NNT: Nearest Neighbour Trapezoid Algorithm for IoT WLAN Smart Indoor Localization Leveraging RSSI Height Estimation

Abstract

Indoor localization as a technique for assisting, or replacing outdoor satellite and cell tower localization systems, has taken a toll in the recent Internet of Things (IoT) era. This IoT drive has prompted increased research towards indoor localization, where fingerprinting, radio mapping as a cost-effective and efficient scheme, is emerging as the best enterprise entrepreneurs choose. However, indoor complex environments comprise of trackable devices (TD) at various heights, such as child trackers, dog tags, TD on the table, TD’s in the pockets, and situations such as pedestrians talking on the phone: that is at the height of the ear, amongst others. This paper first investigates and analyses “experimentally” the impact of received signal strength indicator (RSSI) fingerprinting height to construct radio maps for indoor localization. Secondly, it proposes the novel trapezoid path loss model for RSSI estimation and finally the nearest neighbour trapezoid (NNT) algorithm for IoT smart indoor localization leveraging and mitigating the impact of height considered during the offline signal fingerprinting. We further propose approximately 1 meter above the flooring of the target space as the effective fingerprinting height for indoor localization approaches.