Abstract
Localization of Wireless Sensor Networks (WSN) is an important problem that has gained a lot of attention as can be seen in the survey papers [8, 9]. Among the many techniques that have been employed to obtain accurate localization under various underlying assumptions, Multi-Dimensional Scaling (MDS) provides a centralized solution given range measurements between nodes. However, dissimilarity matrix provided to MDS is incomplete due to missing range measurements between significant numbers of nodes. Researchers have used shortest distance computations on the basis of existing range measurements to provide an estimate of the actual range measurements. This leads to significant error in localization using MDS. In this work, we introduce an improved estimate of the shortest distances between nodes based on estimated angle between nodes. We do not assume AoA (Angle of Arrival) is available, however we estimate inter-node angle to help improve distance measures. Our results show significant improvement in the performance of MDS for localization of wireless sensor networks for both range-based and range-free noisy range network models. Our simulation verifies the result on both sparse and dense networks.
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Kanchi, S. (2021). Localization in Wireless Sensor Networks Using MDS with Inter-node Angle Estimation. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12949. Springer, Cham. https://doi.org/10.1007/978-3-030-86653-2_45
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DOI: https://doi.org/10.1007/978-3-030-86653-2_45
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