Energy-Efficient Task Offloading in Wireless-Powered MEC: A Dynamic and Cooperative Approach

Huaiwen He, Chenghao Zhou, Feng Huang, Hong Shen and Shuangjuan Li ()
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Huaiwen He: School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China
Chenghao Zhou: School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China
Feng Huang: School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China
Hong Shen: School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia
Shuangjuan Li: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China

Mathematics, 2024, vol. 12, issue 15, 1-23

Abstract: Mobile Edge Computing (MEC) integrated with Wireless Power Transfer (WPT) is emerging as a promising solution to reduce task delays and extend the battery life of Mobile Devices (MDs). However, maximizing the long-term energy efficiency (EE) of a user-cooperative WPT-MEC system presents significant challenges due to uncertain load dynamics at the edge MD and the time-varying state of the wireless channel. In this paper, we propose an online control algorithm to maximize the long-term EE of a WPT-MEC system by making decisions on time allocations and transmission powers of mobile devices (MDs) for a three-node network. We formulate a stochastic programming problem considering the stability of network queues and time-coupled battery levels. By leveraging Dinkelbach’s method, we transform the fractional optimal problem into a more manageable form and then use the Lyapunov optimization technique to decouple the problem into a deterministic optimization problem for each time slot. For the sub-problem in each time slot, we use the variable substitution technique and convex optimization theory to convert the non-convex problem into a convex problem, which can be solved efficiently. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline algorithms, achieving a 20% improvement in energy efficiency. Moreover, our algorithm achieves an [ O ( 1 / V ) , O ( V ) ] trade-off between EE and network queue stability.

Keywords: mobile edge computing (MEC); wireless power transfer (WPT); user cooperation; Lyapunov optimization; convex optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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