Verification of a Probabilistic Model and Optimization in Long-Range Networks

This paper presents a comprehensive probabilistic analysis of packet loss in long-range (LoRa) networks, a vital aspect of low-power, wide-area communication systems increasingly employed in IoT applications. The proposed model integrates multiple critical factors, including packet arrival rates, tr...

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Main Authors: José Luis Romero Vázquez, Abel García-Barrientos, José Alberto Del-Puerto-Flores, Francisco R. Castillo Soria, Roilhi F. Ibarra-Hernández, Ulises Pineda Rico, Ernesto Zambrano-Serrano
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Applied Sciences
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Online Access:https://www.mdpi.com/2076-3417/15/4/1873
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author José Luis Romero Vázquez
Abel García-Barrientos
José Alberto Del-Puerto-Flores
Francisco R. Castillo Soria
Roilhi F. Ibarra-Hernández
Ulises Pineda Rico
Ernesto Zambrano-Serrano
author_facet José Luis Romero Vázquez
Abel García-Barrientos
José Alberto Del-Puerto-Flores
Francisco R. Castillo Soria
Roilhi F. Ibarra-Hernández
Ulises Pineda Rico
Ernesto Zambrano-Serrano
author_sort José Luis Romero Vázquez
collection DOAJ
description This paper presents a comprehensive probabilistic analysis of packet loss in long-range (LoRa) networks, a vital aspect of low-power, wide-area communication systems increasingly employed in IoT applications. The proposed model integrates multiple critical factors, including packet arrival rates, transmission power levels, and the distance between transmitting nodes and the gateway. By incorporating these variables into a unified probabilistic framework, the model not only predicts packet loss and interference patterns but also provides insights into optimizing network parameters. Specifically, it focuses on determining the optimal transmission power required to balance energy efficiency and communication reliability. A distinctive feature of the analysis is its ability to adapt dynamically to varying network conditions, ensuring sustained performance even in environments with high node density or fluctuating traffic loads. The study also explores the interplay between transmission power and interference, demonstrating how careful calibration of power settings can significantly reduce packet collisions while conserving energy resources. The proposed framework not only advances theoretical understanding, but also offers actionable guidelines for network designers seeking to achieve high performance in resource-constrained environments.
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issn 2076-3417
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publishDate 2025-02-01
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series Applied Sciences
spelling doaj-art-e00a3f6e7fd9462ab533e230cc6530792025-08-20T03:12:20ZengMDPI AGApplied Sciences2076-34172025-02-01154187310.3390/app15041873Verification of a Probabilistic Model and Optimization in Long-Range NetworksJosé Luis Romero Vázquez0Abel García-Barrientos1José Alberto Del-Puerto-Flores2Francisco R. Castillo Soria3Roilhi F. Ibarra-Hernández4Ulises Pineda Rico5Ernesto Zambrano-Serrano6Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí 78295, MexicoFacultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí 78295, MexicoFacultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, MexicoFacultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí 78295, MexicoFacultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí 78295, MexicoFacultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí 78295, MexicoFacultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, San Nicolás de los Garza 66455, MexicoThis paper presents a comprehensive probabilistic analysis of packet loss in long-range (LoRa) networks, a vital aspect of low-power, wide-area communication systems increasingly employed in IoT applications. The proposed model integrates multiple critical factors, including packet arrival rates, transmission power levels, and the distance between transmitting nodes and the gateway. By incorporating these variables into a unified probabilistic framework, the model not only predicts packet loss and interference patterns but also provides insights into optimizing network parameters. Specifically, it focuses on determining the optimal transmission power required to balance energy efficiency and communication reliability. A distinctive feature of the analysis is its ability to adapt dynamically to varying network conditions, ensuring sustained performance even in environments with high node density or fluctuating traffic loads. The study also explores the interplay between transmission power and interference, demonstrating how careful calibration of power settings can significantly reduce packet collisions while conserving energy resources. The proposed framework not only advances theoretical understanding, but also offers actionable guidelines for network designers seeking to achieve high performance in resource-constrained environments.https://www.mdpi.com/2076-3417/15/4/1873packet loss optimizationRSSIIoTprobabilistic analysisLoRa networkstransmission power
spellingShingle José Luis Romero Vázquez
Abel García-Barrientos
José Alberto Del-Puerto-Flores
Francisco R. Castillo Soria
Roilhi F. Ibarra-Hernández
Ulises Pineda Rico
Ernesto Zambrano-Serrano
Verification of a Probabilistic Model and Optimization in Long-Range Networks
Applied Sciences
packet loss optimization
RSSI
IoT
probabilistic analysis
LoRa networks
transmission power
title Verification of a Probabilistic Model and Optimization in Long-Range Networks
title_full Verification of a Probabilistic Model and Optimization in Long-Range Networks
title_fullStr Verification of a Probabilistic Model and Optimization in Long-Range Networks
title_full_unstemmed Verification of a Probabilistic Model and Optimization in Long-Range Networks
title_short Verification of a Probabilistic Model and Optimization in Long-Range Networks
title_sort verification of a probabilistic model and optimization in long range networks
topic packet loss optimization
RSSI
IoT
probabilistic analysis
LoRa networks
transmission power
url https://www.mdpi.com/2076-3417/15/4/1873
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