Smart indoor living advances in the recent decade, such as home indoor localization and positioning, has seen a significant need for low-cost localization systems based on freely available resources such as Received Signal Strength Indicator by the dense deployment of Wireless Local Area Networks (WLAN). The off-the-shelf user equipment (UE’s) available at an affordable price across the globe are well equipped with the functionality to scan the radio access network for hearable single strength; in complex indoor environments, multiple signals can be received at a particular reference point with no consideration of the height of the transmitter and possible broadcasting coverage. Most effective fingerprinting algorithm solutions require specialized labor, are time-consuming to carry out site surveys, training of the data, big data analysis, and in most cases, additional hardware requirements relatively increase energy consumption and cost, not forgetting that in case of changes in the indoor environment will highly affect the fingerprint due to interferences. This paper experimentally evaluates and proposes a novel technique for Received Signal Indicator (RSSI) distance prediction, leveraging transceiver height, and Fresnel ranging in a complex indoor environment to better suit the path loss of RSSI at a particular Reference Point (RP) and time, which further contributes greatly to indoor localization. The experimentation in different complex indoor environments of the corridor and office lab during work hours to ascertain real-life and time feasibility shows that the technique’s accuracy is greatly improved in the office room and the corridor, achieving lower average prediction errors at low-cost than the comparison prediction algorithms. Compared with the conventional prediction techniques, for example, with Access Point 1 (AP1), the proposed Height Dependence Path–Loss (HEM) model at 0 dBm error attains a confidence probability of 10.98%, higher than the 2.65% for the distance dependence of Path–Loss New Empirical Model (NEM), 4.2% for the Multi-Wall dependence on Path-Loss (MWM) model, and 0% for the Conventional one-slope Path-Loss (OSM) model, respectively. Online localization, amongst the hearable APs, it is seen the proposed HEM fingerprint localization based on the proposed HEM prediction model attains a confidence probability of 31% at 3 m, 55% at 6 m, 78% at 9 m, outperforming the NEM with 26%, 43%, 62%, 62%, the MWM with 23%, 43%, 66%, respectively. The robustness of the HEM fingerprint using diverse predicted test samples by the NEM and MWM models indicates better localization of 13% than comparison fingerprints.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.