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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Related Experiment Video

Updated: Sep 13, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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Indoor Localization Using Multi-Bluetooth Beacon Deployment in a Sparse Edge Computing Environment.

Soheil Saghafi1, Yashar Kiarashi1, Amy D Rodriguez2

  • 1Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA.

Digital Twins and Applications
|July 30, 2025
PubMed
Summary
This summary is machine-generated.

Optimizing indoor localization with Bluetooth low energy (BLE) beacons significantly improves accuracy. Using three BLE beacons reduces localization error in RF-dense environments, with additional beacons offering minimal gains.

Keywords:
ambient health monitoringbluetooth low energycloud computingedge computingindoor localization

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Area of Science:

  • Engineering
  • Computer Science
  • Signal Processing

Background:

  • Bluetooth low energy (BLE) offers cost-effective and low-power indoor localization solutions.
  • Received Signal Strength Indicator (RSSI) variability due to environmental factors challenges BLE localization accuracy.
  • Radio frequency (RF)-dense modern buildings present complex environments for indoor positioning.

Purpose of the Study:

  • To present an optimized method for enhancing indoor localization accuracy using multiple BLE beacons.
  • To evaluate the impact of beacon quantity on localization performance in RF-dense settings.
  • To develop and release an open-source BLE localization framework for wider adoption.

Main Methods:

  • Utilized multiple BLE beacons for indoor localization in a modern building environment.
  • Implemented a proof-of-concept study to assess localization error reduction.
  • Deployed the localization framework on an edge network of Raspberry Pi devices.

Main Results:

  • Localization error was significantly reduced from a worst-case distance of 9.09 m to 2.94 m by using three BLE beacons.
  • Employing more than three BLE beacons yielded minimal incremental improvements in localization accuracy.
  • The developed BLE localization framework was successfully implemented on an edge computing network.

Conclusions:

  • Three BLE beacons are optimal for enhancing indoor localization accuracy in RF-dense environments.
  • The open-source release of the BLE localization framework facilitates broader research and application.
  • This optimized approach addresses RSSI variability challenges for more reliable indoor positioning.