Abstract
Multidimensional Scaling (MDS) has been recently applied to node localization in sensor networks and gained some very impressive performance. MDS treats dissimilarities of pair-wise nodes directly as Euclidean distances and then makes use of the spectral decomposition of a doubly centered matrix of dissimilarities. However dissimilarities mainly estimated by Received Signal Strength (RSS) or by the Time of Arrival (TOA) of communication signal from the sender to the receiver used to suffer much errors when the distances between nodes are far. From this observation, Weighted Multidimensional Scaling (WMDS) is proposed in this paper. Different from MDS, WMDS incorporates weighting factors to account for the impact of pair-wise estimated dissimilarities in MDS framework. The further distance between two nodes is, the less “impact” weight should be considered. The experiment on real sensor network measurements of RSS and TOA shows the efficiency and novelty of WMDS for sensor localization problem in term of sensor location-estimated error.
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Vo, D., Vo, N., Challa, S. (2008). Weighted MDS for Sensor Localization. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, vol 5073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69848-7_34
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DOI: https://doi.org/10.1007/978-3-540-69848-7_34
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