Abstract
Due to the complexity and security requirements of edge computing environments and the limited resources of terminals, secure offloading in multi-access edge computing (MEC) networks has emerged as a critical and urgent research area. However, many studies on task offloading often ignore the necessary balance between security requirements and efficiency. To address this issue, we propose a Secure Offloading Strategy Considering Computational Acceleration, named SOCA, designed to bolster security while preserving offloading efficiency. Specifically, the secure offloading problem is modeled as a multi-objective optimization problem by achieving a composite function of latency mitigation and security metrics as the optimization objective, which is solved by the ChaCha20-based offloading decision algorithm (ChaCha20-ODA). The algorithm employs the ChaCha20 encryption protocol as its security mechanism. By executing a quarter-round function to generate a keystream, it provides robust protection for data tasks, ensuring that the data remains impervious to malevolent interception by adversaries throughout the transmission process. Furthermore, to improve the computational efficiency of task offloading, the algorithm simultaneously leverages both edge and local computing resources, achieving computational acceleration by optimizing the appropriate offload ratio. The experimental results illustrate that as compared with baselines, our approach achieves remarkable improvement in the balance between latency and safety benchmarks, which demonstrates the superiority of our method.










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References
Sun, X., Yu, F. R., & Zhang, P. (2021). A survey on cyber-security of connected and autonomous vehicles (cavs). IEEE Transactions on Intelligent Transportation Systems, 23(7), 6240–6259.
Gao, H., Liu, C., Li, Y., & Yang, X. (2021). V2vr: Reliable hybrid-network-oriented v2v data transmission and routing considering rsus and connectivity probability. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3533–3546. https://doi.org/10.1109/TITS.2020.2983835
Mohsan, S. A. H., Khan, M. A., Noor, F., Ullah, I., & Alsharif, M. H. (2022). Towards the unmanned aerial vehicles (uavs): A comprehensive review. Drones, 6(6), 147.
Liu, R., Xie, M., Liu, A., & Song, H. (2024). Joint optimization risk factor and energy consumption in iot networks with tinyml-enabled internet of uavs. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2023.3348837
Chen, M., Liu, A., Xiong, N. N., Song, H., & Leung, V. C. M. (2023). Sgpl: An intelligent game-based secure collaborative communication scheme for metaverse over 5g and beyond networks. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/JSAC.2023.3345403
Gao, H., Huang, W., & Duan, Y. (2021). The cloud-edge-based dynamic reconfiguration to service workflow for mobile ecommerce environments: a qos prediction perspective. ACM Transactions on Internet Technology (TOIT), 21(1), 1–23.
Cruz, P., Achir, N., & Viana, A. C. (2022). On the edge of the deployment: A survey on multi-access edge computing. ACM Computing Surveys, 55(5), 1–34.
Mao, S., Liu, L., Zhang, N., Dong, M., Zhao, J., Wu, J., & Leung, V. C. (2022). Reconfigurable intelligent surface-assisted secure mobile edge computing networks. IEEE Transactions on Vehicular Technology, 71(6), 6647–6660.
He, D., Wang, W., Xu, Y., Huang, X., Cheng, H., Duan, X., Huang, Y., Hong, H., Zhang, Y., & Zhang, W. (2020). Overview of physical layer enhancement for 5g broadcast in release 16. IEEE Transactions on Broadcasting, 66(2), 471–480. https://doi.org/10.1109/TBC.2020.2981775
Zhou, Y., Yeoh, P. L., Pan, C., Wang, K., Elkashlan, M., Wang, Z., Vucetic, B., & Li, Y. (2019). Offloading optimization for low-latency secure mobile edge computing systems. IEEE Wireless Communications Letters, 9(4), 480–484.
Jiang, H., Dai, X., Xiao, Z., & Iyengar, A. K. (2022). Joint task offloading and resource allocation for energy-constrained mobile edge computing. IEEE Transactions on Mobile Computing. https://doi.org/10.1109/TMC.2022.3150432
Xiao, H., Pei, Q., Song, X., & Shi, W. (2021). Authentication security level and resource optimization of computation offloading in edge computing systems. IEEE Internet of Things Journal, 9(15), 13010–13023.
Chen, M., & Hao, Y. (2018). Task offloading for mobile edge computing in software defined ultra-dense network. IEEE Journal on Selected Areas in Communications, 36(3), 587–597.
Chen, L., Wu, J., Zhang, J., Dai, H.-N., Long, X., & Yao, M. (2020). Dependency-aware computation offloading for mobile edge computing with edge-cloud cooperation. IEEE Transactions on Cloud Computing, 10(4), 2451–2468.
Meng, X., Wang, W., Wang, Y., Lau, V. K., & Zhang, Z. (2019). Closed-form delay-optimal computation offloading in mobile edge computing systems. IEEE Transactions on Wireless Communications, 18(10), 4653–4667.
Wang, J., Hu, J., Min, G., Zhan, W., Ni, Q., & Georgalas, N. (2019). Computation offloading in multi-access edge computing using a deep sequential model based on reinforcement learning. IEEE Communications Magazine, 57(5), 64–69.
Wei, H., Luo, H., Sun, Y., & Obaidat, M. S. (2019). Cache-aware computation offloading in iot systems. IEEE Systems Journal, 14(1), 61–72.
Kuang, Z., Li, L., Gao, J., Zhao, L., & Liu, A. (2019). Partial offloading scheduling and power allocation for mobile edge computing systems. IEEE Internet of Things Journal, 6(4), 6774–6785.
Guo, F., Zhang, H., Ji, H., Li, X., & Leung, V. C. (2018). An efficient computation offloading management scheme in the densely deployed small cell networks with mobile edge computing. IEEE/ACM Transactions on Networking, 26(6), 2651–2664.
Li, P., Xiao, Z., Wang, X., Huang, K., Huang, Y., & Gao, H. (2023). Eptask: Deep reinforcement learning based energy-efficient and priority-aware task scheduling for dynamic vehicular edge computing. IEEE Transactions on Intelligent Vehicles. https://doi.org/10.1109/TIV.2023.3321679
Pan, Y., Pan, C., Wang, K., Zhu, H., & Wang, J. (2021). Cost minimization for cooperative computation framework in mec networks. IEEE Transactions on Wireless Communications, 20(6), 3670–3684.
Liu, Y., Li, Y., Niu, Y., & Jin, D. (2019). Joint optimization of path planning and resource allocation in mobile edge computing. IEEE Transactions on Mobile Computing, 19(9), 2129–2144.
Kuang, L., Gong, T., OuYang, S., Gao, H., & Deng, S. (2020). Offloading decision methods for multiple users with structured tasks in edge computing for smart cities. Future Generation Computer Systems, 105, 717–729.
Roman, R., Rios, R., Onieva, J. A., & Lopez, J. (2018). Immune system for the internet of things using edge technologies. IEEE Internet of Things Journal, 6(3), 4774–4781.
Haddadi, H., Christophides, V., Teixeira, R., Cho, K., Suzuki, S., & Perrig, A. (2018). Siotome: An edge-isp collaborative architecture for iot security. Proc. IoTSec, 1–4.
Ju, Y., Cao, Z., Chen, Y., Liu, L., Pei, Q., Mumtaz, S., Dong, M., & Guizani, M. (2023). Noma-assisted secure offloading for vehicular edge computing networks with asynchronous deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2023.3320861
Li, H., Xu, H., Zhou, C., Lü, X., & Han, Z. (2020). Joint optimization strategy of computation offloading and resource allocation in multi-access edge computing environment. IEEE Transactions on Vehicular Technology, 69(9), 10214–10226. https://doi.org/10.1109/TVT.2020.3003898
Gu, X., Zhang, G., Wang, M., Duan, W., Wen, M., & Ho, P.-H. (2021). Uav-aided energy-efficient edge computing networks: Security offloading optimization. IEEE Internet of Things Journal, 9(6), 4245–4258.
Mao, B., Kawamoto, Y., & Kato, N. (2020). Ai-based joint optimization of qos and security for 6g energy harvesting internet of things. IEEE Internet of Things Journal, 7(8), 7032–7042.
Funding
This work was supported in part by the Consulting Project of Chinese Academy of Engineering under Grant 2023-XY-09, the National Natural Science Foundation of China under Grant 62272100, and in part by the Academy-Locality Cooperation Project of Chinese Academy of Engineering under Grant JS2021ZT05.
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All authors contributed to the study conception and design. Meng Yi wrote the main manuscript text. Peng Yang performed the methodology, funding acquisition and project administration.Jinhu Xie provided material preparation, data collection and analysis. Cheng Fang carried out experimental verification. Bing Li prepared the figures and provided valuable comments on previous versions of the manuscript. All authors reviewed the manuscript.
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Yi, M., Yang, P., Xie, J. et al. Soca: secure offloading considering computational acceleration for multi-access edge computing. Wireless Netw 31, 1021–1035 (2025). https://doi.org/10.1007/s11276-024-03813-2
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DOI: https://doi.org/10.1007/s11276-024-03813-2
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