Computer and Information Technology, International Conference on (2006)
Sept. 20, 2006 to Sept. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CIT.2006.34
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 naive Q-learning based implementation.
Learning, Wireless sensor networks, Base stations, Design optimization, Least squares methods, Routing protocols, Sensor phenomena and characterization, Sensor systems, Computer science, Network topology
"Adaptive Routing for Sensor Networks using Reinforcement Learning", Computer and Information Technology, International Conference on, vol. 00, no. , pp. 219, 2006, doi:10.1109/CIT.2006.34