Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System

Long Li, Gaochao Xu, Peng Liu, Yang Li and Jiaqi Ge
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Long Li: College of Computer Science and Technology, Jilin University, Changchun 130012, China
Gaochao Xu: College of Computer Science and Technology, Jilin University, Changchun 130012, China
Peng Liu: College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
Yang Li: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Jiaqi Ge: College of Computer Science and Technology, Jilin University, Changchun 130012, China

Future Internet, 2020, vol. 12, issue 12, 1-18

Abstract: With the rapid popularity of mobile devices (MDs), mobile edge computing (MEC) networks and wireless power transmission (WPT) will receive more attention. Naturally, by integrating these two technologies, the inherent energy consumption during task execution can be effectively reduced, and the collected energy can be provided to charge the MD. In this article, our research focuses on extending the battery time of MDs by maximizing the harvested energy and minimizing the consumed energy in the MEC–WPT system, which is formulated as a residual energy maximization problem and also a non-convex optimization problem. On the basis of study on maximizing the residual energy under multi-users and multi-time blocks, we propose an effective jointly optimization method (i.e., jointly optimize the energy harvesting time, task-offloading time, task-offloading size and the MDs’ CPU frequency), which combines the convex optimization method and the augmented Lagrangian to solve the residual energy maximum problem. We leverage Time Division Multiple Access (TMDA) mode to coordinate computation offloading. Simulation results show that our scheme has better performance than the benchmark schemes on maximizing residual energy. In particular, our proposed scheme is outstanding in the failure rate of multiple MDs and can adapt to the task size to minimize the failure rate.

Keywords: mobile edge computing; wireless power transmission; convex optimization; augmented Lagrangian method (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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