Optimization Method of Opportunistic Network Routing Based on Deep Reinforcement Learning

Abstract

The conventional routing optimization method of opportunistic networks mainly solves the problem of network breakage, which can not meet the communication conditions of nodes in complex environment. Therefore, an opportunistic network routing optimization method based on deep reinforcement learning is designed. Optimize the routing mechanism of opportunistic network, use routing protocol to obtain the next node, and transmit network data with three hop paths, and control the communication overhead ratio by reducing the number of data copies, thus ensuring the data transmission efficiency. Based on deep reinforcement learning, the opportunistic network congestion-aware probabilistic routing protocol is selected, and the redundancy of packet forwarding and caching is managed according to the contact probability of opportunistic network nodes and the action value of packet state, thus alleviating the congestion problem of long-term storage nodes of packets. The simulation results show that the optimization effect of this method is better and can be applied to real life.