Impact of Spreading Factor on LoRaWAN Propagation in a Metropolitan Environment

Hosseinzadeh, Salaheddin; Ramzan, Naeem; Yousefi, Mehdi; Curtis, Krystyna; Larijani, Hadi

doi:10.1109/ucet51115.2020.9205422

Cited by 3 publications

(6 citation statements)

References 25 publications

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“…These features are extracted using deterministic approaches applied before the regression training. Authors of [10] deployed image processing to extract environmental features, such as base station altitude, AoA and AoD, clutter, number of buildings on the LoS path, NLoS to LoS ratio, street width, and street direction from GoogleMaps to train an MLP.…”

Section: Machine Learning For Propagation Modelingmentioning

confidence: 99%

“…One of the main advantages of LoRa, which has contributed to its popularity and extensive utilization, is its open-source technology, which enables autonomous and costeffective setup [9]. Additionally, the spreading factor (SF) of the proprietary chirp spread spectrum (CSS) modulation further improves the overall sensitivity of the device and increases interference immunity [10]. The impact of SF on the data rate and time-on-air can be precisely calculated [11], whereas its impact on propagation range and the link budget requires empirical experimentation.…”

Section: Introductionmentioning

confidence: 99%

“…The impact of SF on the data rate and time-on-air can be precisely calculated [11], whereas its impact on propagation range and the link budget requires empirical experimentation. For instance, Hosseinzadeh et al [10] observed an equivalent range extension of about 500 m when switching from SF 7 to SF 12 in an urban area. Additionally, adjusting the transmission power and SF can directly impact the per-bit energy consumption of the LoRa network [12].…”

Section: Introductionmentioning

confidence: 99%

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Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling

Hosseinzadeh,

Ashawa,

Owoh

et al. 2024

Sensors

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This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR’s spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads to a more accurate estimation and a better understanding of the LoRa’s propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Things networks.

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Section: Machine Learning For Propagation Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling

Hosseinzadeh,

Ashawa,

Owoh

et al. 2024

Sensors

Self Cite

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“…Based on this premise, to determine the reach of LoRa technology for the most diverse environments and to propose channel attenuation models according to RSSI, studies were carried out [46] - [48]. Furthermore, other radio propagation models that are widespread in the literature, such as the Log-distance model [51], Okumura-Hata [29], COST-231 [50], among others, were compare and use to determine path loss when using LoRa technology in the most diverse scenarios [49].…”

Section: Related Workmentioning

confidence: 99%

Large-Scale Modeling and Analysis of Uplink and Downlink Channels for LoRa Technology in Suburban Environments

Batalha

Lopes

Lima

et al. 2022

IEEE Internet Things J.

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“…However, among the several available LPWAN technologies, there are limited works dealing with experimental results that may help in the configuration of new applications. In particular, for the case of LoRaWAN, very few works address the deployment in an actual city or in a big urban extension [ 53 ], being this a relevant point given the differences between their real and model-based behavior [ 54 ]. Besides, Hybrid Wireless Sensor Networks (H-WSN), including at least one mobile node, have not received attention, despite their suitability for a number of applications, including urban monitoring, but also an emergency response, among others [ 55 , 56 , 57 ].…”

Section: Introductionmentioning

confidence: 99%

Development and Implementation of a Hybrid Wireless Sensor Network of Low Power and Long Range for Urban Environments

Bravo-Arrabal

Lozano

Serón

et al. 2021

Sensors

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The urban population, worldwide, is growing exponentially and with it the demand for information on pollution levels, vehicle traffic, or available parking, giving rise to citizens connected to their environment. This article presents an experimental long range (LoRa) and low power consumption network, with a combination of static and mobile wireless sensors (hybrid architecture) to tune and validate concentrator placement, to obtain a large coverage in an urban environment. A mobile node has been used, carrying a gateway and various sensors. The Activation By Personalization (ABP) mode has been used, justified for urban applications requiring multicasting. This allows to compare the coverage of each static gateway, being able to make practical decisions about its location. With this methodology, it has been possible to provide service to the city of Malaga, through a single concentrator node. The information acquired is synchronized in an external database, to monitor the data in real time, being able to geolocate the dataframes through web mapping services. This work presents the development and implementation of a hybrid wireless sensor network of long range and low power, configured and tuned to achieve efficient performance in a mid-size city, and tested in experiments in a real urban environment.

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Impact of Spreading Factor on LoRaWAN Propagation in a Metropolitan Environment

Cited by 3 publications

References 25 publications

Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling

Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling

Large-Scale Modeling and Analysis of Uplink and Downlink Channels for LoRa Technology in Suburban Environments

Development and Implementation of a Hybrid Wireless Sensor Network of Low Power and Long Range for Urban Environments

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