Modeling and Optimization of LoRa Networks under Multiple Constraints

- 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

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.

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.

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

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.

Future Internet of Things deployments generate a multitude of new challenges and opportunities. While 5G networks promise massive throughput rates with ultra low delays, many verticals in the areas of Industry 4.0, Smart City, or Smart Agriculture only transmit tiny amounts of data and instead demand high energy efficiency. This is addressed by access technologies like Low Power Wide Area Networks being complementary to high performance 5G networks. Especially Long Range Wide Area Network (LoRaWAN) promises transmissions across long distances with low energy requirements with the drawback of unreliable transmission due to potential message collisions through random channel access. For that reason, a broad parameter study simulation for LoRaWAN channel access with slotted Aloha is presented for real world scenarios and influences like clock drifts or cross traffic. The key impact factors of this channel access approach are studied with focus on the collision probability in various scenarios and simulation results are compared to the current state of the art. The contribution of this work are guidelines for parameter settings in a LoRaWAN with slotted Aloha channel access and performance comparisons for different settings. This information is crucial to scale and operate future LoRaWANs.

Long Range Wide Area Network (LoRaWAN) is the one of the promising low power wide area network (LPWAN) technologies at present and is expected to grow in the foreseeable future as a tool to provide connectivity among small things. In this paper, we present a simple analytical model to compute the throughput and packet loss probability of Medium Access Control (MAC) for Class-A of LoRaWAN. This analysis results can be used as a reference for deploying the appropriate number of end-devices (EDs) that can be accepted in a gateway (GW) while maximizing network throughput or guaranteeing the packet loss rate of EDs.

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.

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

<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>

Long Range Wide Area Network (LoRaWAN) has been largely developed and applied for many Internet of Things (IoT) applications with wide coverage and low-power consumption. However, for environment with many obstacles and blind spots, the classical star network has limitation in area coverage. The present study was undertaken to evaluate the LoRaWAN extender using multiple relays in terms of coverage ability, performance, and power consumption. In this research, the design and implementation of a multiple relay system compatible with the LoRaWAN protocol is studied to extend the coverage of a classical LoRaWAN gateway. The relay can have multiple years of operation with a compact battery. These relays were deployed along a deep river will complex terrain that the gateway can not fully cover. The longest distance of each relay was recorded so that the packets sent by the previous relay were still received by the next relay, the packet loss ratio and latency were calculated to evaluate the performance, and power consumption was measured by using a power analyzer. The results show the effectiveness and feasibility of the proposed design for multiple relays to extend the LoRaWAN network.

The use of Low Power Wide Area Networks (LPWANs) is growing due to their advantages in terms of low cost, energy efficiency and range. Although LPWANs attract the interest of industry and network operators, it faces certain constraints related to energy consumption, network coverage and quality of service. In this paper we demonstrate the possibility to optimize the performance of the LoRaWAN (Long Range Wide Area Network) technology, one of the most widely used LPWAN technology. We suggest that nodes use light-weight learning methods, namely, multi-armed bandit algorithms, to select the communication parameters (spreading factor and emission power). Extensive simulations show that such learning methods allow to manage the trade-off between energy consumption and packet loss much better than an Adaptive Data Rate (ADR) algorithm adapting spreading factors and transmission powers on the basis of Signal to Interference and Noise Ratio (SINR) values.

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.

Nowadays, many people around the world depend mostly on agriculture for their livelihood. In the majority of countries around the world, it is the most significant occupation for many families. Unfortunately, farmers, particularly in oil palm plantations, continue to rely on age-old practices. One of the key elements in achieving high and long-term oil palm production on peat is the adoption of efficient precision water management. In essence, this means maintaining the water table at the necessary depth. Because of the peat's persistently low water table, oil palm productivity has sharply decreased. In this work, an Internet of Things (IoT) for precision agriculture monitoring is developed using a long-range wide area network (LoRaWAN) algorithm. Based on an approach point of view, a LoRaWAN is a long-range, low-power, low-bitrate wireless telecommunications system meant to be used as part of the Internet of Things architecture. The end devices link to gateways through a single wireless hop using LoRaWAN. These gateways function as transparent bridges, relaying messages from the end devices to a central network server. The ultimate result is the creation of a precision water management assistance algorithm employing LoRaWAN and IoT that is both affordable and effective.

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.