부산대학교 도서관

Dynamicity and infrastructure-less nature of MANETs expose the routing in such networks to a variety of attacks, and moreover, make the conventional fixed policy routing algorithms inefficient. To deal with the routing challenges and varying behavior of malicious nodes in such networks, employing reinforcement learning algorithms and proper trust models seem promising. In this paper, we introduce a cognition layer in parallel and interacting with the network layer which comprises two cognitive processes: path learning (routing) and trust learning. The first process is based on machine learning algorithms and the latter is based on trust management. We compare our algorithm, TQOR, with a well known trust-based routing protocol, TQR, in terms of three measures of performance. The simulation results show better end-to-end delay and communication overhead which further improve as time progresses, without sacrificing the data packet delivery ratio.