Mobile Edge Computing Empowered Energy Consumption Optimization for Multiuser Power IoT Networks: Mobile Edge Computing Empowered Energy Consumption Optimization

Shuangwei Li; Yang Xie; Mingming Shi; Xian Zheng; Yongling Lu

doi:10.4108/eetsis.8678

Authors

Shuangwei Li State Grid Jiangsu Electric Power Company
Yang Xie State Grid Jiangsu Electric Power Company
Mingming Shi Electric Power Research Institute of State Grid Jiangsu Electric Power Company
Xian Zheng Electric Power Research Institute of State Grid Jiangsu Electric Power Company
Yongling Lu Electric Power Research Institute of State Grid Jiangsu Electric Power Company

DOI:

https://doi.org/10.4108/eetsis.8678

Keywords:

Mobile edge computing, energy consumption, bandwidth allocation, power IoT networks

Abstract

Mobile edge computing (MEC) has emerged as a promising solution to enhance the computational capabilities of resource-constrained IoT devices while optimizing energy consumption. In this paper, we investigate energy-efficient resource allocation strategies for multiuser power IoT networks by jointly optimizing the offloading ratio, transmit power, and wireless bandwidth allocation. We propose an optimization framework that minimizes the total energy consumption of the system while ensuring the latency constraints of computation tasks. To solve this challenging non-convex problem, we employ an alternating optimization approach, where the offloading decision, wireless bandwidth allocation, and transmit power control are iteratively refined using convex optimization techniques and successive convex approximation (SCA). Simulation results are provided to show that the proposed scheme significantly outperforms the competing approaches in terms of energy efficiency. Specifically, for a system with 6 users, the proposed scheme maintains an energy consumption of approximately 0.1 Joules, reducing the energy consumption of the conventional schemes to less that 40 percentages.

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