Analyzing and visualizing multiagent rewards in dynamic and stochastic domains
Autonomous Agents and Multi-Agent Systems
On Computing Mobile Agent Routes for Data Fusion in Distributed Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
Near real-time system identification in a wireless sensor network for adaptive feedback control
ACC'09 Proceedings of the 2009 conference on American Control Conference
Self-organising sensors for wide area surveillance using the max-sum algorithm
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
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Highly accurate sensor measurements are crucial in order for power plants to effectively operate, as well as to predict and subsequently prevent any potentially catastrophic failures. As the cost of sensors decreases while their power increases, distributed sensor networks become a more attractive option for implementation in power plants. In this work, we investi- gate the use of a distributed sensor network to achieve highly accurate measurements. We apply shaped rewards to local components and use a simple learning algorithm at each sen- sor in order to maximize those rewards. Our results show that the measurements from a sensor network trained us- ing shaped rewards are up to two orders of magnitude more accurate than a sensor network trained with a traditional global reward. Further, the algorithm proposed scales well to large networks, and is robust to measurement noise and sensor failures.