Performance optimization for smart grid blockchain integrated with fog computing using DDQN

In order to solve the energy crisis and pollution problems,smart grid is widely used.However,there are many challenges such as the management of distributed energy during the construction.Blockchain,as an emerging technology,can provide a secure and transparent solution to the decentralized network.Meanwhile,fog computing network is considered to avoid the high deployment cost.The edge servers have abundant computing and storage resources to perform as nodes in grid blockchain.In this paper,an innovative structure of smart grid blockchain integrated with fog computing are proposed.And a new consensus mechanism called scalable proof of cryptographic selection(SPoCS)is designed to adapt the hybrid networks.The mechanism not only includes a special index,contribution degree,to measure the loyalty of fog nodes and the probability of being a function node,but also has flexible block interval adjustment method.Meanwhile,the number of function nodes(validating nodes and ordering nodes)can also be adjusted.And a deep reinforcement learning(DRL)method is used to select the appropriate quantity to improve the performance under the strict constraints of security and decentralization.The simulation shows the scheme performs well in the throughput,cost and latency.