Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
In the realm of autonomous driving, cooperative perception serves as a crucial technology for mitigating the inherent constraints of individual vehicle’s perception. To enable cooperative perception, vehicle-to-vehicle (V2V) communication plays an indispensable role. Unfortunately, owing to weak virus protection in V2V networks, the emergence and widespread adoption of V2V communications have also created fertile soil for the breeding and rapid spreading of worms. To stimulate vehicles to participate in cooperative perception while blocking the spreading of worms through V2V communications, we design an incentive mechanism, in which the utility of each sensory data requester and that of each sensory data provider are defined, respectively, to maximize the total utility of all the vehicles. To deal with the highly non-convex problem, we propose a pairing and resource allocation (PRA) scheme based on the Stackelberg game theory. Specifically, we decompose the problem into two subproblems. The subproblem of maximizing the utility of the requester is solved via a two-stage iterative algorithm, while the subproblem of maximizing the utility of the provider is addressed using the linear search method. The results demonstrate that our proposed PRA approach addresses the challenges of cooperative perception and worm spreading while efficiently converging to the Stackelberg equilibrium point, jointly maximizing the utilities for both the requester and the provider.
N. Wu, X. Wang, B. Lin and K. Zhang, A CNN-Based End-to-End Learning Framework Toward Intelligent Communication Systems, IEEE Access., vol. 7, pp. 110197–110204, 2019.
M. K. Abdel-Aziz, C. Perfecto, S. Samarakoon, M. Bennis and W. Saad, Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning, IEEE Trans. Commun., vol. 70, no. 2, pp. 891–903, 2022.
E. Arnold, M. Dianati, R. de Temple and S. Fallah, Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 1852–1864, 2022.
Q. Chen, S. Tang, Q. Yang and S. Fu, Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds, in Proc. IEEE 39th Int. Conf. Distrib. Comput. Syst. (ICDCS), Virtual, pp. 514–524, 2019.
H. J. Zhang, L. Z. Feng, X. N. Liu, K. P. Long and G. K. Karagiannidis, User Scheduling and Task Offloading in Multi-Tier Computing 6G Vehicular Network, IEEE J. Sel. Areas Commun., vol. 41, no. 2, pp. 446–456, 2023.
T. T. Deng, X. X. Liu, H. Zhou and V. C. M. Leung, Global Resource Allocation for High Throughput and Low Delay in High-Density VANETs, IEEE Trans. Wireless Commun., vol. 21, no. 11, pp. 9509–9518, 2022.
Y. Ju, H. Y. Wang, Y. C. Chen, T. -X. Zheng, Q. Q. Pei, J. H. Yuan and N. Al-Dhahir, Deep Reinforcement Learning Based Joint Beam Allocation and Relay Selection in mmWave Vehicular Networks, IEEE Trans. Commun., vol. 71, no. 4, pp. 1997–2012, 2023.
H. W. Cho, Y. Cui and J. Lee, Energy-Efficient Cooperative Offloading for Edge Computing-Enabled Vehicular Networks, IEEE Trans. Wireless Commun., vol. 21, no. 12, pp. 10709–10723, 2022.
L. M. Xu, Z. X. Yang, H. Q. Wu, Y. R. Zhang, Y. H. Wang, L. Wang and Z. Han, Socially Driven Joint Optimization of Communication, Caching, and Computing Resources in Vehicular Networks, IEEE Trans. Wireless Commun., vol. 21, no. 1, pp. 461–472, 2022.
M. K. Abdel-Aziz, C. Perfecto, S. Samarakoon, M. Bennis and W. Saad, Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning, IEEE Trans. Commun., vol. 70, no. 2, pp. 891–903, 2022.
H. Z. Xiao, J. Zhao, J. Feng, L. Liu, Q. Q. Pei and W. S. Shi, Joint Optimization of Security Strength and Resource Allocation for Computation Offloading in Vehicular Edge Computing, IEEE Trans. Wireless Commun., vol. 22, no. 12, pp. 8751–8765, 2023.
N. Guizani and A. Ghafoor, A Network Function Virtualization System for Detecting Malware in Large IoT Based Networks, IEEE J. Sel. Areas Commun., vol. 38, no. 6, pp. 1218–1228, 2020.
N. Guizani, A. Elghariani, J. Kobes, and A. Ghafoor, Effects of social network structure on epidemic disease spread dynamics with application to ad hoc networks, IEEE Netw., vol. 33, no. 3, pp. 139–145, 2019.
A. Dabarov, M. Sharipov, A. Dadlani, M. S. Kumar, W. Saad and C. S. Hong, Heterogeneous Projection of Disruptive Malware Prevalence in Mobile Social Networks, IEEE Commun. Lett., vol. 24, no. 8, pp. 1673–1677, 2023.
J. Xu, L. X. Chen, K. Liu and C. Shen, Designing Security-Aware Incentives for Computation Offloading via Device-to-Device Communication, IEEE Trans. Wireless Commun., vol. 17, no. 9, pp. 6053–6066, 2018.
L. T. Zhang and J. Xu, Differential Security Game in Heterogeneous Device-to-Device Offloading Network Under Epidemic Risks, IEEE Trans. Netw. Sci. Eng., vol. 7, no. 3, pp. 1852–1861, 2020.
A. Boukerche and Q. Zhang, Countermeasures against Worm Spreading: A New Challenge for Vehicular Networks, ACM Comput. Surv., vol. 52, no. 2, pp. 1–25, 2020.
B. Lin, X. Wang, W. Yuan and N. Wu, A Novel OFDM Autoencoder Featuring CNN-Based Channel Estimation for Internet of Vessels, IEEE Internet Things J., vol. 7, no. 8, pp. 7601–7611, 2020.
Z. Xiao, J. M. Shu, H. B. Jiang, G. Y. Min, H. Y. Chen and Z. Han, Perception Task Offloading With Collaborative Computation for Autonomous Driving, IEEE J. Sel. Areas Commun., vol. 41, no. 2, pp. 457–473, 2023.
M. H. Dai, Z. S. Luo, Y. Wu, L. P. Qian, B. Lin and Z. Su, Incentive Oriented Two-Tier Task Offloading Scheme in Marine Edge Computing Networks: A Hybrid Stackelberg-Auction Game Approach, IEEE Trans. Wireless Commun., vol. 22, no. 12, pp. 8603–8619, 2023.
S. C. Xia, Z. X. Yao, Y. Li and S. W. Mao, Online Distributed Offloading and Computing Resource Management With Energy Harvesting for Heterogeneous MEC-Enabled IoT, IEEE Trans. Wireless Commun., vol. 20, no. 10, pp. 6743–6757, 2021.
Z. Y. Shi, X. Z. Xie, H. B. Lu, H. L. Yang, J. Cai and Z. G. Ding, Deep Reinforcement Learning-Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications, IEEE Trans. Commun., vol. 70, no. 5, pp. 3110–3125, 2022.
K. D. Wang, Z. G. Ding, D. K. C. So and G. K. Karagiannidis, Stackelberg Game of Energy Consumption and Latency in MEC Systems With NOMA, IEEE Trans. Commun., vol. 69, no. 4, pp. 2191–2206, 2021.
M. Wang, S. Shi, D. Y. Zhang, C. Y. Wu and Y. Wang, Joint Computation Offloading and Resource Allocation for MIMO-NOMA Assisted Multi-User MEC Systems, IEEE Trans. Commun., vol. 71, no. 7, pp. 4360–4376, 2023.
J. N. Zou, C. L. Li, C. C. Zhai, H. K. Xiong and E. Steinbach, Joint Pricing and Cache Placement for Video Caching: A Game Theoretic Approach, IEEE J. Sel. Areas Commun., vol. 37, no. 7, pp. 1566–1583, 2019.
L. Liang, H. Ye and G. Y. Li, Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning, IEEE J. Sel. Areas Commun., vol. 37, no. 10, pp. 2282–2292, 2019.
W. Y. Feng, S. Y. Lin, N. Zhang, G. P. Wang, B. Ai and L. Cai, Joint C-V2X Based Offloading and Resource Allocation in Multi-Tier Vehicular Edge Computing System, IEEE J. Sel. Areas Commun., vol. 41, no. 2, pp. 432–445, 2023.
Z. J. Nan, S. Zhou, Y. J. Jia and Z. S. Niu, Joint Task Offloading and Resource Allocation for Vehicular Edge Computing With Result Feedback Delay, IEEE Trans. Wireless Commun., vol. 22, no. 10, pp. 6547–6561, 2023.
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).