Sensor networks routing via Bayesian exploration

Shuang Hao, Ting Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations


There is increasing research interest in solving routing problems in sensor networks subject to constraints such as data correlation, link reliability and energy conservation. Since information concerning these constraints are unknown in an environment, a reinforcement learning approach is proposed to solve this problem. To this end, we deploy a Bayesian method to offer good balance between exploitation and exploration. It estimates the benefit of exploration by value of information therefore avoids the error-prone process of parameter tuning which usually requires human intervention. Experimental results have shown that this approach outperforms the widely-used Q-routing method.

Original languageEnglish (US)
Title of host publicationProceedings - The 31st IEEE Conference on Local Computer Networks, LCN 2006
Number of pages2
StatePublished - Dec 1 2006
Event31st Annual IEEE Conference on Local Computer Networks, LCN 2006 - Tampa, FL, United States
Duration: Nov 14 2006Nov 16 2006

Publication series

NameProceedings - Conference on Local Computer Networks, LCN


Other31st Annual IEEE Conference on Local Computer Networks, LCN 2006
Country/TerritoryUnited States
CityTampa, FL

All Science Journal Classification (ASJC) codes

  • Engineering(all)


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