Algorithms for Location Estimation Based on RSSI Sampling

Authors:
Charalampos Papamanthou;Franco P. Preparata;Roberto Tamassia
Affiliations:
Department of Computer Science and Center for Geometric Computing, Brown University,;Department of Computer Science and Center for Geometric Computing, Brown University,;Department of Computer Science and Center for Geometric Computing, Brown University,
Venue:
Algorithmic Aspects of Wireless Sensor Networks
Year:
2008

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Abstract

In this paper, we re-examine the RSSI measurement model for location estimation and provide the first detailed formulation of the probability distribution of the position of a sensor node. We also show how to use this probabilistic model to efficiently compute a good estimation of the position of the sensor node by sampling multiple readings from the beacons (where we do not merely use the mean of the samples) and then minimizing a function with an acceptable computational effort. The results of the simulation of our method in TOSSIM indicate that the location of the sensor node can be computed in a small amount of time and that the quality of the solution is competitive with previous approaches.