Reinforcement learning-enabled swarm intelligence method for computation task offloading in Internet-of-Things blockchain

作     者：Chen, Zhuo Yi, Jiahuan Zhou, Yang Luo, Wei 

作者机构：College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China Department of Computer Science and Software Engineering Auburn University Auburn United States Yunnan Aerospace Engineering Geophysical Detecting Co. Ltd. Kunming650217 China 

出 版 物：《Digital Communications and Networks》 (Digit. Commun Netw.)

年 卷 期：2025年第11卷第3期

页      面：912-924页

核心收录：

基　　金：This work has been jointly supported by the Project of Science and Technology Research Program of Chongqing Education Commission of China (No. KJZD-K202401105)  High-Quality Development Action Plan for Graduate Education at Chongqing University of Technology (No. gzljg2023308  No. gzljd2024204)  the Graduate Innovation Program of Chongqing University of Technology (No. gzlcx20233197) and Yunnan Provincial Key R&D Program (202203AA080006) 

主　　题：Reinforcement learning 

摘      要：Blockchain technology, based on decentralized data storage and distributed consensus design, has become a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT) due to its tamper-proof and non-repudiation features. Although blockchain typically does not require the endorsement of third-party trust organizations, it mostly needs to perform necessary mathematical calculations to prevent malicious attacks, which results in stricter requirements for computation resources on the participating devices. By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud, while providing data privacy protection for IoT applications, it can effectively address the limitations of computation and energy resources in IoT devices. However, how to make reasonable offloading decisions for IoT devices remains an open issue. Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions. The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms (e.g., Proof-of-Work), it optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices. Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms. © 2024 Chongqing University of Posts and Telecommunications