Factor Optimization for the Design of Indoor Positioning Systems Using a Probability-Based Algorithm

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems the most widespread around globe and commonly found have a ready-to-use infrastructure composed mostly of access points (APs). They advertise useful information, such as received signal strength (RSS), that is processed by adequate location algorithms, which not always capable achieving desired localization error only themselves. In this sense, paper proposes new method improve accuracy IPSs optimizing arrangement APs over environment using an enhanced probability-based algorithm. From assumption log-distance path loss model can reasonably describe, on average, distribution RSS throughout environment, we build simulation framework analyze impact, accuracy, main factors constitute algorithm, number reference (RPs) samples collected per test point. To demonstrate applicability proposed solution, real-world testbed dataset used validation. The obtained results show trends verified via strongly correlate in processing when allied with optimal configuration APs. This indicates our providing factor combination—through early simulations—for design more efficient rely