Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area

Self Cite

Designing and deploying telecommunications and broadcasting networks in the challenging terrain of the Amazon region pose significant obstacles due to its unique morphological characteristics. Within low-power wide-area networks (LPWANs), this research study introduces a comprehensive approach to modeling large-scale propagation loss channels specific to the LoRaWAN protocol operating at 915 MHz. The objective of this study is to facilitate the planning of Internet of Things (IoT) networks in riverside communities while accounting for the mobility of end nodes. We conducted extensive measurement campaigns along the banks of Universidade Federal do Pará, capturing received signal strength indication (RSSI), signal-to-noise ratio (SNR), and geolocated point data across various spreading factors. We fitted the empirical close-in (CI) and floating intercept (FI) propagation models for uplink path loss prediction and compared them with the Okumura–Hata model. We also present a new model for path loss with dense vegetation. Furthermore, we calculated received packet rate statistics between communication links to assess channel quality for the LoRa physical layer (PHY). Remarkably, both CI and FI models exhibited similar behaviors, with the newly proposed model demonstrating enhanced accuracy in estimating radio loss within densely vegetated scenarios, boasting lower root mean square error (RMSE) values than the Okumura–Hata model, particularly for spreading factor 9 (SF9). The radius coverage threshold, accounting for node mobility, was 945 m. This comprehensive analysis contributes valuable insights for the effective deployment and optimization of LoRa-based IoT networks in the intricate environmental conditions of the Amazon region.

Section: Resultsmentioning

confidence: 99%

LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions

Lima,

Lopes,

Cardoso

et al. 2024

Self Cite

“…However, to use ML models, it is necessary to realize data acquisition, which will be employed to train different models. In addition, to the best of the authors’ acknowledge, few are the works that collect data to represent signal propagation in an Amazon wooded environment, as seen in [ 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments

Barbosa,

Cruz,

Macedo

et al. 2024

Self Cite

The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with the data transmission between IoT devices, resulting in the need for signal propagation modeling, which considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two machine learning-based propagation models, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors, such as the transmitter’s height relative to the trunk, the beginning of foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12, and the co-polarization of the transmitter and receiver antennas. The proposed models demonstrated higher accuracy, achieving values of root mean square error (RMSE) of 3.86 dB and standard deviation (SD) of 3.8614 dB, respectively, compared to existing empirical models like CI, FI, Early ITU-R, COST235, Weissberger, and FITU-R. The significance of this study lies in its potential to boost wireless communications in wooded environments. Furthermore, this research contributes to enhancing more efficient and robust LoRa networks for applications in agriculture, environmental monitoring, and smart urban infrastructure.

“…LoRa technology has stood out in the domain of wireless sensor networks (WSNs) due to its long-range capabilities [ 1 ]. This technology enables low-power, low-cost, and reliable data transmission, offering a versatile and responsive framework for diverse applications ranging from agriculture and environmental monitoring to smart cities and industrial automation [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Deploying a sensor network in real-world environments requires accurate estimations of the received signal.…”

Section: Introductionmentioning

confidence: 99%

“…COST 231-Hata is utilized in [ 32 , 34 , 36 , 46 , 48 , 50 ], the COST–Walfisch–Ikegami model is used in [ 33 , 46 , 48 , 51 ], the 3GPP model is used in [ 31 , 32 , 33 , 48 , 53 ], and the SUI model is used in [ 33 , 35 , 48 , 50 ]. We also found the use of the Lee model [ 54 ], the Irregular Terrain With Obstructions Model (ITWOM) [ 42 , 55 ], the ITU-R P 526 model [ 39 ], the ITU-R P 1812 model [ 56 , 57 ], the Irregular Terrain Model (ITM) [ 47 , 58 , 59 ], the Ericsson model [ 33 , 35 , 48 ], the Egli model [ 5 , 56 ], the ECC33 model [ 49 ], and the UFPA model [ 6 ]. Some works also include variations of the well-known models.…”

Section: Introductionmentioning

confidence: 99%

A Critical Review of the Propagation Models Employed in LoRa Systems

Azevedo,

Mendonça

2024

LoRa systems are emerging as a promising technology for wireless sensor networks due to their exceptional range and low power consumption. The successful deployment of LoRa networks relies on accurate propagation models to facilitate effective network planning. Therefore, this review explores the landscape of propagation models supporting LoRa networks. Specifically, we examine empirical propagation models commonly employed in communication systems, assessing their applicability across various environments such as outdoor, indoor, and within vegetation. Our investigation underscores the prevalence of logarithmic decay in most empirical models. In addition, we survey the relationship between model parameters and environmental factors, clearing their nuanced interplay. Analyzing published measurement results, we extract the log-distance model parameters to decipher environmental influences comprehensively. Drawing insights from published measurement results for LoRa, we compare them with the model’s outcomes, highlighting successes and limitations. We additionally explore the application of multi-slope models to LoRa measurements to evaluate its effectiveness in enhancing the accuracy of path loss prediction. Finally, we propose new lines for future research in propagation modelling to improve empirical models.