Making Computational Task Offloading Decision Based on Swarm Intelligence Enabled RL in Internet-of-Things Blockchain

Applying blockchain technology to a distributed Internet-of-Things (IoT) environment has emerged as a promising solution for enhancing system data security and mitigating the risk of privacy leakage. Meanwhile, to meet the computational demands of blockchain, IoT devices can offload their computational tasks to nearby edge nodes or even the cloud side, thereby compensating for their insufficient computational resources and available energy. This paper addresses the issue of computational task offloading for heterogeneous IoT devices in a distributed and dynamic environment. It first considers factors such as the delay in IoT execution of blockchain computations, energy consumption costs, and the payable budget required for offloading, and establishes a time-series optimization model. Subsequently, a swarm intelligence method is introduced into Multi-agent Reinforcement Learning to solve the optimization problem, effectively overcoming the challenges faced by purely Reinforcement Learning (RL)-based method, which are susceptible to hyperparameter tuning and prone to getting stuck in local optima, leading to sub-optimal solutions. Comparative experiments with several representative baseline methods shown that the proposed computational task offloading method exhibits relatively better convergence performance, solution quality across different network scales, and robustness.