Collaborative Resource Allocation for Blockchain-Enabled Internet of Things with Multi-Agent Deep Reinforcement Learning

Mobile Edge Computing (MEC) reduces service latency and enhances Quality of Service (QoS) by offloading tasks to the wireless network edge. However, the rapid growth of task offloading and the associated data transmission security challenges deserve further investigation. This study proposes a blockchain-MEC hybrid solution where mobile devices process tasks and engage in block mining to boost system utility. The objective is to maximize the accumulated reward in the blockchain-MEC system by optimizing off loading decisions, channel selection, transmission power, computing resources, and block intervals. A Markov Decision Process is formulated to model the optimization problem which is solved via a multi-agent deep reinforcement learning (MADRL) algorithm. The results of the simulation demonstrate that our approach is more effective than the baseline method.