Adaptive tree: a learning-based meta-routing strategy for sensor networks
One of the most common communication patterns in sensor networks is routing data to a base station, while the base station can be either static or mobile. Even in static cases, a static spanning tree may not survive for a long time due to failures of sensor nodes. In this paper, we present an adaptive spanning tree routing mechanism, using real-time reinforcement learning strategies. We demonstrate via simulation that without additional control packets for tree maintenance, adaptive spanning trees can maintain the ``best' connectivity to the base station, in spite of node failures or mobility of the base station.
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Zhang, Y. ; Huang, Q. Adaptive tree: a learning-based meta-routing strategy for sensor networks. Third IEEE Consumer Communications and Networking Conference (CCNC 2006); 2006 January 8-10; Las Vegas NV. Piscataway, NJ: IEEE; 2006; 1: 122-126.
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