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Big Data Cogn. Comput. 2017, 1(1), 7; doi:10.3390/bdcc1010007

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

School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK
Computer Technology Department, Qatif College of Technology, Al Qatif 32636, Saudi Arabia
Author to whom correspondence should be addressed.
Received: 8 October 2017 / Revised: 8 December 2017 / Accepted: 9 December 2017 / Published: 14 December 2017
(This article belongs to the Special Issue Cognitive Services Integrating with Big Data, Clouds and IoT)
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Among the many technologies competing for the Internet of Things (IoT), one of the most promising and fast-growing technologies in this landscape is the Low-Power Wide-Area Network (LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has previously been studied for outdoor environments. However, this article focuses on end-to-end propagation in an outdoor–indoor scenario. This article will investigate how the reported and documented outdoor metrics are interpreted for an indoor environment. Furthermore, to facilitate network planning and coverage prediction, a novel hybrid propagation estimation method has been developed and examined. This hybrid model is comprised of an artificial neural network (ANN) and an optimized Multi-Wall Model (MWM). Subsequently, real-world measurements were collected and compared against different propagation models. For benchmarking, log-distance and COST231 models were used due to their simplicity. It was observed and concluded that: (a) the propagation of the LoRa Wide-Area Network (LoRaWAN) is limited to a much shorter range in this investigated environment compared with outdoor reports; (b) log-distance and COST231 models do not yield an accurate estimate of propagation characteristics for outdoor–indoor scenarios; (c) this lack of accuracy can be addressed by adjusting the COST231 model, to account for the outdoor propagation; (d) a feedforward neural network combined with a COST231 model improves the accuracy of the predictions. This work demonstrates practical results and provides an insight into the LoRaWAN’s propagation in similar scenarios. This could facilitate network planning for outdoor–indoor environments. View Full-Text
Keywords: LoRaWAN; LPWAN; propagation analysis and modeling; feedforward neural networks; COST231 multi-wall model LoRaWAN; LPWAN; propagation analysis and modeling; feedforward neural networks; COST231 multi-wall model

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hosseinzadeh, S.; Almoathen, M.; Larijani, H.; Curtis, K. A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements. Big Data Cogn. Comput. 2017, 1, 7.

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