Indoor positioning system using multiple services set identifiers-based fingerprints

Tracking people and localizing objects within indoor environments have become a necessity and thus motivated many researchers to tackle this challenge. Indoor positioning systems (IPSs) have been proposed using several technologies and efforts are honing down on mitigating the positioning error. The Received Signal Strength (RSS)-fingerprint based IPS has been recognized as one of the possible most promising techniques. In this paper, we propose a new technique which uses the general framework that is based on fingerprinting of multiple service set identifiers (SSIDs) configured on the same access point. A voting scenario is also proposed as a tool to enhance IPS performance. We implemented the system inside the College of Engineering and Applied Sciences (CEAS) at Western Michigan University, and compared its performance with some of the conventional techniques such as K-Nearest Neighbors (KNN) and support vector machine (SVM). The results also demonstrate the effect of redundancy when using a random selection technique of access points and its impact on IPS performance.