Energy consumption minimized wireless powered edge computing
Document Type
Article
Source of Publication
Ad Hoc Networks
Publication Date
11-1-2025
Abstract
Most Internet of Things (IoT) devices face challenges in handling complex computational tasks due to their limited computing capabilities. To address this issue, Mobile Edge Computing (MEC) has been introduced, which significantly enhances computational efficiency and response speed by offloading tasks to the cloud or the network edge. Additionally, by integrating Wireless Power Transfer (WPT) technology, IoT devices can harvest energy wirelessly, thereby alleviating energy constraints. This paper investigates a WPT-enabled MEC network with the goal of minimizing the system's overall energy consumption. First, we formulate the energy minimization problem as a mixed-integer nonlinear programming (MINLP) problem. Then, we propose a Deep Reinforcement Learning (DRL)-based algorithm to jointly optimize offloading decisions and time allocation. Simulation results demonstrate that the proposed approach not only converges quickly but also achieves performance comparable to that of the exhaustive search method. Furthermore, it significantly reduces energy consumption compared to baseline schemes.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
178
Disciplines
Computer Sciences
Keywords
Binary offloading, Mobile edge computing, Reinforcement learning, Wireless power transfer
Scopus ID
Recommended Citation
Wang, Ke; Chi, Kaikai; and Al-Dulaimi, Anwer, "Energy consumption minimized wireless powered edge computing" (2025). All Works. 7389.
https://zuscholars.zu.ac.ae/works/7389
Indexed in Scopus
yes
Open Access
no