An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

YAO Liang; XU Xiaolong; DOU Wanchun; Bilal Muhammad

doi:10.23919/cje.2023.00.111

Volume 33 Issue 4

Jul. 2024

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Liang YAO, Xiaolong XU, Wanchun DOU, et al., “An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV,” Chinese Journal of Electronics, vol. 33, no. 4, pp. 910–919, 2024 doi: 10.23919/cje.2023.00.111

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Liang YAO, Xiaolong XU, Wanchun DOU, et al., “An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV,” Chinese Journal of Electronics, vol. 33, no. 4, pp. 910–919, 2024 doi: 10.23919/cje.2023.00.111

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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

doi: 10.23919/cje.2023.00.111

YAO Liang^1
,,
XU Xiaolong^{1, 2
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DOU Wanchun^2
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Bilal Muhammad^3
,

1.
School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210044, China
3.
Department of Computer and Electronics Systems Engineering, Hankuk University of Foreign Studies, Yongin-si 17035, Korea

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Author Bio:
Liang YAO is currently pursuing the M.S. degree in software engineering with Nanjing University of Information Science and Technology, Nanjing, China. His areas of interest are mobile computing, big data, cloud computing, and machine learning. (Email: nuistlyao@gmail.com)

Xiaolong XU received the Ph.D. degree in computer science and technology from Nanjing University, Nanjing, China, in 2016. He was a Research Scholar with Michigan State University, East Lansing, MI, USA, from April 2017 to May 2018. He is currently an Associate Professor with the School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China. He received the Best Paper Award from the IEEE CBD 2016, the TOP citation award from the Computational Intelligence journal in 2019, and the Distinguished Paper Award and the Best Student Paper of EAI Cloudcomp 2019. His research interests include edge computing, the Internet of things, cloud computing, and big data.(Email: xlxu@ieee.org)

Wanchun DOU is currently a Full Professor at the State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China. His research interests include workflow, cloud computing, and service computing. (Email: douwc@nju.edu.cn)

Muhammad Bilal received the B.S. degree in computer systems engineering from University of Engineering and Technology, Peshawar, Pakistan, the M.S. degree in computer engineering from Chosun University, Gwangju, South Korea, and the Ph.D. degree in information and communication network engineering from the School of Electronics and Telecommunications Research Institute, Korea University of Science and Technology, Daejeon, South Korea. He is an Assistant Professor in computer science with the Department of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin-si, South Korea. Prior to joining Hankuk University of Foreign Studies, Yongin-si, South Korea, he was a Postdoctoral Research Fellow with the Smart Quantum Communication Center, Korea University, Seoul, South Korea. His primary research interests include design and analysis of network protocols, network architecture, network security, IoT, named data networking, and future Internet.Dr. Bilal has served as a reviewer of various international journals including IEEE Systems Journal, IEEE Access, IEEE Communication Letters, IEEE Transaction on Network and Service Management, Journal of Network and Computer Applications, Personal and Ubiquitous Computing, and International Journal of Communication Systems. He has also served as a Program Committee Member on many international conferences.(Email: m.bilal@ieee.ac.kr)
Corresponding author: Email: xlxu@ieee.org
Received Date: 2023-03-31
Accepted Date: 2023-09-07

Available Online: 2024-02-04

Publish Date: 2024-07-05

Abstract

Abstract

As a pivotal enabler of intelligent transportation system (ITS), Internet of vehicles (IoV) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive, and privacy-aware vehicular applications in IoV result in the transformation from cloud computing to edge computing, which enables tasks to be offloaded to edge nodes (ENs) closer to vehicles for efficient execution. In ITS environment, however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.
- Intelligent transportation system,
- Deep reinforcement learning,
- Edge computing,
- Privacy protection

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References(29)

References

[1]	N. Zhang, T. Han, M. Dianati, et al., “Guest editorial special issue on space-air-ground integrated networks for intelligent transportation systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2701–2704, 2022. doi: 10.1109/TITS.2022.3153079
[2]	J. W. Huang, J. Y. Wan, B. F. Lv, et al., “Joint computation offloading and resource allocation for edge-cloud collaboration in internet of vehicles via deep reinforcement learning,” IEEE Systems Journal, vol. 17, no. 2, pp. 2500–2511, 2023. doi: 10.1109/JSYST.2023.3249217
[3]	C. F. Wang, Y. K. Lin, and J. C. Chen, “A cooperative image object recognition framework and task offloading optimization in edge computing,” Journal of Network and Computer Applications, vol. 204, article no. 103404, 2022. doi: 10.1016/j.jnca.2022.103404
[4]	Y. Chen, J. Zhao, Y. Wu, et al., “QoE-Aware decentralized task offloading and resource allocation for end-edge-cloud systems: A game-theoretical approach,” IEEE Transactions on Mobile Computing, vol. 23, no. 1, pp. 769–784, 2024. doi: 10.1109/TMC.2022.3223119
[5]	H. T. Cao, S. Garg, G. Kaddoum, et al., “Intelligent virtual resource allocation of QoS-Guaranteed slices in B5G-enabled VANETs for intelligent transportation systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 19704–19713, 2022. doi: 10.1109/TITS.2022.3178267
[6]	Y. Chen, J. T. Hu, J. Zhao, et al., “QoS-Aware computation offloading in LEO satellite edge computing for IoT: A game-theoretical approach,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.412.
[7]	G. Li, L. Liu, Z. P. Liang, et al., “Memetic algorithm based on community detection for energy-efficient service migration optimization in 5G mobile edge computing,” in 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland, pp. 1–7, 2021.
[8]	C. Hou and Q. C. Zhao, “Optimal task-offloading control for edge computing system with tasks offloaded and computed in sequence,” IEEE Transactions on Automation Science and Engineering, vol. 20, no. 2, pp. 1378–1392, 2023. doi: 10.1109/TASE.2022.3176745
[9]	J. W. Huang, H. Gao, S. H. Wan, et al., “AoI-aware energy control and computation offloading for industrial IoT,” Future Generation Computer Systems, vol. 139, pp. 29–37, 2023.
[10]	K. Y. Zhang, X. L. Gui, D. W. Ren, et al., “Energy–latency tradeoff for computation offloading in UAV-assisted multiaccess edge computing system,” IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6709–6719, 2021. doi: 10.1109/JIOT.2020.2999063
[11]	W. Z. Wang, Q. Chen, Z. M. Yin, et al., “Blockchain and PUF-based lightweight authentication protocol for wireless medical sensor networks,” IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8883–8891, 2022. doi: 10.1109/JIOT.2021.3117762
[12]	Y. Ding, K. L. Li, C. B. Liu, et al., “Budget-constrained service allocation optimization for mobile edge computing,” IEEE Transactions on Services Computing, vol. 16, no. 1, pp. 147–161, 2023. doi: 10.1109/TSC.2021.3133547
[13]	Y. Chen, J. Zhao, J. T. Hu, et al., “Distributed task offloading and resource purchasing in NOMA-enabled mobile edge computing: Hierarchical game theoretical approaches,” ACM Transactions on Embedded Computing Systems, vol. 23, no. 1, article no. 2, 2024. doi: 10.1145/3597023
[14]	Z. H. Tian, X. S. Gao, S. Su, et al., “Evaluating reputation management schemes of internet of vehicles based on evolutionary game theory,” IEEE Transactions on Vehicular Technology, vol. 68, no. 6, pp. 5971–5980, 2019. doi: 10.1109/TVT.2019.2910217
[15]	Z. Y. Wang, T. Schaul, M. Hessel, et al., “Dueling network architectures for deep reinforcement learning,” in Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, NY, USA, pp. 1995–2003, 2016.
[16]	X. F. He, T. H. Li, R. C. Jin, et al., “Delay-optimal coded offloading for distributed edge computing in fading environments,” IEEE Transactions on Wireless Communications, vol. 21, no. 12, pp. 10796–10808, 2022. doi: 10.1109/TWC.2022.3187427
[17]	C. Y. Yi, J. Cai, and Z. Su, “A multi-user mobile computation offloading and transmission scheduling mechanism for delay-sensitive applications,” IEEE Transactions on Mobile Computing, vol. 19, no. 1, pp. 29–43, 2020. doi: 10.1109/TMC.2019.2891736
[18]	H. Z. Guo and J. J. Liu, “Collaborative computation offloading for multiaccess edge computing over fiber-wireless networks,” IEEE Transactions on Vehicular Technology, vol. 67, no. 5, pp. 4514–4526, 2018. doi: 10.1109/TVT.2018.2790421
[19]	S. T. Guo, J. D. Liu, Y. Y. Yang, et al., “Energy-efficient dynamic computation offloading and cooperative task scheduling in mobile cloud computing,” IEEE Transactions on Mobile Computing, vol. 18, no. 2, pp. 319–333, 2019. doi: 10.1109/TMC.2018.2831230
[20]	J. H. Zhao, Q. P. Li, Y. Gong, et al., “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. doi: 10.1109/TVT.2019.2917890
[21]	X. Y. Qiu, W. K. Zhang, W. H. Chen, et al., “Distributed and collective deep reinforcement learning for computation offloading: A practical perspective,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 5, pp. 1085–1101, 2021. doi: 10.1109/TPDS.2020.3042599
[22]	A. M. Seid, G. O. Boateng, B. Mareri, et al., “Multi-agent DRL for task offloading and resource allocation in multi-UAV enabled IoT edge network,” IEEE Transactions on Network and Service Management, vol. 18, no. 4, pp. 4531–4547, 2021. doi: 10.1109/TNSM.2021.3096673
[23]	I. A. Elgendy, A. Muthanna, M. Hammoudeh, et al., “Advanced deep learning for resource allocation and security aware data offloading in industrial mobile edge computing,” Big Data, vol. 9, no. 4, pp. 265–278, 2021. doi: 10.1089/big.2020.0284
[24]	W. Z. Zhang, I. A. Elgendy, M. Hammad, et al., “Secure and optimized load balancing for multitier IoT and edge-cloud computing systems,” IEEE Internet of Things Journal, vol. 8, no. 10, pp. 8119–8132, 2021. doi: 10.1109/JIOT.2020.3042433
[25]	I. A. Elgendy, W. Z. Zhang, Y. M. Zeng, et al., “Efficient and secure multi-user multi-task computation offloading for mobile-edge computing in mobile IoT networks,” IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 2410–2422, 2020. doi: 10.1109/TNSM.2020.3020249
[26]	I. A. Elgendy, W. Z. Zhang, Y. C. Tian, et al., “Resource allocation and computation offloading with data security for mobile edge computing,” Future Generation Computer Systems, vol. 100, pp. 531–541, 2019.
[27]	M. Khan and N. Munir, “A novel image encryption technique based on generalized advanced encryption standard based on field of any characteristic,” Wireless Personal Communications, vol. 109, no. 2, pp. 849–867, 2019. doi: 10.1007/s11277-019-06594-6
[28]	V. Mnih, K. Kavukcuoglu, D. Silver, et al., “Playing Atari with deep reinforcement learning,” arXiv preprint, arXiv: 1312.5602, 2013.
[29]	H. J. Wu, J. Zhang, Z. P. Cai, et al., “Toward energy-aware caching for intelligent connected vehicles,” IEEE Internet of Things Journal, vol. 7, no. 9, pp. 8157–8166, 2020. doi: 10.1109/JIOT.2020.2980954