Abstract
Localizing sensor nodes is essential due to their random distribution after deployment. To reach a long network lifetime, which strongly depends on the limited energy resources of every node, applied algorithms must be developed with an awareness of computation and communication cost. In this paper we present a new localization method, which places a minimum computational requirement on the nodes but achieves very low localization errors of less than 1%. To achieve this, we split the complex least squares method into a less central precalculation and a simple, distributed subcalculation. This allows precalculating the complex part on high-performance nodes, e.g. base stations. Next, sensor nodes estimate their own positions by simple subcalculation, which does not exhaust the limited resources. We analyzed our method with three commonly used numerical techniques – normal equations, qr-factorization, and singular-value decomposition. Simulation results showed that we reduced the complexity on every node by more than 47% for normal equations. In addition, the proposed algorithm is robust with respect to high input errors and has low communication and memory requirements.
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Reichenbach, F., Born, A., Timmermann, D., Bill, R. (2006). A Distributed Linear Least Squares Method for Precise Localization with Low Complexity in Wireless Sensor Networks. In: Gibbons, P.B., Abdelzaher, T., Aspnes, J., Rao, R. (eds) Distributed Computing in Sensor Systems. DCOSS 2006. Lecture Notes in Computer Science, vol 4026. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11776178_31
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DOI: https://doi.org/10.1007/11776178_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35227-3
Online ISBN: 978-3-540-35228-0
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