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Energy-Efficient Task Offloading for Three-Tier Wireless-Powered Mobile-Edge Computing
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AI
DOI：10.1109/JIOT.2023.3238329.png)
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Abstract
En 中文
Mobile-edge computing (MEC) is envisioned to address the computation demands of Internet of Things (IoT) devices. However, it is crucial for the MEC to operate in coordination with the cloud tier to achieve a highly scalable IoT system. In addition, IoT devices require regular maintenance to either recharge or replace their batteries which may not always be feasible. Wireless energy transfer (WET) can provide IoT devices with a stable source of energy. Nonetheless, proper scheduling of energy harvesting and efficient allocation of computing resources are the key to the sustainable operation of these devices. In this article, we introduce a three-tier wireless-powered MEC (WPMEC) consisting of cloud, MEC servers, and IoT devices. We first formulate a combinatorial optimization problem that aims to minimize the wireless energy transmission. To tackle the complexity of the problem, we use bipartite graph matching and propose a harvest-then-offload mechanism for IoT devices. We also exploit parallel processing to increase the performance of the proposed algorithm. Through numerical experiments, we evaluate the performance of our proposed mechanism. Our results show that the proposed mechanism significantly reduces the required energy for the operation of IoT devices compared to different offloading policies. We further show that the proposed mechanism results in up to 34% less wireless energy transmission in comparison to an existing work in the literature.
Keywords:
Task analysis
Internet of Things
Servers
Computational modeling
Wireless communication
Cloud computing
Costs
Bipartite graph matching
Internet of Things (IoT)
mobile-edge computing (MEC)
wireless power transfer (WPT)
Journal
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