Performance Analysis for Time Difference of Arrival Localization in Long-Range Networks

LoRa technology is a recent technology belonging to the Low Power and Wide Area Networks (LPWANs), which offers distinct advantages for wireless communications and possesses unique features. Among others, it can be used for localization procedures offering minimal energy consumption and quite long-r...

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Bibliographic Details
Published in:Smart cities (Basel) Vol. 7; no. 5; pp. 2514 - 2541
Main Authors: Daramouskas, Ioannis, Perikos, Isidoros, Paraskevas, Michael, Lappas, Vaios, Kapoulas, Vaggelis
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2024
Subjects:
Algorithms
Background noise
Chan’s method
Cities
Colleges & universities
Communication
Data encryption
Emergency procedures
Emergency response
Energy consumption
Error reduction
Experiments
Geodetic coordinates
hyperbolic localization
Internet of Things
Least squares
Localization
LoRa
Machine learning
Messages
Particle swarm optimization
Rescue operations
Search and rescue missions
Sensors
Smart cities
TDoA
Time measurement
Urban planning
Wide area networks
Wireless communications
Wireless networks
ISSN:2624-6511, 2624-6511
Online Access:Get full text
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Summary:LoRa technology is a recent technology belonging to the Low Power and Wide Area Networks (LPWANs), which offers distinct advantages for wireless communications and possesses unique features. Among others, it can be used for localization procedures offering minimal energy consumption and quite long-range transmissions. However, the exact capabilities of LoRa localization performance are yet to be employed thoroughly. This article examines the efficiency of the LoRa technology in localization tasks using Time Difference of Arrival (TDoA) measurements. An extensive and concrete experimental study was conducted in a real-world setup on the University of Patras campus, employing both real-world data and simulations to assess the precision of geodetic coordinate determination. Through our experiments, we implemented advanced localization algorithms, including Social Learning Particle Swarm Optimization (PSO), Least Squares, and Chan techniques. The results are quite interesting and highlight the conditions and parameters that result in accurate LoRa-based localization in real-world scenarios in smart cities. In our context, we were able to achieve state-of-the-art localization results reporting localization errors as low as 300 m in a quite complex 8 km × 6 km real-world environment.
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ISSN:2624-6511
2624-6511
DOI:10.3390/smartcities7050098