Algorithms for Location Estimation Based on RSSI Sampling

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.