Low Power Wide Area Networks Research Articles

Evaluating the Potential and Challenges of LPWAN Technologies like LoRa and Sigfox for Long-range Communication in IoT Systems

In this paper, we consider the performance of two Low Power Wide Area Network technologies, namely LoRa and Sigfox, placing special emphasis on the possibility of efficiently coping with massive numbers of devices in IoT settings with high densities. Finally, we apply MATLAB simulation for evaluating all mentioned performance figures: power consumption, data throughput, latency, and network congestion for various network loads. LoRa has advantages in terms of high data throughput and range but has the disadvantage of high power consumption and latency in a highly populated urban environment. Sigfox is beneficial for low-power, low-data-rate applications - providing long battery life but with strict data throughput and increased latency. LoRa has a much higher latency, which is the time taken for a packet to travel from the sender to the receiver, as it is prone to congestion and interference in dense IoT setups compared to Wi-Fi. Furthermore, the performance of both technologies shows different inefficiencies as the node density increases. We discuss the pros and cons of these technologies followed by the implementation guidelines for smart cities and industrial automation. The technology choice of LPWAN will depend upon the type of applications and requirements in terms of data size, latency, and power consumption.

TinyML and edge intelligence applications in cardiovascular disease: A survey.

Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of TinyML in facilitating pervasive, low-power cardiovascular monitoring and real-time analytics for patients with cardiac anomalies, leveraging wearable devices as the primary interface. To begin with, we provide an overview of TinyML software and hardware enablers, accompanied by an examination of networking solutions such as Low-power Wide area network (LPWAN) that facilitate the seamless deployment of TinyML frameworks. Following this, we delve into the methodologies of knowledge distillation, quantization, and pruning, which represent the cornerstone strategies for optimizing machine learning models to operate efficiently within resource-constrained environments. Furthermore, our discussion extends to the role of efficient deep neural networks tailored specifically for cardiovascular monitoring on wearable devices with limited computational resources. Through a comprehensive review, we analyze the applications of prominent artificial neural network architectures including Convolutional Neural Networks (CNNs), Autoencoders, Deep Belief Networks (DBNs), and Transformers in the domain of Electrocardiogram (ECG) analytics, shedding light on their efficacy and potential in advancing healthcare technology.

Empowering IoT: leveraging data sensor communication with LoRAWAN in diverse environments

The Internet of Things (IoT) connects countless devices, such as sensors and actuators, necessitating an efficient long-range communication technology. Low Power Wide Area Network (LPWAN) solutions, such as LoRAWAN, SIGFOX, and NB-IoT, address this demand. LoRAWAN, known for its low power consumption, excels in Line of Sight (LOS) conditions, offering an effective long-range wireless communication. It’s ideal for monitoring open areas. In Non-Line of Sight (NLOS) scenarios, LoRAWAN provides wide coverage and energy efficiency, though the signal quality may slightly decline. This research tests LoRAWAN’s performance for sensor data communication both inside multi-story buildings (up to 8 storeys) and outside. The results show successful data transmission in both scenarios, including up to 2.60 km with a 35 dBi outdoor antenna.

Energy‐Aware Protocol Design and Evaluation of the PHY Layer in Satellite IoT

ABSTRACTDirect‐to‐satellite communication for the Internet of Things (IoT) has attracted significant interest from both the scientific community and major telecommunications players. The integration of satellite connectivity in smartphones and IoT devices promises a transformative impact on critical applications such as environmental monitoring, asset tracking, agriculture, and nature conservation. These applications require reliable and energy‐efficient technologies for transmitting sensor data from regions without terrestrial networks, necessitating robust design of waveforms and protocols. This work investigates the most suitable IoT protocols for direct‐to‐satellite communication, emphasizing overhead, spectral, and energy efficiency. By introducing a framework and evaluation metrics that incorporate physical layer overhead into the evaluation, a comprehensive analysis of the effective energy efficiency in satellite IoT systems is conducted. Our findings highlight substantial differences among the Low Power Wide Area Network (LPWAN) protocols. Consequently, we propose a new classification for the most energy‐efficient protocols, termed Massive Multiple Access very Low Power Wide Area Networks (MMA‐vLPWANs). This classification aims to streamline the selection process for energy‐conscious satellite IoT waveforms for deployments in remote areas. The results not only advance the understanding of protocol efficiency in satellite IoT communications but also offer a guideline for optimizing power usage in IoT devices, extending their operational life and enhancing their utility in inaccessible regions.

Performance Analysis of Cognitive Radio on licensed Low Power Wide Area Network for IoT applications

Performance Analysis of Cognitive Radio on licensed Low Power Wide Area Network for IoT applications

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.

Enhanced LoRaWAN performance through advanced spread factor allocation empowered by machine learning

Abstract In Wide Area Networks (WANs), optimal resource allocation is crucial for enhancing computational efficiency, particularly in Low Power Wide Area Networks (LPWANs). This work introduces a machine learning-based system to optimize data transfer rates while minimizing power consumption in LPWANs. The focus is on LoRa, a notable LPWAN technology for long-range communication and interference resilience. Existing LoRa networks experience performance degradation due to interference and congestion caused by the Internet of Things (IoT). To address this, advanced Spreading Factor (SF) allocation techniques are employed, using a metaheuristic optimization algorithm (Particle Swarm Optimization) and an ensemble machine learning algorithm based on gradient boosting (XGBoost), alongside Decision Tree Classifier (DTC) and Random Forest (RF). Simulation results reveal that these approaches significantly enhance Packet Delivery Ratio (PDR) and reduce transmit energy consumption across various distances, outperforming traditional SF schemes. The RF method, for instance, achieves up to 6.32% higher PDR and reduces energy consumption by up to 16.67% compared to the Lowest SF method. Additionally, these techniques improve throughput by up to 14.9% over classical methods. The study also examines the effects of gateways, network distance, and SF on PDR and energy utilization, demonstrating that the proposed methods adapt effectively to different network conditions. The findings highlight the potential of these advanced methods to enhance LoRa network performance, making them suitable for large-scale IoT deployments.

Review on Power Control in Multi-hop Networks

This paper reviews power control algorithms through interference management in a variety of network types, such as Device-to-Device (D2D) Networks, Unmanned Aerial Vehicles (UAVs), healthcare systems, and Low Power Wide Area Networks (LPWAN). The growing requirement for effective and consistent wireless communications advanced approaches to mitigate interference, ensuring optimal power usage and network performance. We explore the exceptional interference challenges and requirements of each network type. As wireless networks progress, managing interference becomes essential to maximize power usage and maintain network performance. This paper systematically reviews the latest advancements and methodologies that adjust power levels to mitigate interference. Additionally, we analyze these techniques and discuss their impact on spectral efficiency, supported by recent case studies. Our findings aim to guide researchers and practitioners in developing more effective power control and interference management solutions for next-generation wireless networks.

Potentiometric field-effect transistor pH sensing in a low-power wide-area network

Potentiometric field-effect transistor pH sensing in a low-power wide-area network

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.

Extending Coverage Through Integrating Multiple Low-Power Wide-Area Networks: A Latency Minimizing Approach

Extending Coverage Through Integrating Multiple Low-Power Wide-Area Networks: A Latency Minimizing Approach

An Intelligent Medical Monitoring System using Narrow Band –IOT

With the emergence of technology, the healthcare sector has seen tremendous growth, providing creative ways to improve patient care, increase productivity, and save expenses. Of these developments, the Internet of Things' (IoT) incorporation into the healthcare industry has emerged as a key component for developing intelligent medical systems that are able to remotely and in real-time monitor patients' health problems. Narrowband IoT (NB-IoT), a low-power, wide-area network (LPWAN) protocol created expressly to meet the expanding demands of the IoT ecosystem, particularly in healthcare, is one exciting technology facilitating this shift. Vital health indicators including heart rate, blood pressure, glucose levels, and oxygen saturation can be tracked by healthcare practitioners with the help of an NB-IoT-enabled smart healthcare monitoring system, which can monitor patients continuously and remotely. By providing more timely and individualized care, decreasing hospital stays, and enabling faster reaction times, this technology has the potential to completely transform the healthcare industry.

IoT-Enhanced Decision Support System for Real-Time Greenhouse Microclimate Monitoring and Control

Greenhouses have taken on a fundamental role in agriculture. The Internet of Things (IoT) is a key concept used in greenhouse-based precision agriculture (PA) to enhance vegetable quality and quantity while improving resource efficiency. Integrating wireless sensor networks (WSNs) into greenhouses to monitor environmental parameters represents a critical first step in developing a complete IoT solution. For further optimization of the results, including actuator nodes to control the microclimate is necessary. The greenhouse must also be remotely monitored and controlled via an internet-based platform. This paper proposes an IoT-based architecture as a decision support system for farmers. A web platform has been developed to acquire data from custom-developed wireless sensor nodes and send commands to custom-developed wireless actuator nodes in a greenhouse environment. The wireless sensor and actuator nodes (WSANs) utilize LoRaWAN, one of the most prominent Low-Power Wide-Area Network (LPWAN) technologies, known for its long data transmission range. A real-time end-to-end deployment of a remotely managed WSAN was conducted. The power consumption of the wireless sensor nodes and the recharge efficiency of installed solar panels were analyzed under worst-case scenarios with continuously active nodes and minimal intervals between data transmissions. Datasets were acquired from multiple sensor nodes over a month, demonstrating the system’s functionality and feasibility.

Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities.

Optimizing urban water distribution systems is essential for reducing economic losses, minimizing water wastage, and addressing resource access gaps, particularly in drought-prone regions impacted by climate change. We apply advanced artificial intelligence (AI) techniques and the Internet of Things (IoT) to optimize water networks in Spain using simulation. By employing EPANET for hydraulic modeling and a linear regression-based algorithm for optimization, we achieved up to 96.62% system efficiency with a mean absolute error of 0.049. Our approach demonstrates the potential to conserve up to 648,000 L of water daily at high-demand nodes, contributing to substantial resource savings across urban water networks. We propose a global architecture utilizing Low Power Wide Area Network and Low Earth Orbit solutions for widespread deployment. This study underscores the potential of AI in water network optimization and suggests future research avenues for implementing the proposed architecture in real urban water systems.

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.

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.

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.