Smart City Gnosys
Smart city article details
| Title | Performance Analysis For Time Difference Of Arrival Localization In Long-Range Networks |
|---|---|
| ID_Doc | 41631 |
| Authors | Daramouskas I.; Perikos I.; Paraskevas M.; Lappas V.; Kapoulas V. |
| Year | 2024 |
| Published | Smart Cities, 7, 5 |
| DOI | http://dx.doi.org/10.3390/smartcities7050098 |
| Abstract | Highlights: What are the main findings? Noise can cause discrepancies in the estimated distances, including negative values, indicating inaccuracies in reference base station identification. Using sets of ten messages to average TDoA measurements helps smooth out random fluctuations and mitigate multipath and environmental noise. Advanced algorithms such as Social Learning Particle Swarm Optimization (SL-PSO) and Least Squares are effective in handling noisy data and improving localization accuracy. The Chan algorithm performed less reliably under high noise conditions, often failing to provide accurate solutions. Grouping messages reduces the impact of noise and improves the accuracy of localization. What are the implications of the main finding? The reduction in localization errors through noise mitigation and message grouping indicates that more reliable and precise positioning can be achieved in smart city applications. Better services in applications such as asset tracking, autonomous vehicles, and emergency response. For applications such as search and rescue operations, improved localization accuracy means quicker and more reliable identification of individuals in need. This can significantly enhance the safety and efficiency of such operations. The insights gained from this study can inform policymakers and city planners about the technical requirements and benefits of deploying advanced localization systems in urban areas. This can support strategic decisions in smart city planning and development. 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. © 2024 by the authors. |
| Author Keywords | Chan’s method; hyperbolic localization; localization; LoRa; particle swarm optimization; TDoA |
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