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

• Type of Material: Article
• Authors: Yashar Kiarashi, Emory University; Soheil Saghafi, Emory University; Barun Das, Emory University; Chaitra Hegde, Georgia Institute of Technology; Venkata Siva Krishna Madala, Georgia Institute of Technology; Arjun Sinh Nakum, Georgia Institute of Technology; Ratan Singh, Georgia Institute of Technology; Robert Tweedy, Emory University; Matt Doiron, Emory University; Amy D. Rodriguez, Emory University; Allan I Levey, Emory University; Gari D. Clifford, Emory University; Hyeokhyen Kwon, Emory University
• Language: English
• Date: 2023-11-30
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2023 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): https://doi.org/10.3390/s23239517
• Title of Journal or Parent Work: Sensors (Basel)
• Volume: 23
• Issue: 23
• Start Page: 9517
• Grant/Funding Information: This research was funded by James M. Cox Foundation and Cox Enterprises, Inc. grant number cox-emory2019.; The Cognitive Empowerment program is supported by a generous investment from the James M. Cox Foundation and Cox Enterprises, Inc., in support of Emory’s Brain Health Center and the Georgia Institute of Technology.
• Keywords: Bluetooth low energy; ambient health monitoring; indoor localization; edge computing; cloud computing
• Research Categories: Psychology, Cognitive; Computer Science

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.