A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements

Hosseinzadeh, Salaheddin; Almoathen, Mahmood; Larijani, Hadi; Curtis, Krystyna

doi:10.3390/bdcc1010007

Cited by 24 publications

(18 citation statements)

References 27 publications

(31 reference statements)

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“…To predict the received power at this pixel, we apply (17) with C and γ as per the inferred values in Table 3, but with η eff and κ eff as given in (18) and (19), respectively, given below. The effective values handle the heterogeneity in the propagation conditions.…”

Section: B Algorithm For Rssi Computation In a Heterogeneous Regionmentioning

confidence: 99%

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Coverage Estimation in Outdoor Heterogeneous Propagation Environments

Rathod

Subramanian²,

Sundaresan

2020

IEEE Access

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This paper is on a coverage estimation procedure for the deployment of outdoor Internet of Things (IoT). In the first part of the paper, a data-driven coverage estimation technique is proposed. The estimation technique combines multiple machine-learning-based regression ideas. The proposed technique achieves two purposes. The first purpose is to reduce the bias in the estimated received signal strength arising from estimations performed only on the successfully received packets. The second purpose is to exploit commonality of physical parameters, e.g. antenna-gain, in measurements that are made across multiple propagation environments. It also provides the correct link function for performing a nonlinear regression in our communication systems context. In the second part of the paper, a method to use readily available geographic information system (GIS) data (for classifying geographic areas into various propagation environments) followed by an algorithm for estimating received signal strength (which is motivated by the first part of the paper) is proposed. Together they enable quick and automated estimation of coverage in outdoor environments. It is anticipated that these will lead to faster and more efficient deployment of outdoor Internet of Things. INDEX TERMS Coverage, geographic information system (GIS), heterogeneous propagation environment, Internet of Things (IoT) deployment. NIHESH RATHOD received the B.E. degree in electronics and communication from Dharmsinh Desai University, Nadiad, in 2012, and the M.E. degree in telecommunication from the Indian Institute of Science, Bengaluru, in 2015, where he is currently pursuing the Ph.D. degree in communication and networks. Since 2015, he has been a Cisco Research Scholar with the Department of Electrical Communication Engineering, Indian Institute of Science. His interest includes the areas in design and implementation of the Internet of Things (IoT). RENU SUBRAMANIAN received the B.Tech. degree in electrical and electronics from the Rajiv

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Section: B Algorithm For Rssi Computation In a Heterogeneous Regionmentioning

confidence: 99%

“…Also, they use only 20 packets per link, which is much lower than our 1200 packets per link described in our experimental methodology and data collection section. Hosseinzadeh et al [19] proposed a neural network based correction to the COST-231 model. Similarly, Dobrilović et al [20] proposed an optimisation of the Lee propagation model.…”

Section: Introductionmentioning

confidence: 99%

Coverage Estimation in Outdoor Heterogeneous Propagation Environments

Rathod

Subramanian²,

Sundaresan

2020

IEEE Access

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“…While, in [20], a constant difference of 27 dB received signal power between Okumura-Hata and the LoRaWAN measurements was observed. An enhanced modified multi-wall propagation model and a neural network propagation model were presented by the authors in [21] and [22] respectively. Finally, a comparative performance analysis of Okumura-Hata, COST-231 Hata and COST-231 Walfish-Ikegami (COST-WI) propagation models was performed using the NS3 simulator and the measurements in an urban environment by Harinda et al [23].…”

Section: ) Itu-r 1225 Model: This Is a Radio Propagation Model Definmentioning

confidence: 99%

Performance Evaluation of Propagation Models for LoRaWAN in an Urban Environment

Ingabire

Larijani

Gibson

2020

2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)

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Low-Power, Wide-Area Networks (LPWAN) are projected to support a significant number of devices within the Internet of Things (IoT). Long-Range Wide Area Network (LoRaWAN) is an open specification and emerging LPWAN connectivity solution for IoT platforms. However, LoRaWAN network performance in urban scenarios is a fundamental research topic with limited exploration and characteristic analysis. In this paper, ATDI ICS Telecom is used to investigate LoRaWAN radio network coverage at 868 MHz using the Okumura-Hata, COST-231 Hata, Extended Hata, and ITU-R 1225 propagation models. The predicted received signal strength simulation results are compared with real-world test measurements taken in the urban environment of Glasgow City to evaluate various propagation models' accuracy. The proposed work demonstrates ITU R 225 and Extended Hata overestimated the real measured received signal strength power whereas, COST-231 Hata and Okumura-Hata underestimated the same signal power. Our results and analysis give important insights into the performance, evaluation, and comparison of existing propagation models for IoT connectivity with LoRaWAN technology within an urban environment.

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“…It is the ANN's responsibility to learn the FSPL from the distance and frequency of transmission or understand the attenuating impact of obstacles on the LoS. A review of the ANN-based models is provided in [19]. The drawbacks of this approach include:…”

Section: Hybrid and Artificial Neural Network-based Modelsmentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN

Hosseinzadeh

Larijani

Curtis

et al. 2019

ASI

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This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios.

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A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements

Cited by 24 publications

References 27 publications

Coverage Estimation in Outdoor Heterogeneous Propagation Environments

Coverage Estimation in Outdoor Heterogeneous Propagation Environments

Performance Evaluation of Propagation Models for LoRaWAN in an Urban Environment

An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN

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