关键词: 应急通信, 蚁群算法, 强化学习, Qos路由, 流量均衡

Abstract:

To adapt the rapid dynamic change of network topology and sudden network congestion in emergency communication, a routing algorithm MLSA suitable for emergency communication network is proposed. In this method, the ant colony algorithm framework is combined with the reinforcement learning process for system modeling. The performance statistical analysis process is limited to higher priority areas. By detecting the status information of local neighbor nodes, the different routing decision processes are scored, and the policy adjustment is made according to the network feedback, thereby the overall performance of the network is improved and network congestion is relieved. It increases the real-time and stability of data transmission. Experimental results show that in the environment nodes move in high speed, MLSA has obvious performance advantages over the classic on-demand routing protocols AODV and DSR for network congestion caused by emergency communication.

Key words: emergency communication, ant colony algorithm, reinforcement learning, Qos routing, traffic equalization