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Summary

This study optimizes an indoor positioning system (IPS) using Bluetooth Low-Energy (BLE) beacons and machine learning. The optimized system efficiently estimates user location on resource-constrained embedded devices.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Indoor positioning systems (IPS) are crucial for various applications but face challenges with accuracy and resource efficiency.
  • Miniaturized embedded systems require optimized algorithms for effective operation.

Purpose of the Study:

  • To evaluate and optimize a novel indoor positioning system (IPS) for portable and embedded devices.
  • To enhance the performance of Received Signal Strength Indicator (RSSI) based positioning using machine learning.

Main Methods:

  • Utilized Received Signal Strength Indicator (RSSI) from Bluetooth Low-Energy (BLE) beacons.
  • Applied a Received Signal Strength Indicator (RSSI) filter and multiple machine learning (ML) models for distance inference.
  • Implemented model reduction techniques for resource-constrained embedded systems.
  • Solved the positioning problem using multilateration with inferred distances.

Main Results:

  • Evaluated various ML models and optimization algorithms for accurate distance estimation.
  • Demonstrated successful implementation of optimized algorithms on embedded systems.
  • Showcased the efficiency and effectiveness of the proposed indoor positioning method.

Conclusions:

  • The proposed method offers an efficient and optimized solution for indoor positioning on embedded systems.
  • Machine learning and model reduction are key to achieving high performance in resource-limited environments.
  • This work contributes to the advancement of practical indoor positioning technologies.