Low Power Wide Area Networks Research Articles

Enhancing Security in Low-power Wide-area (LPWA) IoT Environments: The Role of HSM, Tamper-proof Technology, and Quantum Cryptography

Low-power wide-area (LPWA) networks are integral to expanding Internet of Things (IoT) applications, offering extensive coverage with low power consumption. However, these networks face significant security challenges due to their widespread deployment and inherent constraints. In order to provide secure services in an LPWA IoT environment, important information stored in IoT devices (encryption keys, device unique numbers, etc.) must be safely protected from external hacking or theft by physical access, and it is necessary to develop tamper-proof technology to enhance physical security. Meanwhile, with so many ruggedized IoT devices processing and transmitting sensitive information, security systems are essential to protect the integrity and privacy of IoT data. This paper explores the critical role of hardware security modules (HSMs), tamper-proof technology, and quantum cryptography in enhancing the physical, network, and data security of LPWA IoT environments. We propose operational strategies for HSMs, tamper-proof technology in ruggedized LPWA IoT settings, and a quantum key distribution (QKD)-based IPsec solution for robust network and data security.

RTPL: A Real-Time Communication Protocol for LoRa Network

The industrial Internet of Things (IIoT) is prominently emerging in applications of large-scale and wide-area applications, such as oilfield management, smart grid management, real-time equipment monitoring, and integration of traffic management systems for smart cities. Relying on short-range wireless technologies (e.g., WirelessHART and ISA100.11a), traditional wireless solutions for industrial automation find it challenging to support the expansive scale of today’s IIoT. To address this limitation, we propose to adopt LoRaWAN, a prominent low-power wide-area network technology, for industrial automation. LoRaWAN for industrial automation poses some unique challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. LoRaWAN traditionally adopts a simple protocol based on ALOHA with no collision avoidance or Listen Before Talk with Clear Channel Assessment and Random Backoff mechanisms to minimize energy consumption, which are less suitable for real-time communication. Existing real-time protocols for short-range technologies cannot be applied to a LoRaWAN network due to its unique characteristics such as asymmetry between downlink and the uplink spectrum, predefined modes (or classes) of operation, and concurrent reception through orthogonal spreading factors. In this paper, we address these challenges and propose RTPL - a Real-Time communication Protocol for LoRaWAN networks. RTPL is a low-overhead and conflict-free communication protocol allowing autonomous real-time communication of low-energy devices and exploits LoRa’s capability of parallel communication. We implement our approach on LoRa devices and evaluate through both physical experiments and extensive simulations. All results show that RTPL achieves on average 75% improvement in real-time performance without sacrificing throughput or energy compared to traditional LoRaWAN.

Air quality monitoring system based on low power wide area network technology at public transport stops

<span>Mass migration from rural areas to urban areas has caused problems of traffic congestion, high industrial concentration and inequity in the distribution of housing in the world's capitals, generating a significant threat to sustainable development and public health due to air pollution air. In the Peruvian context, the importance of real-time monitoring of air quality is highlighted according to the standards established by the government. Several studies propose real-time environmental monitoring systems using internet of thing (IoT) technologies, electrochemical and optical sensors to measure pollutants, highlighting the need for data analysis. The objective of the paper is to show the implementation of IoT devices called sensor nodes, with long range wide area network (LoRaWAN) transmission technology for continuous monitoring of polluting gas concentrations. In addition, they are integrated into a central node called gateway to perform real-time monitoring through a web application. As an initial result, IoT devices demonstrated their effectiveness for real-time monitoring. Despite being a prototype-level result, the next stage involves its deployment at public transport stops in Lima. Overcoming the limitations of the solution, this paper establishes the foundation for future research on pollution and public health.</span>

Scalability Analysis of LoRa and Sigfox in Congested Environment and Calculation of Optimum Number of Nodes.

Low-power wide area network (LPWAN) technologies as part of IoT are gaining a lot of attention as they provide affordable communication over large areas. LoRa and Sigfox as part of LPWAN have emerged as highly effective and promising non-3GPP unlicensed band IoT technologies while challenging the supremacy of cellular technologies for machine-to-machine-(M2M)-based use cases. This paper presents the design goals of LoRa and Sigfox while throwing light on their suitability in congested environments. A practical traffic generator of both LoRa and Sigfox is introduced and further interpolated for understanding simultaneous operation of 100 to 10,000 such nodes in close vicinity while establishing deep understanding on effects of collision, re-transmissions, and link behaviour. Previous work in this field have overlooked simultaneous deployment, collision issues, effects of re-transmission, and propagation profile while arriving at a number of successful receptions. This work uses packet error rate (PER) and delivery ratio, which are correct metrics to calculate successful transmissions. The obtained results show that a maximum of 100 LoRa and 200 Sigfox nodes can be deployed in a fixed transmission use case over an area of up to 1 km. As part of the future scope, solutions have been suggested to increase the effectiveness of LoRa and Sigfox networks.

A novel effective key synchronization approach based on optimized deep neural networks for IoT-based low-power wide area networks

A novel effective key synchronization approach based on optimized deep neural networks for IoT-based low-power wide area networks

Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency.

Supporting critical downlink traffic in LoRaWAN

LoRaWAN, a low-power wide-area network (LPWAN) technology, has been successfully used in the Internet of Things (IoT) industry over the last decade. It is an easy-to-use, long-distance communication protocol combined with minimal power consumption. Supporting critical downlink traffic in LoRaWAN networks is crucial for ensuring the reliable and efficient delivery of essential data in certain actuating applications. However, challenges arise when prioritizing critical downlink traffic, including commands, alerts, and emergency notifications that demand immediate attention from actuating devices. This paper explores strategies to improve downlink traffic delivery in LoRaWAN networks, focusing on enhancing reliability, fairness, and energy efficiency through prioritization techniques and network parameter configurations in the EU868 spectrum. Theoretical as well as simulation results provide insights into the effectiveness of the available solutions for supporting critical downlink traffic in LoRaWAN networks.

Wearable Fall Detectors Based on Low Power Transmission Systems: A Systematic Review

Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living alone, the development of automatic fall alerting systems has garnered significant research attention over the past decade. A key element for deploying a fall detection system (FDS) based on wearables is the wireless transmission method employed to transmit the medical alarms. In this regard, the vast majority of prototypes in the related literature utilize short-range technologies, such as Bluetooth, which must be complemented by the existence of a gateway device (e.g., a smartphone). In other studies, standards like Wi-Fi or 3G communications are proposed, which offer greater range but come with high power consumption, which can be unsuitable for most wearables, and higher service fees. In addition, they require reliable radio coverage, which is not always guaranteed in all application scenarios. An interesting alternative to these standards is Low Power Wide Area Network (LPWAN) technologies, which minimize both energy consumption and hardware costs while maximizing transmission range. This article provides a comprehensive search and review of that works in the literature that have implemented and evaluated wearable FDSs utilizing LPWAN interfaces to transmit alarms. The review systematically examines these proposals, considering various operational aspects and identifying key areas that have not yet been adequately addressed for the viable implementation of such detectors.

A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models.

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as 7.3 dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 MHz, the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)-LoRa, etc.

Multiple Access in Large-Scale LoRaWAN: Challenges, Solutions, and Future Perspectives

Enabling energy-efficient and long-distance wireless networking is expected by various Internet-of-Things systems and consumer electronics applications. Low Power Wide Area Networks can commendably satisfy this requirement because of their combination of low-power and long-range features. Thereinto, open-standard Long Range Wide Area Network (LoRaWAN) is the representative player with hundreds of deployment cases worldwide. Unfortunately, current multiple access (MA) strategy for LoRaWAN has been proved to incur severe wireless signal interference under large-scale deployment. This is the inherent hindrance for current LoRaWAN to support large-scale wireless networking. In this context, this article aims to provide a detailed tutorial of MA issues in large-scale LoRaWAN for the first time. We start with several practical use cases of large-scale LoRaWAN. We then explain the detailed challenges of large-scale LoRaWAN networking. Representative MA solutions for better LoRaWAN networking are then described and compared. Future research directions for enhanced MA protocol design are also discussed.

Design and Implementation of Long Range Wide Area Networks for Future Industrial IoT Applications

Background: The evolution of Long Range Wide Area Networks (LoRaWAN) is a potential candidate for next generation networks for managing the massive number of devices in the Industrial Internet of Things (IIoT). LoRaWAN is more suitable for transmitting smaller intermittent data but using smaller bandwidth over a long distance. Objective: This research paper proposes to design a Low Power Wide Area Network (LPWAN) to provide long-range data transfer for Industrial IoT applications, especially for industrial sensor data. Methods: This research work deploys the experimental setup of LoRaWAN devices using LoRa gateways and Internet of Things network server to evaluate its performance for IIOT applications. Results: The deployment of this LoRaWAN has demonstrated its long range, low power, stability and low deployment cost through extensive performance evaluation carried out. LoRaWAN has been analyzed for its coverage and throughput performance. Conclusion: Through performance analysis, potential enhancements have been identified to overcome its shortcomings. This paper concludes the feasibility of deploying LoRaWAN technology for the future generation IIOT applications.

Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway.

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.

A LoRa-based safety monitoring system for hydrogen compressed natural gas (HCNG)

Abstract Hydrogen compressed with natural gas pipeline for gas mixture delivery is an effective way to realize large-scale, low-cost delivery of hydrogen. The Internet of Things (IoT) technology is one of the important supporting technologies for smart energy transformation, and the low power wide area network (LPWAN) represented by LoRa will play a key role. In this paper, a LoRa-based hydrogen compressed natural gas safety monitoring system is designed, which can collect, transmit, and analyze multiple types of data, such as ambient temperature and humidity, gas concentration, pipeline and equipment vibration, etc., and store the data and configure the alarm thresholds through the cloud server, and finally complete the real-time safety monitoring in the application layer. The system is characterized by high efficiency, high precision, low power consumption, and intelligence, which will have a positive effect on the safe transportation of hydrogen.

Software-Defined Radio Implementation of a LoRa Transceiver.

The number of applications of low-power wide-area networks (LPWANs) has been growing quite considerably in the past few years and so has the number of protocol stacks. Despite this fact, there is still no fully open LPWAN protocol stack available to the public, which limits the flexibility and ease of integration of the existing ones. The closest to being fully open is LoRa; however, only its medium access control (MAC) layer, known as LoRaWAN, is open and its physical and logical link control layers, also known as LoRa PHY, are still only partially understood. In this paper, the essential missing aspects of LoRa PHY are not only reverse engineered, but also, a new design of the transceiver and its sub-components are proposed and implemented in a modular and flexible way using GNU Radio. Finally, some examples of applications of both the transceiver and its components, which are made to be run in a simple setup by using cheap and widely available off-the-shelf hardware, are given to show how the library can be used and extended.

Robust IoT system for Smart Beaches Applications: A case study in the Valencian Region, Spain

This paper presents the design of a robust IoT (Internet of Things) system developed for Smart Beaches Applications that allows the planning and smart management of these areas. The contributions of this development were to implement low-cost and maintained-free hardware devices with wireless data transmission based on Sigfox LPWAN (Low Power Wide Area Network) technology, evaluation of the application, and overcome the aggressive environment where nodes were placed. Nodes have a low power consumption, which allows to be supplied by batteries, and these batteries are recharged by an energy harvesting module with solar panels. These nodes include different sensors for monitoring physical parameters such as: temperature, relative humidity, ultraviolet index, etc. Sensors’ data are transmitted via the network base stations to an IoT platform and from there to an application server. A database has been designed to save the data and make it available to tourists via a mobile application. In this way, the proposed IoT system achieves real-time monitoring of the existing environmental conditions at beaches and provides tourists with information on the environmental situation of the beach. The node developed presents an average current and power consumption of 1.88 mA and 6 mW, respectively. This consumption allows an autonomous operation without solar panels for around 10 days with a full charged 500 mA battery. Conducted experiments with real deployments at beaches in the Mediterranean coast of Spain have proved that, in case of operating the nodes with solar panels and protecting them adequately, they can run for several years without maintenance thanks to a ruggedized electronic.

Architecting CubeSat constellations for messaging service, Part I

In today’s modern and globalized world, connectivity is a key factor for businesses, production facilities, sensor networks, and ordinary people. However, there are still populated areas which are not covered by ground-based telecommunications infrastructure. This is where telecommunication satellite constellations come in, as they can provide coverage to remote and uninhabited regions and fill existing connectivity gaps to ensure data transfer.LoRa is one of the technologies designed for data transmissions over long distances with low power consumption. Alongside with other technologies of the Low-Power Wide Area Network family, it is widely used for Internet of Things applications. LoRa chirp spread spectrum modulation is robust against the Doppler frequency shifts encountered in low earth orbits, and it has already been used in IoT satellite communications. Due to the low transmitted signal power, the achieved data rate is not high, making it a suitable technology for telecommunications payloads on CubeSat platforms for messaging services. As compared to existing traditional communication satellite systems, CubeSat constellations are low-cost and may offer an affordable connectivity service to developing regions.This study is divided in two parts. In Part I the demand model is built based on the population distribution not covered by cell towers. The LoRa link performance is analyzed, considering the impact of LoRa channel parameters variation, such as spreading factor and channel bandwidth, while satellite orbital height, transmission antenna beamwidth, and transmitter peak power have a direct impact on the payload mass. Among thousands of possible configurations, 73 feasible payload designs have been downselected. In Part II of the study, the satellite mass and the total system cost are estimated based on the payload parameters obtained. Messages transmission simulation via a constellation is conducted in order to identify optimal constellation architectures for messaging service, as well as the main drivers of the system economic profitability.The presented analysis results provide a deeper understanding of LoRa connectivity advantages and limitations together with the performance drivers, which will support the optimization of future LoRa-based satellite communication systems and other IoT satellite constellations.

Recent Developments in AI and ML for IoT: A Systematic Literature Review on LoRaWAN Energy Efficiency and Performance Optimization.

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance.

Assessment of the dynamic parameters of a 673-meter cable-stayed bridge with wireless three-component MEMS-based sensors

Dynamic structural monitoring of bridges under ambient vibrations is a method to assess dynamic parameters of structures with low intrusiveness, e.g. with Operational Modal Analysis (OMA) data processing. Yet, several barriers hinder its adoption for real world applications, such as a lack of adequate sensors or industrial sensor networks and more generally, a lack of integrated solutions. In this paper, we present a data collection campaign using a novel wireless sensing system for dynamic structural monitoring of civil engineering structures. The core enabler of this SHM product named S-lynks is the use of a novel ultra-low noise seismic MEMS accelerometer, QuietSeisTM, which makes possible to envision a true industrial wireless sensor network for OMA in civil engineering structures. We developed a bespoke wireless sensing system composed of Wireless Sensor Nodes (WSN) and Gateways (WSG) and combining Low Power Wide Area Network (LPWAN) and Wireless Local Area Network (WLAN) technologies to manage the WSN wake up, synchronization and data transmission to WSG. This product has been used on a 675-meter cable-stayed bridge to assess the dynamic parameters from ambient vibrations measurements. We successfully triggered 120 measure points on the deck and inside a tower in less than 10 hours. More than 20 modes are identified between 0 and 5 Hz, with well-defined mode shapes. Extra measurements on a few cables to identify the tension allow explaining the asymmetry observed in some mode shapes.

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

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.

A Study for Comparative Analysis of Dueling DQN and Centralized Critic Approaches in Multi-Agent Reinforcement Learning

In this study, we introduce a deep Q-network agent utilizing a dueling architecture to refine the valuation of actions through separate estimations of the state-value and action-value functions, adapted to facilitate concurrent multi-agent operations within a shared environment. Inspired by the self-organized, decentralized cooperation observed in natural swarms, this study uniquely integrates a centralized mechanism, or a centralized critic. This enhances performance and coherence in decision-making within the multi-agent system. This hybrid approach enables agents to execute informed and optimized decisions by considering the actions of their counterparts while maintaining an element of collective and flexible task-information sharing, thereby presenting a groundbreaking framework for cooperation and information sharing in swarm robot systems. To augment the communication capabilities, we employ low-power wide-area networks, or Long Range (LoRa), which are characterized by their low power consumption and long-range communication abilities, facilitating the sharing of task information and reducing the load on individual robots. The aim is to leverage LoRa as a communication platform to construct a cooperative algorithm that enables efficient task-information sharing among groups. This can provide innovative solutions and promote effective cooperation and communication within multi-agent systems, with significant implications for industrial and exploratory robots. In conclusion, by integrating a centralized system into the proposed model, this approach successfully enhances the performance of multi-agent systems in real-world applications, offering a balanced synergy between decentralized flexibility and centralized control.