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Wireless Sensor Networks (WSN) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) in each node can not be easily replaced. One solution is to deploy a large number of sensor nodes, since the lifetime and dependability of the network can be increased through cooperation among nodes. In addition to energy consumption, applications for WSN may also have other concerns, such as, meeting deadlines and maximizing the quality of information. In this paper, we present a Genetic Machine Learning algorithm aimed at applications that make use of trade-offs between different metrics. Simulations were performed on random topologies assuming different levels of faults. Our approach showed a significant improvement when compared with the use of IEEE 802.15.4 protocol.
Date of Conference: 3-5 Nov. 2009