A Hybrid Localization Algorithm for Enhanced Accuracy and Robustness in Healthcare Systems

Authors

  • Siti Nur Department of Computer Science, Lampung University, Bandar Lampung, Indonesia
  • Muhammad Ashfaq Department of Electronics Engineering, University of Engineering & Technology, Taxila, Pakistan

Keywords:

Hybrid localization, RSSI, Time of Arrival (ToA), Machine Learning, Healthcare, Localization

Abstract

This paper presents a novel hybrid localization algorithm designed for healthcare systems, integrating Received Signal Strength Indicator (RSSI) and Time of Arrival (ToA) measurements with machine learning techniques. The algorithm aims to enhance the accuracy, robustness, and computational efficiency of sensor localization in dynamic healthcare environments. Experimental results demonstrate that the hybrid algorithm achieves a significantly lower localization error, averaging 0.5 meters, compared to traditional RSSI-only and ToA-only methods. The algorithm's rapid convergence and low computational time make it suitable for real-time applications. Additionally, its robustness to measurement noise, a common challenge in healthcare settings, underscores its reliability. This research underscores the potential of advanced localization technologies to improve patient monitoring, safety, and overall healthcare delivery, with future work poised to further enhance performance and adaptability.

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Published

2024-08-01

How to Cite

[1]
S. Nur and M. Ashfaq, “A Hybrid Localization Algorithm for Enhanced Accuracy and Robustness in Healthcare Systems”, IJIRCST, vol. 12, no. 4, pp. 110–116, Aug. 2024.

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