2020/06/19 by Xiaojing Chen, Zhouyu Lu, Chen, Xiaojing +11 · 1 citation
Computer Science · Engineering · #Algorithm #Channel (broadcasting) #Cloud computing #Computation #Computation offloading #Computer network #Computer science #Distributed computing #Edge computing #Edge device #Efficient energy use #Embedded system #Energy Harvesting in Wireless Networks #Energy consumption #Engineering #Enhanced Data Rates for GSM Evolution #FOS: Electrical engineering #Internet of Things #IoT Networks and Protocols #IoT and Edge/Fog Computing #Mobile device #Mobile edge computing #Operating system #Resource allocation #Resource management (computing) #Signal Processing (eess.SP) #Systems and Control (eess.SY) #Telecommunications #Transmitter #Transmitter power output #Wireless #Wireless power transfer #cs.SY #eess.SP #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2006.10978
published in arXiv (Cornell University) (Cornell University) · submitted for review
arxiv created 2020/06/19 · openalex publication_date 2020/06/19 · arxiv updated 2020/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Driven by explosive computation demands of Internet of Things (IoT), mobile edge computing (MEC) provides a promising technique to enhance the computation capability for mobile users. In this paper, we propose a joint resource allocation and load management mechanism in an MEC system with wireless power transfer (WPT), by jointly optimizing the transmit power for WPT, the local/edge computing load, the offloading time, and the frequencies of the central processing units (CPUs) at the access point (AP) and the users. To achieve an energy-efficient and sustainable WPT-MEC system, we minimize the total energy consumption of the AP, while meeting computation latency requirements. Cooling energy which is non-negligible, is taken into account in minimizing the energy consumption of the MEC system. By rigorously orchestrating the state-of-the-art optimization techniques, we design an iterative algorithm and obtain the optimal solution in a semi-closed form. Based on the solution, interesting properties and insights are summarized. Extensive numerical tests show that the proposed algorithm can save up to 90.4% the energy of existing benchmarks.