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<p>Data fusion in wireless sensor networks can improve the performance of a network by eliminating redundancy  and power consumption, ensuring fault-tolerance between sensors, and managing eÂ®ectively the available com-  munication bandwidth between network components. This thesis considers a data fusion approach applied to  wireless sensor networks based on fuzzy logic theory. In particular, a cluster-based hierarchical design in wire-  less sensor networks is explored combined with two data fusion methods based on fuzzy logic theory. A data  fusion algorithm is presented and tested using Mamdani and Tsukamoto fuzzy inference methods. In addition,  a concept related to the appropriate queuing models is presented based on classical queuing theory. Results  show that the Mamdani method gives better results than the Tsukamoto approach for the two implementations  considered. We noted that the proposed algorithm requires low processing and computational power. As a  result, it can be applied to WSNs to provide optimal data fusion and ensures maximum sensor lifetime and  minimum time delay.
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<br>Subjects: Electrical engineering; Biology; Energy consumption; Multisensor data fusion; Traffic patterns; Algorithms; Queuing theory</div>
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Bougiouklis, Theodoros C.</div>
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