Resource Allocation for Vehicle Networking Mobile Blockchain

Mobile edge computing addresses resource scarcity in vehicle devices during consensus tasks. Yet, in edge-computing-based vehicle network blockchains, dynamic system states and user needs challenge resource allocation. This study presents a deep reinforcement learning technique for efficient workload distribution and resource management. The method allows vehicle devices to optimally offload and allocate resources, minimizing task latency in the blockchain network. Results show that this approach notably reduces the peak task delay for all miners, surpassing other methods.