Low-power LoRaWAN Extender Using Multiple Relays: Design and Evaluation

The advent of Long Range Wide Area Network (LoRaWAN) has significantly enhanced Internet of Things (IoT) applications, particularly in Smart Cities, with its broad coverage and energy efficiency. However, in areas with blind spots and obstacles, the conventional star network has limitations in coverage. To overcome this, researchers conducted a study to explore the potential of LoRaWAN extenders with multiple relays, focusing on coverage, performance, and power consumption. The study involved designing and deploying a relay system aligned with protocol of LoRaWAN to extend the reach of a standard LoRaWAN gateway. Strategically placed relays addressed areas beyond the gateway's coverage. Performance was measured by the longest communication distance achieved by each relay, packet loss ratio, and latency. Power consumption was also monitored. The results demonstrate the practicality and effectiveness of the proposed multiple relay design, expanding the capabilities of LoRaWAN network.

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.

- LoRaWAN (Long Range Wide Area Network) is one of the most widely adopted Low-Power Wide Area Network (LPWAN) technologies, enabling long-range, energy-efficient communication for Internet of Things (IoT) applications. A key mechanism in LoRaWAN is Adaptive Data Rate (ADR), which dynamically configures each end device’s Spreading Factor (SF) and transmission power (Tx Power) to balance energy consumption, coverage, and reliability. The efficient allocation of SFs is particularly critical, as it directly influences bandwidth utilization, throughput, latency, and packet collision rates. In this paper, we present a comparative study and implementation of three approaches for ADR-based bandwidth optimization schemes: Static ADR, Centralized ADR, and a proposed Decentralized ADR scheme. Python-based simulations are conducted under varying traffic conditions, incorporating realistic wireless channel models such as Free Space, Okumura-Hata, and Nakagami-m fading.Performance metrics including throughput, packet success rate, collision rate, and SF distribution are evaluated to provide a clear and comprehensive comparison of the different ADR approaches. The results demonstrate that the proposed Decentralized ADR significantly improves overall performance, achieving up to 18.6% higher throughput compared to the conventional Centralized ADR scheme. These findings highlight the importance of distributed decision-making in LoRaWAN and provide useful insights into scalable, adaptive, and efficient ADR designs for next-generation large-scale IoT deployments. Key Words: LoRaWAN, IoT, Adaptive Data Rate, Bandwidth Optimization, Decentralized Networks

Long Range Wide Area Network (LoRaWAN) in the Internet of Things (IoT) domain has been the subject of interest for researchers. There is an increasing demand to localize these IoT devices using LoRaWAN due to the quickly growing number of IoT devices. LoRaWAN is well suited to support localization applications in IoTs due to its low power consumption and long range. Multiple approaches have been proposed to solve the localization problem using LoRaWAN. The Expected Signal Power (ESP) based trilateration algorithm has the significant potential for localization because ESP can identify the signal’s energy below the noise floor with no additional hardware requirements and ease of implementation. This research article offers the technical evaluation of the trilateration technique, its efficiency, and its limitations for the localization using LoRa ESP in a large outdoor populated campus environment. Additionally, experimental evaluations are conducted to determine the effects of frequency hopping, outlier removal, and increasing the number of gateways on localization accuracy. Results obtained from the experiment show the importance of calculating the path loss exponent for every frequency to circumvent the high localization error because of the frequency hopping, thus improving the localization performance without the need of using only a single frequency.

Continuous and reliable monitoring of soldiers’ health in remote and resource-constrained environments is essential for operational readiness and timely emergency interventions. Long Range Wide Area Network (LoRaWAN) provides an energy-efficient, long-range communication solution for wearable Internet of Medical Things (IoMT) devices, but dynamic factors such as packet loss, latency, and bandwidth limitations can compromise real-time health inference. This paper presents HEAL, Health-Enhanced Adaptive LoRaWAN, an AI-enabled IoMT framework designed to predict soldiers’ health under dynamic physiological and communication conditions. “Adaptive” denotes AI-level robustness, where models sustain stable and accurate health predictions under varying communication constraints and transmission parameters. HEAL employs an end-to-end workflow in which a publicly available multivariate physiological time-series dataset is preprocessed, structured, and automatically labeled using transformer-based large language models. Multiple Deep Learning (DL) architectures are trained and compared to identify models suitable for real-time health status classification, with the best-performing model fine-tuned and deployed in a LoRaWAN simulation to evaluate AI inference under realistic communication constraints. Experimental results show that a BiLSTM model achieves up to 94% accuracy and 95% Macro F1-score on structured physiological data. Within the LoRaWAN simulation, the fine-tuned model maintains approximately 93% accuracy, while the network achieves a packet delivery ratio of up to 91% under favorable conditions (SF7–SF8, 200–400 m). HEAL provides a scalable and energy-efficient framework for systematically evaluating AI models alongside communication reliability metrics, demonstrating that accurate AI-driven health monitoring can be sustained despite dynamic LoRaWAN effects and bridging the gap between model development and realistic operational deployment.

The Long Range Wide Area Network (LoRaWAN) is one of the fastest growing Internet of Things (IoT) access protocols. It operates in the license free 868 MHz band and gives everyone the possibility to create their own small sensor networks. The drawback of this technology is often unscheduled or random channel access, which leads to message collisions and potential data loss. For that reason, recent literature studies alternative approaches for LoRaWAN channel access. In this work, state-of-the-art random channel access is compared with alternative approaches from the literature by means of collision probability. Furthermore, a time scheduled channel access methodology is presented to completely avoid collisions in LoRaWAN. For this approach, an exhaustive simulation study was conducted and the performance was evaluated with random access cross-traffic. In a general theoretical analysis the limits of the time scheduled approach are discussed to comply with duty cycle regulations in LoRaWAN.

Long Range Wide Area Network (LoRaWAN) is an emerging wireless technology that is expected to be widely deployed and implemented in several applications, especially with the promising widespread use of the Internet of Things (IoT) and its potential applications within the Fifth Generation (5G) communication technology. LoRaWAN consists of a number of nodes that monitors and senses the environment to collect specific data, and then sends the collected data to a remote monitoring device for further processing and decision-making. Energy consumption and security assurance are two vital factors needed to be optimized to ensure an efficient and reliable network operation and performance. To achieve that, each of LoRaWAN nodes can be configured by five transmission parameters, which are the spreading factor, carrier frequency, bandwidth, coding rate and transmission power. Choosing the best values of these parameters leads to enhancing the network deployment. In this paper, we shed the light to the security aspect in LoRaWAN network. Then, we introduced an algorithm that depends on the reinforcement learning approach to enable each node in the network to choose the best values of spreading factor and transmission power such that it leads to a lower energy consumption and higher packets’ delivery rate. The results of the simulation experiments of our proposed technique showed a valuable increase in the packet reception rate at the gateway and a significant decrease in the total consumed energy at the end nodes compared with the most related work in literature

Low-power wide-area networks (LPWAN) are gaining a lot of interest to support Internet of things (IoT) applications. LPWAN can provide massive connectivity and services for thousands of nodes with low power consumption, which is crucial for battery-based IoT devices. Different technologies have been proposed for (LPWAN), however, long range wide-area network (LoRaWAN) is one of the most promising and widely used types that provide large-scale deployment, high energy efficiency, and low-cost. Resources allocation in LoRaWAN has vital role to enhance the system performance. In this work, we propose a resource allocation technique that aims to minimize the total system transmit power. The proposed technique exploits the Hungarian matching algorithm to assign both the LoRaWAN spreading factors (SFs) and the sub-carriers (SCs) for different end nodes. The results show that the performance of the proposed Hungarian-based resources allocation algorithm reduces the power consumption compared with other conventional algorithms. Additionally, it provides a better energy efficiency.

Long Range Wide Area Network (LoRaWAN) provides a promising solution for long-range, low-power wireless communication in Internet of Things (IoT) applications. However, energy consumption remains a critical challenge that significantly impacts network lifetime and scalability. Traditional LoRaWAN implementations rely on automatic and continuous transmission parameter allocation schemes managed by centralized gateways, which fail to adapt to dynamic network conditions such as varying link quality, node mobility, and fluctuating energy levels, leading to excessive energy consumption and reduced network performance. In this work, we propose Adaptive Energy Efficient Routing Protocol (AEERP), a reinforcement learning-based framework that dynamically optimizes transmission parameters in LoRaWAN network. Our approach leverages Expected State-Action-Reward-State-Action (SARSA) to enable intelligent adaptation of spreading factor, transmission power, and gateway selection based on real-time conditions. Key innovations include: (1) a hybrid Q-value initialization strategy combining domain knowledge with exploration, (2) adaptive discount factor and learning rate mechanisms, and (3) joint optimization of transmission parameters based on residual energy, distance, and Packet Delivery Ratio (PDR). Extensive simulations demonstrate that AEERP significantly outperforms state-of-the-art methods, achieving substantial energy conservation, superior PDR, enhanced signal-to-noise ratios, and reduced bit error rates. Results show that AEERP effectively balances energy efficiency, and communication reliability for sustainable LoRaWAN deployments.

Adaptive data rate control in low power wide area networks for long range IoT services

HighlightsWhat are the main findings?Ensuring energy efficiency through the use of the P&O MPPT algorithmThe integration of a PV-battery hybrid system for a smart street lighting systemThe use of internet-free LoRaWAN communication for a remote monitoring and control systemWhat is the implication of the main finding?The primary objective of this study is to present a design for a street lighting system based on LEDs, which is hybrid-powered by solar energy and batteries, thereby making it independent of the grid.It focuses on reducing energy consumption during times of low demand, managing energy according to the potential of energy sources, and enhancing system reliability by enabling monitoring and control of the entire system through the LoRaWAN communication method.The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid smart street lighting system that combines solar photovoltaic generation with battery storage and Internet of Things (IoT)-based control to ensure continuous and efficient operation. The system integrates Long Range Wide Area Network (LoRaWAN) communication technology for remote monitoring and control without internet connectivity and employs the Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm to maximize energy extraction from solar sources. Data transmission from the LoRaWAN gateway to the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, enabling real-time access and management via a graphical user interface. Experimental results demonstrate that the proposed system achieves a maximum MPPT efficiency of 97.96%, supports reliable communication over distances of up to 10 km, and successfully operates four LED streetlights, each spaced 400 m apart, across an open area of approximately 1.2 km—delivering a practical, energy-efficient, and internet-independent solution for smart urban infrastructure.

The Internet of Things (IoT) has introduced transformative capabilities for ecological monitoring through the acquisition of real-time sensor-based data. In this context, the Internet of Trees (IoTr) has emerged as a promising domain for monitoring trees in forest ecosystems. However, the existing literature lacks species-specific, energy-efficient, and scalable frameworks capable of supporting proactive tree health surveillance and disease detection. Current research focuses on using IoTr for tree monitoring, but primarily covers basic soil moisture, with limited integration of disease-relevant parameters and species classification. This research addresses the existing research gap by proposing IoT-Based Tree Routing for Energy-Efficient Systems (I-TREES), a novel context-aware framework for real-time monitoring of tree health, with a specific focus on ash dieback and oak decline in the United Kingdom (UK). This research first evaluates three long-range communication protocols: Narrowband IoT (NB-IoT), Long Range Wide Area Network (LoRaWAN), and Sigfox, using MATLAB simulations to compare energy efficiency, Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), delay, latency, and throughput across two node densities (100 and 300 nodes). Based on the comparative evaluation, NB-IoT was selected as the optimal protocol, and the I-TREES framework is designed and simulated in Network Simulator 3 (NS-3) using a large-scale network of 700 IoT nodes to assess its performance in forest health monitoring. The comparative benchmark against six established routing protocols Low-Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information Systems (PEGASIS), Hierarchical Clustering Algorithm (HCA), Heuristic Clustering and Scheduling Algorithm (H-CSA), Event-Driven Spatio-Temporal Optimisation (ESTO), and K-Means. The results showed that I-TREES consistently outperformed in terms of scalability, throughput, latency, and energy efficiency. In the final phase, a Proof of-Concept (PoC) approach is adopted to assess the feasibility of disease classification under limited real-world data conditions. A multimodal PoC dataset was constructed by integrating IoT sensor readings with custom labelled image data of ash and oak trees. Machine Learning (ML) models, including Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid (CNN-RF, CNN-LSTM) architectures, are trained for disease detection. The RF-based multimodal model, in particular, offered strong interpretability and demonstrated consistently high performance in accuracy, precision, recall, and F1 score, supporting its suitability for real world deployment. The findings verified the effectiveness of the framework as an energy efficient and scalable approach for the detection of tree diseases. The proposed I-TREES framework presents a reliable and intelligent forest health surveillance framework for tree disease detection, suitable for real-world forest applications to manage diseases sustainably and support policy-making decisions for ecological resilience in the UK and similar environments.

LoRaWAN is well suited for the transmission of small amounts of numeric data, while its technical features are not conceived for the continuous real-time monitoring of time-evolving signals. The aim of this paper is to discuss the usability of the Long Range Wide Area Network (LoRaWAN) technology for the continuous transmission of signals characterized by different scales of variability. At the purpose the so-called burst-mode acquisition is considered and analyzed for a LoraWAN infrastructure. Furthermore, a very simple messaging protocol is introduced to increase data throughput, by allowing application-level messages to be sent using the same data message, avoiding extra overload. To demonstrate this approach, the paper focuses on possible techniques to perform a long-term monitoring of the electrocardiographic (ECG) signal over a LoRaWAN communication infrastructure where duty cycle induced constraints are carefully considered. The analysis of real signals proves the feasibility of the proposed system, which can be easily generalized for other kind of signals.

The Internet of Things (IoT) has expanded rapidly, allowing for a network of sensors and gadgets to collect and share information to make people's lives easier and more convenient. As the Internet of Things (IoT) grows, however, energy efficiency becomes a major issue, especially for portable and wireless gadgets. Low-power, long-range communication capabilities are needed, and Long-Range Wide Area Network (LoRaWAN) has emerged as a viable solution to meet this need. This study provides an in-depth analysis of the LoRaWAN-based, low-power Internet of Things. The suggested network architecture is optimized for low power consumption and high connectivity for numerous Internet of Things (IoT) use cases. This low-power Internet of Things network relies on LoRaWAN gateways, end devices, and a server to function. LoRaWAN is a technology that enables the low-power, long-range transmission of data packets. The results show that the optimized case and non-optimized case have a delivery ratio of 0.85 to 0.73 from node 100 to 500. LoRaWAN significantly reduces energy usage compared to conventional IoT connectivity alternatives, making it a fantastic option for battery-powered devices in far-flung or limited-resource locations. Finally, the adoption of LoRaWAN provides a viable solution to address the energy efficiency concerns in IoT networks, hence allowing for sustainable, long-lasting IoT installations and enabling a wide variety of new applications within the IoT ecosystem. Furthermore, addresses the potential applications of this technology in the future, including upgrades and integration with other technologies like edge computing and 5G networks.

In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early fire warning methods, such as smoke or gas sensors, are ineffectual. Many fires have caused deaths and property damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, and everyday things with computing and sensing capabilities. These objects can be managed and monitored remotely as they are connected to the Internet. In the Internet of Things concept, low-power devices like sensors and controllers are linked together using the concept of Low Power Wide Area Network (LPWAN). Long Range Wide Area Network (LoRaWAN) is an LPWAN product used on the Internet of Things (IoT). It is well suited for networks of things connected to the Internet, where terminals send a minute amount of sensor data over large distances, providing the end terminals with battery lifetimes of years. In this article, we design and implement a LoRaWAN-based system for smart building fire detection and prevention, not reliant upon Wireless Fidelity (Wi-Fi) connection. A LoRa node with a combination of sensors can detect smoke, gas, Liquefied Petroleum Gas (LPG), propane, methane, hydrogen, alcohol, temperature, and humidity. We developed the system in a real-world environment utilizing Wi-Fi Lora 32 boards. The performance is evaluated considering the response time and overall network delay. The tests are carried out in different lengths (0–600 m) and heights above the ground (0–2 m) in an open environment and indoor (1st Floor–3rd floor) environment. We observed that the proposed system outperformed in sensing and data transfer from sensing nodes to the controller boards.