Improving localization in geosensor networks through use of sensor measurement data

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Abstract

The determination of a precise position in geosensor networks requires the use of measurements which are inherently inaccurate while minimizing the required computations. The imprecise positions produced using these inaccurate measurements mean that available methods for measurement of distances or angles are unsuitable for use in most applications. In this paper we present a new approach, the Anomaly Correction in Localization (ACL) algorithm, whereby classical trilateration is combined with the measurements of physical parameters at the sensor nodes to improve the precision of the localization. Simulation results show that for localization using triangulation of distance measurements with a standard deviation of 10% then the improvement in precision of the estimated location when using ACL is up to 30%. For a standard deviation in the measurements of 5% then an improvement in positioning precision of ca. 12% was achieved. © 2008 Springer-Verlag Berlin Heidelberg.

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Reichenbach, F., Born, A., Nash, E., Strehlow, C., Timmermann, D., & Bill, R. (2008). Improving localization in geosensor networks through use of sensor measurement data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5266 LNCS, pp. 261–273). Springer Verlag. https://doi.org/10.1007/978-3-540-87473-7_17

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