Indoor localization for smart services requires cost-effective solutions that work on edge devices with limited power. BLE provides low power consumption but limited accuracy, while UWB provides accuracy at higher cost. This work is innovative because it combines these technologies to achieve practical accuracy with acceptable computational overhead.
This paper presents a solution for low-cost indoor localization using Bluetooth Low Energy (BLE) and ultra-wideband (UWB) RF technologies. The approach combines RSSI fingerprinting with UWB ranging to achieve accurate position estimation on resource-constrained edge devices. The work demonstrates practical implementation using Intel Edison boards with BLE beacons.
The paper demonstrates sub-meter indoor localization accuracy using a hybrid approach combining BLE RSSI fingerprinting with UWB ranging on Intel Edison edge devices. Results show computation time under 1 millisecond and practical accuracy improvements over single-technology approaches.
@inproceedings{Khare2017,
author = {Khare, Shweta Prabhat and Sallai, J{\'{a}}nos and Dubey, Abhishek and Gokhale, Aniruddha S.},
booktitle = {20th {IEEE} International Symposium on Real-Time Distributed Computing, {ISORC} 2017, Toronto, ON, Canada, May 16-18, 2017},
title = {Short Paper: Towards Low-Cost Indoor Localization Using Edge Computing Resources},
year = {2017},
pages = {28--31},
abstract = {Emerging smart services, such as indoor smart parking or patient monitoring and tracking in hospitals, incur a significant technical roadblock stemming primarily from a lack of cost-effective and easily deployable localization framework that impedes their widespread deployment. To address this concern, in this paper we present a low-cost, indoor localization and navigation system, which performs continuous and real-time processing of Bluetooth Low Energy (BLE) and IEEE 802.15.4a compliant Ultra-wideband (UWB) sensor data to localize and navigate the concerned entity to its desired location. Our approach depends upon fusing the two feature sets, using the UWB to calibrate the BLE localization mechanism.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/isorc/KhareSDG17},
category = {selectiveconference},
contribution = {lead},
doi = {10.1109/ISORC.2017.23},
file = {:Khare2017-Short_Paper_Towards_Low-Cost_Indoor_Localization_Using_Edge_Computing_Resources.pdf:PDF},
keywords = {indoor localization, BLE, ultra-wideband, RSSI fingerprinting, edge computing},
project = {cps-middleware},
tag = {transit},
timestamp = {Wed, 16 Oct 2019 14:14:53 +0200},
url = {https://doi.org/10.1109/ISORC.2017.23}
}