Model-Free Probabilistic Localization of Wireless Sensor Network Nodes in Indoor Environments

We present a technique that makes up a practical probabilistic approach for locating wireless sensor network devices using the commonly available signal strength measurements (RSSI). From the RSSI measurements between transmitters and receivers situated on a set of landmarks, we construct appropriate probabilistic descriptors associated with a device's position in the contiguous space using a pdf interpolation technique. We then develop a localization system that relies on these descriptors and the measurements made by a set of clusterheads positioned at some of the landmarks. The localization problem is formulated as a composite hypothesis testing problem. We develop the requisite theory, characterize the probability of error, and address the problem of optimally placing clusterheads. Experimental results show that our system achieves an accuracy equivalent to 95% < 5 meters and 87% < 3 meters.