Cognitive network management with reinforcement learning for wireless mesh networks
IPOM'07 Proceedings of the 7th IEEE international conference on IP operations and management
User to user QoE routing system
WWIC'11 Proceedings of the 9th IFIP TC 6 international conference on Wired/wireless internet communications
Overhead-Controlled routing in WSNs with reinforcement learning
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Review: A survey on intelligent routing protocols in wireless sensor networks
Journal of Network and Computer Applications
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Efficient and robust routing is central to wireless sensor networks (WSN) that feature energy-constrained nodes, unreliable links, and frequent topology change. While most existing routing techniques are designed to reduce routing cost by optimizing one goal, e.g., routing path length, load balance, re-transmission rate, etc, in real scenarios however, these factors affect the routing performance in a complex way, leading to the need of a more sophisticated scheme that makes correct trade-offs. In this paper, we present a novel routing scheme, AdaR that adaptively learns an optimal routing strategy, depending on multiple optimization goals. We base our approach on a least squares reinforcement learning technique, which is both data efficient, and insensitive against initial setting, thus ideal for the context of ad-hoc sensor networks. Experimental results suggest a significant performance gain over a na篓ýve Q-learning based implementation.