In modern wireless sensor networks (WSN) applications, location awareness has been one of the features that attracted many research interests. Various applications utilize location information for surveillance and asset tracking purposes. Common WSN localization systems use radio frequency (RF), ultra-sound, or laser devices to provide range information, whose node positions are to be determined by various algorithms accordingly. Multi-dimensional scaling (MDS) is one of the most common algorithms for transforming inter-node distances into node positions in Cartesian coordinates. However, MDS algorithm, by nature, has a cubic computational complexity. Also, the algorithm‟s ability to localize is restricted to fully connected WSNs, where every node sees every other node. This thesis proposes a low-cost pure RF based localization system, implemented with a novel clustering MDS algorithm. Its most attractive feature is its ability to localize a partially connected WSN with a linear computation complexity without sacrificing the localization accuracy. In this thesis, we review various localization techniques and conduct experiments to compare the clustering MDS‟ performance against the classical MDS‟ and GPS‟. The localization with a commercial GPS, although, better than the above two methods, has also introduced significant discrepancy. At the end, we have demonstrated that RF localization in our low-cost system does not deliver GPS-grade accuracy, but its ability to localize partially-connected WSN and low computation complexity have outperformed the classical MDS approach.
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Thesis advisor: Kaminska, Bozena
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