Robust Indoor Sensor Localization Using Signatures of Received Signal Strength

Indoor localization based on the received signal strength (RSS) values of the wireless sensors has recently received a lot of attention. However, due to the interference of other wireless devices and human activities, the RSS value varies significantly over different times. This hinders exact location prediction using RSS values. In this paper, we propose three methods to counter the adverse effect of the RSS value variation on location prediction. First, we propose to use an index location to select the best radio map, among several preconstructed radio maps, for online location prediction. Second, for an observed value of the signal strength of a sensor, we record, respectively, the distances from the sensor to the nearest location and the farthest location where the signal strength value has been observed. The minimal and maximal (min-max) distances for each signal strength value of a sensor are then used to reduce the search space in online location prediction. Third, a location-dependent received signal strength vector, called the RSS signature, is used to predict the location of a user. We have built a system, called the region-point system, based on the proposed three methods. The experimental results show that the region-point system offers less mean position error compared to the existing methods, namely, RADAR, TREE, and CaDet. Furthermore, the index location method correctly selects the best radio map for online location prediction, and the min-max distance method promotes the prediction accuracy of RADAR by restricting the search space of RADAR in location prediction.