Efficient vehicular networks offloading using Hybrid Localization Algorithm and Deep Reinforcement Learning

Research Article
Open access

Efficient vehicular networks offloading using Hybrid Localization Algorithm and Deep Reinforcement Learning

Yankai Peng 1* , Zhiyuan Wang 2 , Hailin Li 3 , Ning Dong 4
  • 1 University of Electronic Science and Technology of China    
  • 2 Zhejiang University    
  • 3 Beijing University of Posts and Telecommunications    
  • 4 Huaqiao University    
  • *corresponding author 2020190902027@std.uestc.edu.cn
ACE Vol.44
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-327-2
ISBN (Online): 978-1-83558-328-9

Abstract

In the context of growing urbanization and increased vehicular traffic, the demand for efficient computation and location-based services is paramount. This paper proposes a pioneering solution to address the challenges of precise location services in Vehicular Networks within urban settings. The system combines a Hybrid Localization Algorithm (HLA) that integrates multiple methods for improved location accuracy with Deep Reinforcement Learning (DRL) for intelligent and adaptive offloading decisions based on real-time traffic conditions. Extensive simulations demonstrate the effectiveness of our approach in reducing response times, optimizing offloading strategies, and alleviating the burden of urban peak vehicle navigation pressure. This research paves the way for enhanced location-based services and intelligent transportation systems in urban areas.

Keywords:

Hybrid Localization Algorithm, Vehicular Networks Offloading, Deep Reinforcement Learning

Peng,Y.;Wang,Z.;Li,H.;Dong,N. (2024). Efficient vehicular networks offloading using Hybrid Localization Algorithm and Deep Reinforcement Learning. Applied and Computational Engineering,44,160-171.
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References

[1]. F. Wang, G. Yin, L. Xu, W. Zhuang, Y. Liu and J. Liang, "Distance-Based Cooperative Localization of Connected Vehicles Via Convex Relaxation Under Extreme Environments," 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI), Tianjin, China, 2021, pp. 1-5, doi: 10.1109/CVCI54083.2021.9661208.

[2]. J. Li and N. Ma, "Design of Vehicle Cooperative localization System Based on Cooperative Communication Switching Strategy," 2021 17th International Conference on Computational Intelligence and Security (CIS), Chengdu, China, 2021,pp. 237-241, doi: 10.1109/CIS54983.2021.00057.

[3]. F. Wen and T. Svensson, "Collaborative Localization with Truth Discovery for Heterogeneous and Dynamic Vehicular Networks," 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020, pp. 1-5, doi: 10.1109/VTC2020-Spring48590.2020.9128766.

[4]. [4] X. Chu et al., "Joint Vehicular Localization and Reflective Mapping Based on Team Channel-SLAM," in IEEE Transactions on Wireless Communications, vol. 21, no. 10, pp. 7957-7974, Oct. 2022, doi: 10.1109/TWC.2022.3163071.

[5]. S. Wen, J. Chen, F. R. Yu, F. Sun, Z. Wang and S. Fan, "Edge Computing-Based Collaborative Vehicles 3D Mapping in Real Time," in IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 12470-12481, Nov. 2020, doi: 10.1109/TVT.2020.3019061.

[6]. L. Gao, L. Xiong, X. Xia, Y. Lu, Z. Yu and A. Khajepour, "Improved Vehicle Localization Using On-Board Sensors and Vehicle Lateral Velocity," in IEEE Sensors Journal, vol. 22, no. 7, pp. 6818-6831, 1 April1, 2022, doi: 10.1109/JSEN.2022.3150073.

[7]. Z. Wang, J. Fang, X. Dai, H. Zhang and L. Vlacic, "Intelligent Vehicle Self-Localization Based on Double-Layer Features and Multilayer LIDAR," in IEEE Transactions on Intelligent Vehicles, vol. 5, no. 4, pp. 616-625, Dec. 2020, doi: 10.1109/TIV.2020.3003699.

[8]. [8] Z. Wu and D. Yan, "Deep reinforcement learning-based computation offloading for 5G vehicle-aware multi-access edge computing network," in China Communications, vol. 18, no. 11, pp. 26-41, Nov. 2021, doi: 10.23919/JCC.2021.11.003.

[9]. [9] X. Peng et al., "Deep Reinforcement Learning for Shared Offloading Strategy in Vehicle Edge Computing," in IEEE Systems Journal, vol. 17, no. 2, pp. 2089-2100, June 2023, doi: 10.1109/JSYST.2022.3190926.

[10]. X. Hou, Y. Li, M. Chen, D. Wu, D. Jin and S. Chen, "Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures," in IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 3860-3873, June 2016, doi: 10.1109/TVT.2016.2532863.

[11]. J. Park and K. Chung, "Collaborative Computation Offloading Scheme Based on Deep Reinforcement Learning," 2023 International Conference on Information Networking (ICOIN), Bangkok, Thailand, 2023, pp. 110-115, doi: 10.1109/ICOIN56518.2023.10048957.

[12]. [12] C. Wu, Z. Huang and Y. Zou, "Delay Constrained Hybrid Task Offloading of Internet of Vehicle: A Deep Reinforcement Learning Method," in IEEE Access, vol. 10, pp. 102778-102788, 2022, doi: 10.1109/ACCESS.2022.3206359.

[13]. L. Lu, X. Li, J. Sun and Z. Yang, "Cooperative Computation Offloading and Resource Management for Vehicle Platoon: A Deep Reinforcement Learning Approach," 2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), Hainan, China, 2022, pp. 1641-1648, doi: 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00249.

[14]. G. Ma, X. Wang, M. Hu, W. Ouyang, X. Chen and Y. Li, "DRL-Based Computation Offloading With Queue Stability for Vehicular-Cloud-Assisted Mobile Edge Computing Systems," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 4, pp. 2797-2809, April 2023, doi: 10.1109/TIV.2022.3225147.

Cite this article

Peng,Y.;Wang,Z.;Li,H.;Dong,N. (2024). Efficient vehicular networks offloading using Hybrid Localization Algorithm and Deep Reinforcement Learning. Applied and Computational Engineering,44,160-171.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-327-2(Print) / 978-1-83558-328-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.44
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. F. Wang, G. Yin, L. Xu, W. Zhuang, Y. Liu and J. Liang, "Distance-Based Cooperative Localization of Connected Vehicles Via Convex Relaxation Under Extreme Environments," 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI), Tianjin, China, 2021, pp. 1-5, doi: 10.1109/CVCI54083.2021.9661208.

[2]. J. Li and N. Ma, "Design of Vehicle Cooperative localization System Based on Cooperative Communication Switching Strategy," 2021 17th International Conference on Computational Intelligence and Security (CIS), Chengdu, China, 2021,pp. 237-241, doi: 10.1109/CIS54983.2021.00057.

[3]. F. Wen and T. Svensson, "Collaborative Localization with Truth Discovery for Heterogeneous and Dynamic Vehicular Networks," 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020, pp. 1-5, doi: 10.1109/VTC2020-Spring48590.2020.9128766.

[4]. [4] X. Chu et al., "Joint Vehicular Localization and Reflective Mapping Based on Team Channel-SLAM," in IEEE Transactions on Wireless Communications, vol. 21, no. 10, pp. 7957-7974, Oct. 2022, doi: 10.1109/TWC.2022.3163071.

[5]. S. Wen, J. Chen, F. R. Yu, F. Sun, Z. Wang and S. Fan, "Edge Computing-Based Collaborative Vehicles 3D Mapping in Real Time," in IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 12470-12481, Nov. 2020, doi: 10.1109/TVT.2020.3019061.

[6]. L. Gao, L. Xiong, X. Xia, Y. Lu, Z. Yu and A. Khajepour, "Improved Vehicle Localization Using On-Board Sensors and Vehicle Lateral Velocity," in IEEE Sensors Journal, vol. 22, no. 7, pp. 6818-6831, 1 April1, 2022, doi: 10.1109/JSEN.2022.3150073.

[7]. Z. Wang, J. Fang, X. Dai, H. Zhang and L. Vlacic, "Intelligent Vehicle Self-Localization Based on Double-Layer Features and Multilayer LIDAR," in IEEE Transactions on Intelligent Vehicles, vol. 5, no. 4, pp. 616-625, Dec. 2020, doi: 10.1109/TIV.2020.3003699.

[8]. [8] Z. Wu and D. Yan, "Deep reinforcement learning-based computation offloading for 5G vehicle-aware multi-access edge computing network," in China Communications, vol. 18, no. 11, pp. 26-41, Nov. 2021, doi: 10.23919/JCC.2021.11.003.

[9]. [9] X. Peng et al., "Deep Reinforcement Learning for Shared Offloading Strategy in Vehicle Edge Computing," in IEEE Systems Journal, vol. 17, no. 2, pp. 2089-2100, June 2023, doi: 10.1109/JSYST.2022.3190926.

[10]. X. Hou, Y. Li, M. Chen, D. Wu, D. Jin and S. Chen, "Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures," in IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 3860-3873, June 2016, doi: 10.1109/TVT.2016.2532863.

[11]. J. Park and K. Chung, "Collaborative Computation Offloading Scheme Based on Deep Reinforcement Learning," 2023 International Conference on Information Networking (ICOIN), Bangkok, Thailand, 2023, pp. 110-115, doi: 10.1109/ICOIN56518.2023.10048957.

[12]. [12] C. Wu, Z. Huang and Y. Zou, "Delay Constrained Hybrid Task Offloading of Internet of Vehicle: A Deep Reinforcement Learning Method," in IEEE Access, vol. 10, pp. 102778-102788, 2022, doi: 10.1109/ACCESS.2022.3206359.

[13]. L. Lu, X. Li, J. Sun and Z. Yang, "Cooperative Computation Offloading and Resource Management for Vehicle Platoon: A Deep Reinforcement Learning Approach," 2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), Hainan, China, 2022, pp. 1641-1648, doi: 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00249.

[14]. G. Ma, X. Wang, M. Hu, W. Ouyang, X. Chen and Y. Li, "DRL-Based Computation Offloading With Queue Stability for Vehicular-Cloud-Assisted Mobile Edge Computing Systems," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 4, pp. 2797-2809, April 2023, doi: 10.1109/TIV.2022.3225147.

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