Modeling on Energy-Efficiency Computation Offloading Using Probabilistic Action Generating

• Published in: IEEE internet of things journal 2022-10, Vol.9 (20), p.20681-20692
• Main Authors: Wang, Cong, Lu, Weicheng, Peng, Sancheng, Qu, Youyang, Wang, Guojun, Yu, Shui
• Format: Article
• Language: English
• Subjects: Computation offloading; Computational efficiency; Computing time; deep reinforcement learning (DRL); Edge computing; Energy consumption; Energy efficiency; Integer programming; Internet of Things; Linear programming; Mixed integer; Mobile computing; mobile-edge computing (MEC); Optimization; Probability theory; Resource allocation; Resource management; Statistical analysis; Task analysis; Wireless communication; Wireless networks; Wireless sensor networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2022.3175760

Wireless-powered mobile-edge computing (MEC) emerges as a crucial component in the Internet of Things (IoTs). It can cope with the fundamental performance limitations of low-power networks, such as wireless sensor networks or mobile networks. Although computation offloading and resource allocation in MEC have been studied with different optimization objectives, performance optimization in larger-scale systems still needs to be further improved. More importantly, energy efficiency is also a key issue as well as computation offloading and resource allocation for wireless-powered MEC. In this article, we investigate the joint optimization of computation rate and energy consumption under limited resources, and propose an online offloading model to search for the asymptotically optimal offloading and resource allocation strategy. First, the joint optimization problem is modeled as a mixed integer programming (MIP) problem. Second, a deep reinforcement learning (DRL)-based method, energy efficiency computation offloading using probabilistic action generating (ECOPG), is designed to generate the joint optimization policy for computation offloading and resource allocation. Finally, to avoid the curse of dimensionality in large network scales, an action exploration mechanism based on probability is introduced to accelerate the convergence rate by targeted sampling and dynamic experience replay. The experimental results demonstrate that the proposed methods significantly outperform other DRL-based methods in energy consumption, and gain better computation rate and execution efficiency at the same time. With the expansion of the network scale, the improvements become more apparent.