Graph Trilateration for Indoor Localization in Sparsely Distributed Edge Computing Devices in Complex Environments Using Bluetooth Technology

Abstract

Spatial navigation patterns in indoor space usage can reveal important cues about the cognitive health of participants. In this work, we present a low-cost, scalable, open-source edge computing system using Bluetooth low energy (BLE) beacons for tracking indoor movements in a large, 1700 m2 facility used to carry out therapeutic activities for participants with mild cognitive impairment (MCI). The facility is instrumented with 39 edge computing systems, along with an on-premise fog server. The participants carry a BLE beacon, in which BLE signals are received and analyzed by the edge computing systems. Edge computing systems are sparsely distributed in the wide, complex indoor space, challenging the standard trilateration technique for localizing subjects, which assumes a dense installation of BLE beacons. We propose a graph trilateration approach that considers the temporal density of hits from the BLE beacon to surrounding edge devices to handle the inconsistent coverage of edge devices. This proposed method helps us tackle the varying signal strength, which leads to intermittent detection of beacons. The proposed method can pinpoint the positions of multiple participants with an average error of 4.4 m and over 85% accuracy in region-level localization across the entire study area. Our experimental results, evaluated in a clinical environment, suggest that an ordinary medical facility can be transformed into a smart space that enables automatic assessment of individuals’ movements, which may reflect health status or response to treatment.

A Self-Localization Algorithm for Mobile Targets in Indoor Wireless Sensor Networks Using Wake-Up Media Access Control Protocol

“…The proposed methods present performed results for a small area scale by 1.87 m and 1.81 m compared to results presented in [17,38], respectively. In [39], the authors used an area of 1700 m 2 in a complex environment and presented an average error of 4.4 m with BLE technology, which costs more than the technology used and has lower range detection. For [40], the authors proposed optimized propagation model parameters in order to optimize parameters for trilateration localization.…”

“…For [40], the authors proposed optimized propagation model parameters in order to optimize parameters for trilateration localization. Their experiments covered a space of 1052 m 2 and yielded an average error of 3.75 m. Comparatively, the proposed approach demonstrated superior performance in larger areas, showing improvements of 1.23 m and 1.88 m, respectively, for similar spatial ranges [39,40]. Balancing the need to localize in all positions with energy efficiency remains challenging.…”

Adaptive Network Model for Assisting People with Disabilities through Crowd Monitoring and Control

“…However, it usually has an accuracy of 4 to 5 m and cannot operate underground, which makes it inappropriate for this application. The latest Bluetooth technologies have a high location accuracy in the centimeter range and can work quite well indoors and outdoors [ 16 , 17 , 18 ]. However, it may have a low signal strength for larger neighborhoods, which can cause loss or delays.…”