Many-Objective Edge Computing Server Deployment Optimization for Vehicle Road Cooperation
Source abstract
Abstract
In the Internet of Vehicles (IoV), vehicles need to process a large amount of perception data to support tasks such as road navigation and autonomous driving. However, their computational resources are limited. Therefore, it is necessary to explore the combination of vehicle–road cooperation with edge computing. Roadside units (RSUs) can provide data access services for vehicles, and deploying edge servers on RSUs can improve the data processing capability in IoV environments and ensure the sustainability of vehicle communications, thus supporting complex traffic scenarios more effectively. In this work, we study the deployment of RSUs in vehicle–road cooperative systems. To balance the deployment cost of RSUs and the quality of service (QoS) of vehicle users, we propose an RSU deployment optimization model with six objectives, including time delay, energy consumption and security when vehicles offload their tasks to RSUs, as well as load balancing and the number and communication coverage area of RSUs. In addition, we propose a Wasserstein generative adversarial network (WGAN)-based Two_Arch2 (WGTwo_Arch2) to solve this many-objective optimization problem to better ensure the diversity and convergence of the solutions. In addition, a polynomial variation strategy based on Lecy’s flight mechanism and a diversity archive selection strategy with an adaptive Lp-norm are also proposed to balance the exploratory and exploitative capabilities of the algorithm. The effectiveness of the proposed algorithm WGTwo_Arch2 for 6-objective RSU deployment optimization is verified by comparisons with five different algorithms.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicular Ad Hoc Networks (VANETs) · IoT and Edge/Fog Computing · Autonomous Vehicle Technology and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China
References (31)
Show source-provided references
1. Optimizing task offloading and resource allocation in vehicular edge computing based on heterogeneous cellular networks10.1109/TVT.2023.3345364 ↗
2. Mobile edge computing-enabled internet of vehicles: Toward energy-efficient scheduling10.1109/MNET.2019.1800309 ↗
3. A multi-objective Roadside Units deployment strategy based on reliable coverage analysis in Internet of Vehicles10.1016/j.adhoc.2024.103630 ↗
4. Infrastructure deployment in vehicular communication networks using a parallel multiobjective evolutionary algorithm10.1002/int.21890 ↗
5. Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading10.1109/MVT.2017.2668838 ↗
6. Combining capital and operating expenditure costs in vehicular roadside unit placement10.1109/TVT.2017.2665480 ↗
7. An RSU deployment strategy based on traffic demand in vehicular ad hoc networks (VANETs)10.1109/JIOT.2021.3111048 ↗
8. Drld-sp: A deep-reinforcement-learning-based dynamic service placement in edge-enabled internet of vehicles10.1109/JIOT.2021.3110913 ↗
9. Lin, M., Huo, J., Wang, L., Yao, G., Chen, Y., and Lu, Z. (2024, January 14–17). A Multi-Objective Optimization Approach for Roadside Unit Deployment Strategy in IoV. Proceedings of the 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates.10.1109/WCNC57260.2024.10570692 ↗
10. Zhang, L., Wang, L., Zhang, L., Zhang, X., and Sun, D. (2023). An RSU deployment scheme for vehicle-infrastructure cooperated autonomous driving. Sustainability, 15.10.3390/su15043847 ↗
11. Secgreen: Secrecy ensured power optimization scheme for software-defined connected iov10.1109/TMC.2021.3116954 ↗
12. Two_Arch2: An improved two-archive algorithm for many-objective optimization10.1109/TEVC.2014.2350987 ↗
13. Praditwong, K., and Yao, X. (2006, January 3–6). A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm. Proceedings of the 2006 International Conference on Computational Intelligence and Security, Guangzhou, China.10.1109/ICCIAS.2006.294139 ↗
14. Zitzler, E., and Künzli, S. (2004, January 18–22). Indicator-based selection in multiobjective search. Proceedings of the International Conference on Parallel Problem Solving from Nature, Birmingham, UK.10.1007/978-3-540-30217-9_84 ↗
15. A combined genetic adaptive search (GeneAS) for engineering design
16. Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., and Bengio, Y. (2014, January 8–13). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada.
17. Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 9–15). Wasserstein generative adversarial networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA.
18. Joint optimization of coverage and reliability for application placement in mobile edge computing10.1109/TSC.2023.3296742 ↗
19. A fast and elitist multiobjective genetic algorithm: NSGA-II10.1109/4235.996017 ↗
20. Kumrai, T., Ota, K., Dong, M., and Champrasert, P. (May, January 27). RSU placement optimization in vehicular participatory sensing networks. Proceedings of the 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Toronto, ON, Canada.10.1109/INFCOMW.2014.6849228 ↗
21. Multiobjective differential evolution with discrete elite guide in internet of vehicles roadside unit deployment10.1155/2021/4207130 ↗
22. Guerna, A., and Bitam, S. (2019, January 26–27). GICA: An evolutionary strategy for roadside units deployment in vehicular networks. Proceedings of the 2019 International Conference on Networking and Advanced Systems (ICNAS), Annaba, Algeria.10.1109/ICNAS.2019.8807882 ↗
23. A cost-effective strategy for road-side unit placement in vehicular networks10.1109/TCOMM.2012.062512.100550 ↗
24. Ghosh, D., Katehara, H., Rawlley, O., Gupta, S., Arulselvan, N., and Chamola, V. (2022, January 14–17). Artificial intelligence-empowered optimal roadside unit (RSU) deployment mechanism for internet of vehicles (IoV). Proceedings of the 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Belfast, UK.10.1109/WoWMoM54355.2022.00077 ↗
25. Deep reinforcement learning-based multi-objective edge server placement in Internet of Vehicles10.1016/j.comcom.2022.02.011 ↗
26. Dynamic server placement in edge computing toward internet of vehicles10.1016/j.comcom.2021.07.021 ↗
27. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints10.1109/TEVC.2013.2281535 ↗
28. Nath, S., Li, Y., Wu, J., and Fan, P. (2020, January 7–11). Multi-user multi-channel computation offloading and resource allocation for mobile edge computing. Proceedings of the ICC 2020—2020 IEEE International Conference on Communications (ICC), Dublin, Ireland.10.1109/GLOBECOM42002.2020.9348161 ↗
29. IGD indicator-based evolutionary algorithm for many-objective optimization problems10.1109/TEVC.2018.2791283 ↗
30. Tri-goal evolution framework for constrained many-objective optimization
31. Hyperplane assisted evolutionary algorithm for many-objective optimization problems10.1109/TCYB.2019.2899225 ↗