The UAV Ad hoc network receives extensive attention in recent years due to its strong anti-interference ability, suitable for complex terrain, high degree of intelligence and low cost. The design and optimization of the routing protocol in this network are always the core research problem. Aiming at the problem that the node doesn't update the local route in time due to it's rapid movement in the UAV Ad hoc network, this paper proposed a dynamic sensor optimized link state routing protocol based on the Q-learning algorithm(dynamic sensor optimized link state routing based on Q-learning algorithm, DSQOLSR). Firstly, the proposed protocol fully considered the characteristics of the high-speed movement of nodes in the UAV Ad hoc network. When selected MPR(multi-point relay) nodes, this paper added two indicators of link stability and link existence time to make the selected MPR nodes set more stable and reasonable. In addition, this paper used the Q-learning algorithm to adjust the sending interval of TC messages adaptively. In order to reduce control overhead, the proposed algorithm increased the TC sending interval when the network topology changed slightly. In order to meet the requirements of rapid perception and construction of the network topology and further realize the timely routing of data, the algorithm reduced the TC sending interval when the topology changed greatly. The simulation results show that compared with the DT-OLSR(dynamic topology link state routing based on Q-learning algorithm) protocol, the proposed protocol improves the end-to-end delay, throughput, success rate and network lifetime performance by 12.61%, 9.28%, 7.69% and 5.86% respectively, which verifies the effectiveness of this protocol.

[2021-10-18] Accepted Paper /doc/2021.07.0304/v1

[2022-02-05] Printed Article /doc/2021.07.0304/v2