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Energy consumption minimized wireless powered edge computing
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AI
DOI：10.1016/j.adhoc.2025.103973.png)
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Abstract
En 中文
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.
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