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LoRa Communication Technology for IoT Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 3936

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Dr. Luca Leonardi

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Department of Electrical, Electronic and Computer Engineering, University of Catania—UNICT, Catania, Italy
Interests: real-time industrial networks; low power wide area networks; wireless sensor and actuator networks; industrial internet of things; automotive communications
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Special Issue Information

Dear Colleagues,

Low-power wide area networks (LPWANs) represent a novel communication paradigm that will replace or complement traditional cellular and short-range wireless technologies in several applications. In the Internet of Things (IoT) field, LPWANs are expected to offer energy-efficient connectivity to a high number of low-power devices, distributed over very large geographical areas. In this context, LoRa is a promising LPWAN technology for inter-connecting billions of low-power IoT nodes. We envision that an increasing number of IoT nodes will be deployed and connected to the Internet via LoRa to enable various innovative applications in several domains, including smart cities, smart monitoring, healthcare, and factory automation. We face great practical challenges and research opportunities in the design, implementation, and evaluation of LoRa technology and its applications and system developments.

This Special Issue is focused on LPWAN technologies, and in particular on LoRa, addressing (but not limited to) the following topics:

Experimental deployments and solutions for mobile scenarios or situations where devices are deployed in a wide area;
Machine learning techniques for the configuration and management of LoRa-based communications;
Novel physical layer design and optimization for LoRa;
Novel link layer and network layer design and implementation for LoRa;
Co-existence and co-operation of LoRa with other wireless technologies in ISM bands;
Security aspects of LoRa.

Dr. Luca Leonardi
Guest Editor

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Keywords

LoRa
LoRaWAN
LPWANs
IoT and IIoT
wide area coverage
mobile communications
network configuration
network management
machine learning techniques
security

Published Papers (4 papers)

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Research

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24 pages, 20381 KiB

Open AccessArticle

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

by Brenda S. de S. Barbosa, Hugo A. O. Cruz, Alex S. Macedo, Caio M. M. Cardoso, Filipe C. Fernandes, Leslye E. C. Eras, Jasmine P. L. de Araújo, Gervásio P. S. Calvacante and Fabrício J. B. Barros

Sensors 2024, 24(8), 2542; https://doi.org/10.3390/s24082542 - 16 Apr 2024

Viewed by 442

Abstract

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. Full article

(This article belongs to the Special Issue LoRa Communication Technology for IoT Applications)

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18 pages, 3473 KiB

Open AccessArticle

An Environment-Aware Adaptive Data-Gathering Method for Packet-Level Index Modulation in LPWA

by Osamu Takyu, Keita Takeda, Ryuji Miyamoto, Koichi Adachi, Mai Ohta and Takeo Fujii

Sensors 2024, 24(8), 2514; https://doi.org/10.3390/s24082514 - 14 Apr 2024

Viewed by 281

Abstract

Low-power wide-area (LPWA) is a communication technology for the IoT that allows low power consumption and long-range communication. Additionally, packet-level index modulation (PLIM) can transmit additional information using multiple frequency channels and time slots. However, in a competitive radio access environment, where multiple sensors autonomously determine packet transmission, packet collisions occur when transmitting the same information. The packet collisions cause a reduction in the throughput. A method has been proposed to design a mapping table that shows the correspondence between indexes and information using a packet collision minimization criterion. However, the effectiveness of this method depends on how the probability of the occurrence of the information to be transmitted is modeled. We propose an environment-aware adaptive data-gathering method that identifies the location of factors affecting sensor information and constructs a model for the probability of the occurrence of sensor information. The packet collision rate of the environment-aware adaptive data-gathering method was clarified through computer simulations and actual experiments on a 429 MHz LPWA. We confirm that the proposed scheme improves the packet collision rate by 15% in the computer simulation and 30% in the experimental evaluation, respectively. Full article

(This article belongs to the Special Issue LoRa Communication Technology for IoT Applications)

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17 pages, 6721 KiB

Open AccessArticle

LoRaWAN for Vehicular Networking: Field Tests for Vehicle-to-Roadside Communication

by Gabriele Di Renzone, Stefano Parrino, Giacomo Peruzzi, Alessandro Pozzebon and Lorenzo Vangelista

Sensors 2024, 24(6), 1801; https://doi.org/10.3390/s24061801 - 11 Mar 2024

Viewed by 512

Abstract

Vehicular wireless networks are one of the most valuable tools for monitoring platforms in the automotive domain. At the same time, Internet of Things (IoT) solutions are playing a crucial role in the same framework, allowing users to connect to vehicles in order to gather data related to their working cycle. Such tasks can be accomplished by resorting to either cellular or non-cellular wireless technologies. While the former can ensure low latency but require high running costs, the latter can be employed in quasi-real-time applications but definitely reduce costs. To this end, this paper proposes the results of two measurement campaigns aimed at assessing the performance of the long-range wide-area network (LoRaWAN) protocol when it is exploited as an enabling technology to provide vehicles with connectivity. Performances are evaluated in terms of packet loss (PL) and received signal strength indicator (RSSI) in wireless links. The two testing scenarios consisted of a transmitter installed on a motorbike running on an elliptical track and a receiver placed in the centre of the track, and a transmitter installed on the roof of a car and a receiver placed next to a straight road. Several speeds were tested, and all the spreading factors (SFs) foreseen by the protocol were examined, showing that the Doppler effect has a marginal influence on the receiving performance of the technology, and that, on the whole, performance is not significantly affected by the speed. Such results prove the feasibility of LoRaWAN links for vehicular network purposes. Full article

(This article belongs to the Special Issue LoRa Communication Technology for IoT Applications)

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Review

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36 pages, 1074 KiB

Open AccessReview

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

by Arshad Farhad and Jae-Young Pyun

Sensors 2023, 23(15), 6851; https://doi.org/10.3390/s23156851 - 01 Aug 2023

Cited by 4 | Viewed by 2201

Abstract

The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network. Full article

(This article belongs to the Special Issue LoRa Communication Technology for IoT Applications)

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