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Cityscape LoRa Signal Propagation Predicted and Tested Using Real-World Building-Data Based O-FDTD Simulations and Experimental Characterization

The age of the Internet of Things (IoT) and smart cities calls for low-power wireless communication networks, for which the Long-Range (LoRa) is a rising star. Efficient network engineering requires the accurate prediction of the Received Signal Strength Indicator (RSSI) spatial distribution. Howeve...

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Detalles Bibliográficos
Autores principales: Adão, Ricardo M. R., Balvís, Eduardo, Carpentier, Alicia V., Michinel, Humberto, Nieder, Jana B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8069062/
https://www.ncbi.nlm.nih.gov/pubmed/33921509
http://dx.doi.org/10.3390/s21082717
Descripción
Sumario:The age of the Internet of Things (IoT) and smart cities calls for low-power wireless communication networks, for which the Long-Range (LoRa) is a rising star. Efficient network engineering requires the accurate prediction of the Received Signal Strength Indicator (RSSI) spatial distribution. However, the most commonly used models either lack the physical accurateness, resolution, or versatility for cityscape real-world building distribution-based RSSI predictions. For this purpose, we apply the 2D electric field wave-propagation Oscillator Finite-Difference Time-Domain (O-FDTD) method, using the complex dielectric permittivity to model reflection and absorption effects by concrete walls and the receiver sensitivity as the threshold to obtain a simulated coverage area in a 600 × 600 m(2) square. Further, we report a simple and low-cost method to experimentally determine the signal coverage area based on mapping communication response-time delays. The simulations show a strong building influence on the RSSI, compared against the Free-Space Path (FSPL) model. We obtain a spatial overlap of 84% between the O-FDTD simulated and experimental signal coverage maps. Our proof-of-concept approach is thoroughly discussed compared to previous works, outlining error sources and possible future improvements. O-FDTD is demonstrated to be most promising for both indoors and outdoors applications and presents a powerful tool for IoT and smart city planners.