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A MANET routing protocol using Q-learning method integrated with Bayesian network

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3 Author(s)
Ke Wang ; NUS Grad. Sch. for Integrative Sci. & Eng., Nat. Univ. of Singapore, Singapore, Singapore ; Wai-Choong Wong ; Teck Yoong Chai

Frequent changes in topology and link quality in mobile ad-hoc networks (MANETs) present challenging problems in achieving optimal performance. We propose a self-learning routing protocol based on Q-learning that makes use of Quality of Service (QoS) parameters such as Signal to Interference plus Noise Ratio (SINR), delay and throughput, to make routing decisions. At the same time, a Bayesian Network (BN) is implemented to estimate neighboring network congestion level to tune the Q-learning weights. Our protocol also sends out probing packets to detect and solve the routing-loop problem which is not addressed in most Q-learning-based routing proposals. The simulation results show that the proposed system demonstrates comparatively better performance in a dense heavy-loaded scenario.

Published in:

Communication Systems (ICCS), 2012 IEEE International Conference on

Date of Conference:

21-23 Nov. 2012