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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 2025 | Viewed by 17395

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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
Special Issues, Collections and Topics in MDPI journals

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

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Published Papers (10 papers)

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Research

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15 pages, 5657 KiB

Open AccessArticle

Nanosecond Time Synchronization over a 2.4 GHz Long-Range Wireless Link

by Pascal Müller, Dominic Berger and Luciano Sarperi

Sensors 2025, 25(7), 1961; https://doi.org/10.3390/s25071961 - 21 Mar 2025

Viewed by 206

Abstract

Time synchronization between geographically separated equipment, such as, for example, that required in sensor networks for radio localization, is often based on global navigation satellite systems (GNSSs). However, in environments that are GNSS-denied due to signal blockage or interference, alternative timing synchronization methods are necessary. In this work, an experimental wireless time synchronization system based on long-range (LoRa) modulation has been developed and tested in the field. LoRa modulation operating in the license-free 2.4 GHz industrial, scientific and medical (ISM) band was chosen due to the potentially large coverage area of several kilometers and the availability of a ranging engine in the SX1280 transceiver by Semtech, which facilitates the implementation of time synchronization. The prototype system was tested over 170 m, where it achieved a time deviation (TDEV) of 30 ps for an average time of 1 s and a maximum TDEV of 3 ns over one day of measurement, improving over existing work on time synchronization with LoRa modulation by around three orders of magnitude. The field tests showed that ns accuracy can be achieved using LoRa modulation, making it suitable for the synchronization of remote sites, for example, for radio localization. Full article

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

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24 pages, 586 KiB

Open AccessArticle

Performance Evaluation of a Mesh-Topology LoRa Network

by Thomas Gerhardus Durand and Marthinus Johannes Booysen

Sensors 2025, 25(5), 1602; https://doi.org/10.3390/s25051602 - 5 Mar 2025

Viewed by 484

Abstract

Research into, and the usage of, Low-Power Wide-Area Networks (LPWANs) has increased significantly to support the ever-expanding requirements set by IoT applications. Specifically, the usage of Long-Range Wide-Area Networks (LoRaWANs) has increased, due to the LPWAN’s robust physical layer, Long-Range (LoRa), modulation scheme, which enables scalable, low-power consumption, long-range communication to IoT devices. The LoRaWAN Medium Access Control (MAC) protocol is currently limited to only support single-hop communication. This limits the coverage of a single gateway and increases the power consumption of devices which are located at the edge of a gateway’s coverage range. There is currently no standardised and commercialised multi-hop LoRa-based network, and the field is experiencing ongoing research. In this work, we propose a complementary network to LoRaWAN, which integrates mesh networking. An ns-3 simulation model has been developed, and the proposed LoRaMesh network is simulated for a varying number of scenarios. This research focuses on the design decisions needed to design a LoRa-based mesh network which maintains the low-power consumption advantages that LoRaWAN offers while ensuring that data packets are routed successfully to the gateway. The results highlighted a significant increase in the packet delivery ratio in nodes located far from a centralised gateway in a dense network. Nodes located further than 5.8 km from a gateway’s packet delivery ratio were increased from an average of 40.2% to 73.78%. The findings in this article validate the concept of a mesh-type LPWAN network based on the LoRa physical layer and highlight the potential for future optimisation. Full article

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

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20 pages, 38855 KiB

Open AccessArticle

A Self-Configurable BUS Network Topology Based on LoRa Nodes for the Transmission of Data and Alarm Messages in Power Line-Monitoring Systems

by Bartomeu Alorda-Ladaria, Marta Pons and Eugeni Isern

Sensors 2025, 25(5), 1484; https://doi.org/10.3390/s25051484 - 28 Feb 2025

Viewed by 434

Abstract

Power transmission lines transfer energy between power plants and substations by means of a linear chain of towers. These towers are often situated over extensive distances, sometimes in regions that are difficult to access. Wireless sensor networks present a viable solution for monitoring these long chains of towers due to their wide coverage, ease of installation and cost-effectiveness. The proposed LoRaBUS approach implements and analyses the benefits of a linear topology using a mixture of LoRa and LoRaWAN protocols. This approach is designed to enable automatic detection of nearby nodes, optimise energy consumption and provide a prioritised transmission mode in emergency situations. On remote, hard-to-reach towers, a prototype fire protection system was implemented and tested. The results demonstrate that LoRaBUS creates a self-configurable linear topology which proves advantageous for installation processes, node maintenance and troubleshooting node failures. The discovery process collects data from a neighbourhood to construct the network and to save energy. The network’s autonomous configuration can be completed within approximately 2 min. In addition, energy consumption is effectively reduced 25% by dynamically adjusting the transmission power based on the detected channel quality and the distance to the nearest neighbour nodes. Full article

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

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21 pages, 5140 KiB

Open AccessArticle

LoRa Resource Allocation Algorithm for Higher Data Rates

by Hossein Keshmiri, Gazi M. E. Rahman and Khan A. Wahid

Sensors 2025, 25(2), 518; https://doi.org/10.3390/s25020518 - 17 Jan 2025

Viewed by 830

Abstract

LoRa modulation is a widely used technology known for its long-range transmission capabilities, making it ideal for applications with low data rate requirements, such as IoT-enabled sensor networks. However, its inherent low data rate poses a challenge for applications that require higher throughput, such as video surveillance and disaster monitoring, where large image files must be transmitted over long distances in areas with limited communication infrastructure. In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time (T_tx) and increasing the bit rate (BR). The LRA algorithm leverages the quasi-orthogonality of LoRa’s Spreading Factors (SFs) and employs specially designed end devices equipped with dual LoRa transceivers, each operating on a distinct SF. For experimental analysis we choose an image transmission application and investigate various parameter combinations affecting T_tx to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces T_tx by 42.36% and 19.98% for SF combinations of seven and eight, and eight and nine, respectively. In terms of BR, we observe improvements of 73.5% and 24.97% for these same combinations. Furthermore, BER analysis confirms that the LRA algorithm delivers high-quality images at SNR levels above −5 dB in line-of-sight communication scenarios. Full article

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

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18 pages, 6764 KiB

Open AccessArticle

Towards Mass-Scale IoT with Energy-Autonomous LoRaWAN Sensor Nodes

by Roberto La Rosa, Lokman Boulebnane, Antonino Pagano, Fabrizio Giuliano and Daniele Croce

Sensors 2024, 24(13), 4279; https://doi.org/10.3390/s24134279 - 1 Jul 2024

Cited by 4 | Viewed by 1901

Abstract

By 2030, it is expected that a trillion things will be connected. In such a scenario, the power required for the trillion nodes would necessitate using trillions of batteries, resulting in maintenance challenges and significant management costs. The objective of this research is to contribute to sustainable wireless sensor nodes through the introduction of an energy-autonomous wireless sensor node (EAWSN) designed to be an energy-autonomous, self-sufficient, and maintenance-free device, to be suitable for long-term mass-scale internet of things (IoT) applications in remote and inaccessible environments. The EAWSN utilizes Low-Power Wide Area Networks (LPWANs) via LoRaWAN connectivity, and it is powered by a commercial photovoltaic cell, which can also harvest ambient light in an indoor environment. Storage components include a capacitor of 2 mF, which allows EAWSN to successfully transmit 30-byte data packets up to 560 m, thanks to opportunistic LoRaWAN data rate selection that enables a significant trade-off between energy consumption and network coverage. The reliability of the designed platform is demonstrated through validation in an urban environment, showing exceptional performance over remarkable distances. Full article

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

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28 pages, 9511 KiB

Open AccessArticle

Design and Evaluation of a Low-Power Wide-Area Network (LPWAN)-Based Emergency Response System for Individuals with Special Needs in Smart Buildings

by Habibullah Safi, Ali Imran Jehangiri, Zulfiqar Ahmad, Mohammed Alaa Ala’anzy, Omar Imhemed Alramli and Abdulmohsen Algarni

Sensors 2024, 24(11), 3433; https://doi.org/10.3390/s24113433 - 26 May 2024

Cited by 4 | Viewed by 2069

Abstract

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments. Full article

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

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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 1863

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 963

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

Cited by 4 | Viewed by 1842

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 - 1 Aug 2023

Cited by 19 | Viewed by 5127

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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