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RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell.

Thomas Janssen¹, Rafael Berkvens¹, Maarten Weyn¹

¹IDLab-Faculty of Applied Engineering, University of Antwerp-imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium.

Sensors (Basel, Switzerland)

|November 3, 2020

Summary

Low Power Wide Area Networks (LPWAN) offer low-cost mobile transmitter localization. Optimized fingerprinting achieved the best accuracy (204m), but NB-IoT

Keywords:

LPWAN NB-IoT RSS localization mobility

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Area of Science:

Wireless Communication
Localization Technologies
Internet of Things

Background:

Low Power Wide Area Networks (LPWAN) provide a low-cost alternative to satellite localization.
Received Signal Strength (RSS) is a key parameter for LPWAN localization.

Purpose of the Study:

Evaluate the accuracy of three RSS-based localization algorithms in an urban environment.
Investigate the impact of NB-IoT mobility limitations on localization performance.

Main Methods:

Implemented and evaluated proximity, range-based, and optimized fingerprint-based localization algorithms.
Conducted large-scale urban field tests using a public Narrowband Internet of Things (NB-IoT) network.
Analyzed NB-IoT mobility issues, specifically the lack of handover support and its effect on localization.

Main Results:

Mean location estimation errors were 340m (proximity), 320m (range-based), and 204m (optimized fingerprint-based).
NB-IoT's single-cell localization limitation and lack of handover support were identified.
Reduced localization performance was observed due to increased cell reselection time and poor cell sector reliability.

Conclusions:

Optimized fingerprinting offers the highest accuracy among the tested RSS-based methods for NB-IoT localization.
Current NB-IoT hardware and software limitations, particularly mobility handling, significantly hinder localization accuracy.
Further research and development are needed to address NB-IoT mobility issues for improved location services.