Computation Rate Maximization for Wireless-Powered and Multiple-User MEC System with Buffer Queue

Abstract

Mobile edge computing(MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things(IoT) devices in industry 4.0 technology, MEC also facilitates wireless power transfer, enhancing efficiency and sustainability for these devices. The most related studies concerning the computation rate in MEC are based on the coordinate descent method, the alternating direction method of multipliers(ADMMs) and Lyapunov optimization. Nevertheless, these studies do not consider the buffer queue size. This research work concerns the computation rate maximization for wireless-powered and multiple-user MEC systems, specifically focusing on the computation rate of end devices and managing the task buffer queue before computation at the terminal devices. A deep reinforcement learning(RL)-based task offloading algorithm is proposed to maximize the computation rate of end devices and minimizes the buffer queue size at the terminal devices. The central idea of this work is to explore the best optimal mode selection for IoT devices connected to the MEC system. The proposed algorithm optimizes computation delay by maximizing the computation rate of end devices and minimizing the buffer queue size before computation at the terminal devices. Then, the current study presents a deep RL-based task offloading algorithm to solve such a mixed-integer and non-convex optimization problem, aiming to get a better trade-off between the buffer queue size and the computation rate. The extensive simulation results reveal that the presented algorithm is much more efficient than the existing work to maintain a small buffer queue for terminal devices while simultaneously achieving a high-level computation rate.

Keywords

computation rate / mobile edge computing(MEC) / buffer queue / non-convex optimization / deep reinforcement learning

References

[1] VAILSHERY L S. Iot and non-iot connections worldwide 2010-2025[EB/OL].(2022-09-22)[2023-10-12]. https://www.statista.com/statistics/1101442/iot-number-of-connected-devices-worldwide.

[2] HU Y C, PATEL M, SABELLA D, et al. Mobile edge computing:a key technology towards 5G[J]. ETSI White Paper, 2015, 11(11):1-16.

[3] XIAO Z, DAI X X, JIANG H B, et al. Vehicular task offloading via heat-aware MEC cooperation using game-theoretic method[J]. IEEE Internet of Things Journal, 2020, 7(3):2038-2052.

[4] XIAO Z, XIAO D P, HAVYARIMANA V, et al. Toward accurate vehicle state estimation under non-gaussian noises[J]. IEEE Internet of Things Journal, 2019, 6(6):10652-10664.

[5] MACH P, BECVAR Z. Mobile edge computing:a survey on architecture and computation offloading[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3):1628-1656.

[6] ABBAS N, ZHANG Y, TAHERKORDI A, et al. Mobile edge computing:a survey[J]. IEEE Internet of Things Journal, 2018, 5(1):450-465.

[7] PSIHA M M, VLAMOS P. IoT applications with 5G connectivity in medical tourism sector management:third-party service scenarios[M]//VLAMOS P.Advances in Experimental Medicine and Biology. Cham: Springer International Publishing, 2017:141-154.

[8] LIU Y Q, PENG M G, SHOU G C, et al. Toward edge intelligence:multiaccess edge computing for 5G and Internet of Things[J]. IEEE Internet of Things Journal, 2020, 7(8):6722-6747.

[9] XIAO Z, LI F C, JIANG H B, et al. A joint information and energy cooperation framework for CR-enabled macro-femto heterogeneous networks[J]. IEEE Internet of Things Journal, 2020, 7(4):2828-2839.

[10] ZHANG G L, NI S F, ZHAO P. Learning-based joint optimization of energy delay and privacy in multiple-user edge-cloud collaboration MEC systems[J]. IEEE Internet of Things Journal, 2022, 9(2):1491-1502.

[11] ZHAO P, TAO J W, RAUF A, et al. Load balancing for energy-harvesting mobile edge computing[J]. IEICE Transactions on Fundamentals of Electronics,Communications and Computer Sciences, 2021, E104.A(1):336-342.

[12] ZHANG G L, ZHANG W Q, CAO Y, et al. Energy-delay tradeoff for dynamic offloading in mobile-edge computing system with energy harvesting devices[J]. IEEE Transactions on Industrial Informatics, 2018, 14(10):4642-4655.

[13] LIN Z F, WANG F, LIU L C. Computation rate maximization for multiuser mobile edge computing systems with dynamic energy arrivals[C]//2021 IEEE/CIC International Conference on Communications in China (ICCC). New York: IEEE, 2021:312-317.

[14] ABDUL R, MUHAMMAD U, AHMAD B, et al. Health monitoring of induction motor using electrical signature analysis[J]. Journal of Donghua University (English Edition), 2022, 39(3):265-271.

[15] XU L T, HUANG D Y, ALI ZAIDI S F, et al. Graph Fourier transform based audio zero-watermarking[J]. IEEE Signal Processing Letters, 2021,28:1943-1947.

[16] YANG Y, HU Y L, GURSOY M C. Deep reinforcement learning and optimization based green mobile edge computing[C]//2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). New York: IEEE, 2021:1-2.

[17] ZHAN W H, LUO C B, WANG J, et al. Deep-reinforcement-learning-based offloading scheduling for vehicular edge computing[J]. IEEE Internet of Things Journal, 2020, 7(6):5449-5465.

[18] YU S, CHEN X, ZHOU Z, et al. When deep reinforcement learning meets federated learning:intelligent multitimescale resource management for multiaccess edge computing in 5G ultradense network[J]. IEEE Internet of Things Journal, 2021, 8(4):2238-2251.

[19] BI S Z, ZHANG Y J. Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading[J]. IEEE Transactions on Wireless Communications, 2018, 17(6):4177-4190.

[20] LI J, ZENG F Z, XIAO Z, et al. Drive2friends:inferring social relationships from individual vehicle mobility data[J]. IEEE Internet of Things Journal, 2020, 7(6):5116-5127.

[21] WANG F, XU J, WANG X, et al. Joint offloading and computing optimization in wireless powered mobile-edge computing systems[C]//2017 IEEE International Conference on Communications (ICC). New York: IEEE, 2017:1784-1797.

[22] JIANG H B, LI J, ZHAO P, et al. Location privacy-preserving mechanisms in location-based services[J]. ACM Computing Surveys, 2022, 54(1):1-36.

[23] HERBERT S, WASSELL I, LOH T H, et al. Characterizing the spectral properties and time variation of the in-vehicle wireless communication channel[J]. IEEE Transactions on Communications, 2014, 62(7):2390-2399.

[24] WU X D, FU L Y, WANG S Q, et al. Collective influence maximization in mobile social networks[J]. IEEE Transactions on Mobile Computing, 2023, 22(2):797-812.

[25] LIU D B, CAO Z C, HOU M S, et al. Pushing the limits of transmission concurrency for low power wireless networks[J]. ACM Transactions on Sensor Networks, 2020, 16(4):1-29.

[26] PRABHA B, RAMESH K, RENJITH P N. A review on dynamic virtual machine consolidation approaches for energy-efficient cloud data centers[M]//Data Intelligence and Cognitive Informatics. Singapore: Springer Singapore, 2021:761-780.

[27] JIANG H B, LIU W P, JIANG G Y, et al. Fly-navi:a novel indoor navigation system with on-the-fly map generation[J]. IEEE Transactions on Mobile Computing, 2021, 20(9):2820-2834.

[28] ZHAO P, YANG Z Y, MU Y Q, et al. Selfish-aware and learning-aided computation offloading for edge-cloud collaboration network[J]. IEEE Internet of Things Journal, 2023, 10(11):9953-9965.

Funding

National Natural Science Foundation of China(61902060)

Shanghai Sailing Program, China(19YF1402100)

Fundamental Research Funds for the Central Universities, China(2232019D3-51)

Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications, China)(SKLNST-2021-1-06)