243, 2019.
[23]
D. W. Wei, H. S. Ning, F. F. Shi, Y. L. Wan, J. B. Xu, S. K. Yang, and L. Zhu, Dataflow management in the Internet of Things: Sensing, control, and security, Tsinghua Science and Technology, vol. 26, no. 6, pp. 918-930, 2021.
[24]
L. M. Ang, K. P. Seng, G. K. Ijemaru, and A. M. Zungeru, Deployment of IOV for smart cities: Applications, architecture, and challenges, IEEE Access, vol. 7, pp. 6473-6492, 2019.
[25]
B. F. Ji, X. R. Zhang, S. Mumtaz, C. Z. Han, C. G. Li, H. Wen, and D. Wang, Survey on the internet of vehicles: Network architectures and applications, IEEE Communications Standards Magazine, vol. 4, no. 1, pp. 34-41, 2020.
[26]
K. Smida, H. Tounsi, M. Frikha, and Y. Q. Song, Software defined internet of vehicles: A survey from QoS and scalability perspectives, presented at 2019 15th Int. Wireless Communications & Mobile Computing Conf. (IWCMC), Tangier, Morocco, 2019, pp. 1349-1354.
[27]
H. Talat, T. Nomani, M. Mohsin, and S. Sattar, A survey on location privacy techniques deployed in vehicular networks, presented at 2019 16th Int. Bhurban Conf. Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, 2019, pp. 604-613.
[28]
H. B. Zhou, W. C. Xu, J. C. Chen, and W. Wang, Evolutionary v2x technologies toward the internet of vehicles: Challenges and opportunities, Proceedings of the IEEE, vol. 108, no. 2, pp. 308-323, 2020.
[29]
X. L. Xu, Y. Xue, L. Y. Qi, Y. Yuan, X. Y. Zhang, T. Umer, and S. H. Wan, An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles, Future Generation Computer Systems, vol. 96, pp. 89-100, 2019.
[30]
Z. Tong, F. Ye, M. Yan, H. Liu, and S. Basodi, A survey on algorithms for intelligent computing and smart city applications, Big Data Mining and Analytics, vol. 4, no. 3, pp. 155-172, 2021.
[31]
F. C. Yang, S. G. Wang, J. L. Li, Z. H. Liu, and Q. B. Sun, An overview of internet of vehicles, China Communications, vol. 11, no. 10, pp. 1-15, 2014.
[32]
M. Azrour, J. Mabrouki, A. Guezzaz, and Y. Farhaoui, New enhanced authentication protocol for Internet of Things, Big Data Mining and Analytics, vol. 4, no. 1, pp. 1-9, 2021.
[33]
X. K. Wang, L. T. Yang, L. W. Song, H. H. Wang, L. Ren, and J. Deen, A tensor-based multi-attributes visual feature recognition method for industrial intelligence, IEEE Transactions on Industrial Informatics, .
[34]
D. R. Lawrence, C. Palacios-González, and J. Harris, Artificial intelligence: The shylock syndrome, CambridgeQuarterly of Healthcare Ethics, vol. 25, no. 2, pp. 250-261, 2016.
[35]
J. X. Li, T. T. Cai, K. Deng, X. J. Wang, T. Sellis, and F. Xia, Community-diversified influence maximization in social networks, Information Systems, vol. 92, p. 101522, 2020.
[36]
M. Caprolu, R. Di Pietro, F. Lombardi, and S. Raponi, Edge computing perspectives: Architectures, technologies, and open security issues, presented at 2019 IEEE Int. Conf. Edge Computing (EDGE), Milan, Italy, 2019, pp. 116-123.
[37]
H. H. Pang and K. L. Tan, Authenticating query results in edge computing, in Proc. 20th Int. Conf. Data Engineering, Boston, MA, USA, 2004, pp. 560-571.
[38]
S. Al-Janabi and A. H. Salman, Sensitive integration of multilevel optimization model in human activity recognition for smartphone and smartwatch applications, Big Data Mining and Analytics, vol. 4, no. 2, pp. 124-138, 2021.
[39]
M. Alrowaily and Z. Lu, Secure edge computing in IoT systems: Review and case studies, presented at 2018 IEEE/ACM Symp. Edge Computing (SEC), Seattle, WA, USA, 2018, pp. 440-444.
[40]
X. X. Chi, C. Yan, H. Wang, W. Rafique, and L. Y. Qi, Amplified locality-sensitive hashing-based recommender systems with privacy protection, Concurrency and Computation: Practice and Experience, .
[41]
R. Bi, Q. Liu, J. K. Ren, and G. Z. Tan, Utility aware offloading for mobile-edge computing, Tsinghua Science and Technology, vol. 26, no. 2, pp. 239-250, 2020.
[42]
G. Manasvi, A. Chakraborty, and B. S. Manoj, Social network aware dynamic edge server placement for next-generation cellular networks, presented at 2020 Int. Conf. Communication Systems & Networks (COMSNETS), Bengaluru, India, 2020, pp. 499-502.
[43]
Y. Liang, J. D. Ge, S. Zhang, J. Wu, Z. Tang, and B. Luo, A utility-based optimization framework for edge service entity caching, IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 11, pp. 2384-2395, 2019.
[44]
X. L. Xu, X. H. Liu, Z. Y. Xu, F. Dai, X. Y. Zhang, and L. Y. Qi, Trust-oriented IoT service placement for smart cities in edge computing, IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4084-4091, 2020.
[45]
D. Kombate and L. N. Wang, The internet of vehicles based on 5G communications, presented at 2016 IEEE Int. Conf. Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, China, 2016, pp. 445-448.
[46]
N. Sharma, N. Chauhan, and N. Chand, Smart logistics vehicle management system based on internet of vehicles, presented at 2016 4th Int Conf. Parallel, Distributed and Grid Computing (PDGC), Waknaghat, India, 2016, pp. 495-499.
[47]
J. X. Li, T. T. Cai, A. Mian, R. H. Li, T. Sellis, and J. X. Yu, Holistic influence maximization for targeted advertisements in spatial social networks, presented at 2018 IEEE 34th Int. Conf. Data Engineering (ICDE), Paris, France, 2018, pp. 1340-1343.
[48]
P. Lai, Q. He, M. Abdelrazek, F. F. Chen, J. Hosking, J. Grundy, and Y. Yang, Optimal edge user allocation in edge computing with variable sized vector bin packing, in Proc. 16th Int. Conf. Service-Oriented Computing, Hangzhou, China, 2018, pp. 230-245.
[49]
X. W. Hou, Z. Y. Ren, J. J. Wang, W. C. Cheng, Y. Ren, K. C. Chen, and H. L. Zhang, Reliable computation offloading for edge-computing-enabled software-defined IoV, IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7097-7111, 2020.
[50]
S. Khorsandroo and A. S. Tosun, An experimental investigation of SDN controller live migration in virtual data centers, presented at 2017 IEEE Conf. Network Function Virtualization and Software Defined Networks (NFV-SDN), Berlin, Germany, 2017, pp. 309-314.
[51]
Z. J. Hu, D. Y. Wang, Z. Li, M. Sun, and W. Z. Wang, Differential compression for mobile edge computing in internet of vehicles, presented at 2019 Int. Conf. Wireless and Mobile Computing, Networking and Communications (WiMob), Barcelona, Spain, 2019, pp. 336-341.
[52]
K. Wang, H. Yin, W. Quan, and G. Y. Min, Enabling collaborative edge computing for software defined vehicular networks, IEEE Network, vol. 32, no. 5, pp. 112-117, 2018.
[53]
Y. Y. Dai, D. Xu, Y. L. Lu, S. Maharjan, and Y. Zhang, Deep reinforcement learning for edge caching and content delivery in internet of vehicles, presented at 2019 IEEE/CIC Int. Conf. Communications in China (ICCC), Changchun, China, 2019, pp. 134-139.
[54]
H. W. Liu, H. Z. Kou, C. Yan, and L. Y. Qi, Keywords-driven and popularity-aware paper recommendation based on undirected paper citation graph, Complexity, vol. 2020, p. 2085638, 2020.
[55]
X. K. Wang, L. T. Yang, X. Xie, J. R. Jin, and M. J. Deen, A cloud-edge computing framework for cyber-physical-social services, IEEE Communications Magazine, vol. 55, no. 11, pp. 80-85, 2017.
[56]
M. Körner, T. M. Runge, A. Panda, S. Ratnasamy, and S. Shenker, Open carrier interface: An open source edge computing framework, in Proc. 2018 Workshop on Networking for Emerging Applications and Technologies, Budapest, Hungary, 2018, pp. 27-32.
[57]
Y. M. Saputra, D. T. Hoang, D. N. Nguyen, E. Dutkiewicz, D. Niyato, and D. I. Kim, Distributed deep learning at the edge: A novel proactive and cooperative caching framework for mobile edge networks, IEEE Wireless Communications Letters, vol. 8, no. 4, pp. 1220-1223, 2019.
[58]
H. Ji, O. Alfarraj, and A. Tolba, Artificial intelligence-empowered edge of vehicles: Architecture, enabling technologies, and applications, IEEE Access, vol. 8, pp. 61 020-61 034, 2020.
[59]
S. Park and N. Kwak, Cultural event recognition by subregion classification with convolutional neural network, presented at 2015 IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA, 2015, pp. 45-50.
[60]
Z. Y. Jiao, Y. Yang, H. R. Zhu, and F. J. Ren, Realization and improvement of object recognition system on raspberry Pi 3B+, presented at 2018 5th IEEE Int. Conf. Cloud Computing and Intelligence Systems (CCIS), Nanjing, China, 2018, pp. 465-469.
[61]
Y. F. Tian, J. W. Yuan, and H. B. Song, Efficient privacy-preserving authentication framework for edge-assisted internet of drones, Journal of Information Security and Applications, vol. 48, p. 102354, 2019.
[62]
Y. M. Saputra, D. T. Hoang, D. N. Nguyen, E. Dutkiewicz, D. Niyato, and I. K. Dong, Distributed deep learning at the edge: A novel proactive and cooperative caching framework for mobile edge networks, IEEE Wireless Communications Letters, vol. 8, no. 4, pp. 1220-1223, 2019.
[63]
M. Bensalem, J. Dizdarevic, and A. Jukan, Modeling of Deep Neural Network (DNN) placement and inference in edge computing, arXiv preprint arXiv: 2001.06901, 2020.
[64]
H. Sadr, M. M. Pedram, and M. Teshnehlab, Multi-view deep network: A deep model based on learning features from heterogeneous neural networks for sentiment analysis, IEEE Access, vol. 8, pp. 86 984-86 997, 2020.
[65]
S. Z. Yang, Z Gong, K Ye, Y. G. Wei, Z. Huang, and Z. H. Huang, EdgeCNN: Convolutional neural network classification model with small inputs for edge computing, arXiv preprint arXiv: Computer Vision and Pattern Recognition, 2019.
[66]
A. Jonathan, M. Uluyol, A. Chandra, and J. Weissman, Ensuring reliability in geo-distributed edge cloud, presented at 2017 Resilience Week (RWS), Wilmington, DE, USA, 2017, pp. 127-132.
[67]
Y. Tian, J. Yuan, S. Yu, and Y. Hou, LEP-CNN: A lightweight edge device assisted privacy-preserving CNN inference solution for IoT, arXiv preprint arXiv: 1901. 04100v1, 2019.
[68]
G. H. Qiao, S. P. Leng, K. Zhang, and Y. J. He, Collaborative task offloading in vehicular edge multi-access networks, IEEE Communications Magazine, vol. 56, no. 8, pp. 48-54, 2018.
[69]
H. El-Sayed and M. Chaqfeh, The deployment of mobile edges in vehicular environments, presented at 2018 Int. Conf. Information Networking (ICOIN), Chiang Mai, Thailand, 2018, pp. 322-324.
[70]
Y. X. Sun, X. Y. Guo, J. H. Song, S. Zhou, Z. Y. Jiang, X. Liu, and Z. S. Niu, Adaptive learning-based task offloading for vehicular edge computing systems, IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3061-3074, 2019.
[71]
Y. Y. Dai, D. Xu, S. Maharjan, and Y. Zhang, Joint load balancing and offloading in vehicular edge computing and networks, IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4377-4387, 2019.
[72]
K. Zhang, Y. M. Mao, S. P. Leng, S. Maharjan, and Y. Zhang, Optimal delay constrained offloading for vehicular edge computing networks, presented at 2017 IEEE Int. Conf. Communications (ICC), Paris, France, 2017, pp. 1-6.
[73]
K. Zhang, Y. M. Mao, S. P. Leng, Y. J. He, and Y. Zhang, Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading, IEEE Vehicular Technology Magazine, vol. 12, no. 2, pp. 36-44, 2017.
[74]
Z. Chang, Z. Y. Zhou, T. Ristaniemi, and Z. S. Niu, Energy efficient optimization for computation offloading in fog computing system, presented at GLOBECOM 2017-2017 IEEE Global Communications Conf., Singapore, 2017, pp. 1-6.
[75]
L. C. Yang, H. L. Zhang, M. Li, J. Guo, and H. Ji, Mobile edge computing empowered energy efficient task offloading in 5G, IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 6398-6409, 2018.
[76]
J. H. Zhao, Q. P. Li, Y. Gong, and K. Zhang, Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks, IEEE Transactions on Vehicular Technology, vol. 68, no. 8, pp. 7944-7956, 2019.
[77]
J. J. Yu, M. X. Zhao, W. T. Li, D. Liu, S. W. Yao, and W. Feng, Joint offloading and resource allocation for time-sensitive multi-access edge computing network, presented at 2020 IEEE Wireless Communications and Networking Conf. (WCNC), Seoul, Korea, 2020, pp. 1-6.
[78]
C. L. Li, M. Y. Song, L. Zhang, W. N. Chen, and Y. L. Luo, Offloading optimization and time allocation for multiuser wireless energy transfer based mobile edge computing system, Mobile Networks and Applications, .
[79]
C. F. Liu, M. Bennis, and H. V. Poor, Latency and reliability-aware task offloading and resource allocation for mobile edge computing, presented at 2017 IEEE Globecom Workshops (GC Wkshps), Singapore, 2017, pp. 1-7.
[80]
P. L. Dai, K. Liu, X. Wu, H. L. Xing, Z. F. Yu, and V. C. S. Lee, A learning algorithm for real-time service in vehicular networks with mobile-edge computing, presented at ICC 2019-2019 IEEE Int. Conf. Communications (ICC), Shanghai, China, 2019, pp. 1-6.
[81]
W. H. Zhan, H. C. Duan, and Q. X. Zhu, Multi-user offloading and resource allocation for vehicular multi-access edge computing, presented at 2019 IEEE Int. Conf. Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS), Shenyang, China, 2019, pp. 50-57.
[82]
K. Gilly, A. Mishev, S. Filiposka and S. Alcaraz, Offloading edge vehicular services in realistic urban environments, IEEE Access, vol. 8, pp. 11 491-11 502, 2020.
[83]
T. X. Tran and D. Pompili, Joint task offloading and resource allocation for multi-server mobile-edge computing networks, IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 856-868, 2019.
[84]
J. F. Lv, J. Y. Xiong, H. Z. Guo, and J. J. Liu, Joint computation offloading and resource configuration in ultra-dense edge computing networks: A deep reinforcement learning solution, presented at 2019 IEEE 90th Vehicular Technology Conf. (VTC2019-Fall), Honolulu, HI, USA, 2019, pp. 1-5.
[85]
J. B. Du, F. R. Yu, X. L. Chu, J. Feng, and G. Y. Lu, Computation offloading and resource allocation in vehicular networks based on dual-side cost minimization, IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1079-1092, 2019.
[86]
T. Dbouk, A. Mourad, H. Otrok, H. Tout, and C. Talhi, A novel ad-hoc mobile edge cloud offering security services through intelligent resource-aware offloading, IEEE Transactions on Network and Service Management, vol. 16, no. 4, pp. 1665-1680, 2019.
[87]
Z. P. Gao, J. Meng, Q. Wang, and Y. Yang, Service migration for deadline-varying user-generated data in mobile edge-clouds, presented at 2018 IEEE World Congress on Services (SERVICES), San Francisco, CA, USA, 2018, pp. 51-52.
[88]
Z. H. Wang, R. T. Gu, G. Zhang, T. Y. Zhao, Y. N. Wang, Y. Wang, and Y. F. Ji, Demonstration of network slicing in mobile edge computing service migration, presented at 2018 Asia Communications and Photonics Conf. (ACP), Hangzhou, China, 2018, pp. 1-3.
[89]
J. Zhou, F. Wu, K. Zhang, Y. M. Mao, and S. P. Leng, Joint optimization of offloading and resource allocation in vehicular networks with mobile edge computing, presented at 2018 10th Int. Conf. Wireless Communications and Signal Processing (WCSP), Hangzhou, China, 2018, pp. 1-6.
[90]
T. X. Tran and D. Pompili, Joint task offloading and resource allocation for multi-server mobile-edge computing networks, IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 856-868, 2019.
[91]
X. L. Xu, Q. H. Huang, X. C. Yin, M. Abbasi, M. R. Khosravi, and L. Y. Qi, Intelligent offloading for collaborative smart city services in edge computing, IEEE Internet of Things Journal, vol. 7, no. 9, pp. 7919-7927, 2020.
[92]
L. Qingqing, J. Peñia Queralta, T. N. Gia, H. Tenhunen, Z. Zou, and T. Westerlund, Visual odometry offloading in internet of vehicles with compression at the edge of the network, presented at 2019 12th Int. Conf. Mobile Computing and Ubiquitous Network (ICMU), Kathmandu, Nepal, 2019, pp. 1-2.
[93]
Z. Deng, Z. Cai, and M. Liang, A multi-hop VANETs-assisted offloading strategy in vehicular mobile edge computing, IEEE Access, .
[94]
R. J. Tan, J. Zhou, H. B. Du, S. C. Shang, and L. Dai, An modeling processing method for video games based on deep reinforcement learning, presented at 2019 IEEE 8th Joint Int. Information Technology and Artificial Intelligence Conf. (ITAIC), Chongqing, China, 2019, pp. 939-942.
[95]
X. Chen, L. Jiao, W. Z. Li, and X. M. Fu, Efficient multi-user computation offloading for mobile-edge cloud computing, IEEE/ACM Transactions on Networking, vol. 24, no. 5, pp. 2795-2808, 2016.
[96]
Q. Qi, J. Y. Wang, Z. Y. Ma, H. F. Sun, Y. F. Cao, L. X. Zhang, and J. X. Liao, Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach, IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4192-4203, 2019.
[97]
Z. L. Ning, P. R. Dong, X. J. Wang, J. J. P. C. Rodrigues, and F. Xia, Deep reinforcement learning for vehicular edge computing: An intelligent offloading system, ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 6, p. 60, 2019.
[98]
E. Nishani and B. Çiço, Computer vision approaches based on deep learning and neural networks: Deep neural networks for video analysis of human pose estimation, presented at 2017 6th Mediterranean Conf. Embedded Computing (MECO), Bar, Montenegro, 2017, pp. 1-4.
[99]
J. L. Li, G. Y. Luo, N. Cheng, Q. Yuan, Z. H. Wu, S. Gao, and Z. H. Liu, An end-to-end load balancer based on deep learning for vehicular network traffic control, IEEE Internet of Things Journal, vol. 6, no. 1, pp. 953-966, 2019.
[100]
Z. L. Ning, X. J. Wang, and J. Huang, Mobile edge computing-enabled 5G vehicular networks: Toward the integration of communication and computing, IEEE Vehicular Technology Magazine, vol. 14, no. 1, pp. 54-61, 2019.
[101]
Z. P. Gao, Q. D. Jiao, K. L. Xiao, Q. Wang, Z. J. Mo, and Y. Yang, Deep reinforcement learning based service migration strategy for edge computing, presented at 2019 IEEE Int. Conf. Service-Oriented System Engineering (SOSE), San Francisco, CA, USA, 2019, pp. 116-121.
[102]
Y. X. Guo, Z. L. Ning, and R. Kwok, Deep reinforcement learning based traffic offloading scheme for vehicular networks, presented at 2019 IEEE 5th Int. Conf. Computer and Communications (ICCC), Chengdu, China, 2019, pp. 81-85.
[103]
J. A. Onieva, R. Rios, R. Roman, and J. Lopez, Edge-assisted vehicular networks security, IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8038-8045, 2019.
[104]
H. J. Ji, O. Alfarraj, and A. Tolba, Artificial intelligence-empowered edge of vehicles: Architecture, enabling technologies, and applications, IEEE Access, vol. 8, pp. 61 020-61 034, 2020.
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Publication history

Received: 28 June 2020
Revised: 14 July 2020
Accepted: 02 August 2020
Published: 29 September 2021
Issue date: April 2022

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© The author(s) 2022

Acknowledgements

This research was supported by the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps (No. 2020DB005), the National Key R&D Program of China (No. 2019YFE0190500), the National Natural Science Foundation of China (Nos. 61702442, 61862065, and 61702277), the Application Basic Research Project in Yunnan Province (No. 2018FB105), the Major Project of Science and Technology of Yunnan Province (Nos. 202002AD080002 and 2019ZE005), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund.

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