Comparative Study and Implementation of Efficient Bandwidth Utilization Technique for LoRaWAN

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.

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

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) introduces the Adaptive Data rate (ADR) mechanism [1] aiming to maximize both battery life of the end-devices and overall network capacity. ADR performs adaptive tuning of radio configurations of the nodes by adjusting bandwidth, spreading factor, coding rate and transmission power parameters whenever the signal quality changes. The ADR algorithm was established for stable radio channel environments and is not efficient when conditions dramatically change (e.g. mobility). So, we have previously proposed an Enhanced-ADR (E-ADR) [2] that deals with mobile nodes in case of predefined mobility patterns. However, several Internet of Thing (IoT) applications, such as smart cattle ranching in smart farms [3], require sensors travelling with unknown or undefined trajectories. So, this paper extends E-ADR to unknown mobility patterns. This extension, called VHMM-based E-ADR, is based on a Variable order Hidden Markov Model (VHMM) to predict the node trajectory. It has been implemented on Waspmote SX1272 hardware platform. Experimental results show its high efficiency in terms of the packet loss rate (PLR) and the power consumption.

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.

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) 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.

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.

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

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.

To exploit the LoRaWAN (Long-Range Wide Area Network), it is essential to design suitable allocation schemes for the wireless resources. To this aim, strategies for a fair allocation of Spreading Factors (SF) among the network devices have been presented. These strategies greatly outperform the basic Adaptive Data Rate (ADR) scheme. Within these techniques, EXPLoRa-AT yields so far the best results exploiting an “ordered water-filling” approach which aims to equalize the Air-Time channel usage for each group of devices using the same SF. This paper proposes two innovative schemes based on the former one, named EXPLoRa-KM (K-means) and EXPLoRa-TS (Time Symbol). Both schemes exploit the “ordered water-filling” approach, and apply further heuristics based on network traffic knowledge. EXPLoRa-KM aims to relieve critical regions, characterized by a significant number of collisions, computing suitable adjustments on the SF allocation using K-means. Conversely, and with incremented complexity, EXPLoRa-TS performs an equalization of the traffic load (measured in symbol times) among the SF channels. The latter takes into account the fact that each device, according to its application, transmits a variable amount of data at a different sending rate. Thus, different traffic types (more or less aggressive) can be recognized. Simulation results show how both heuristics give significant performance improvements when different traffic loads are generated around a LoRaWAN Gateway. Taking into account the traffic behavior, the techniques provided in this paper contribute as promising kick-off strategies for enhancing the network performance in order to come up with the ultimate goal of scalability on a LoRaWAN network for heterogeneous IoT scenarios.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of this study is to propose a “pragmatic” artifact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of this study is that Narrow Band-Internet of Things (NB-IoT) and Long Range Wide Area Network (LoRaWAN) technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.

In modern times, the world is heading toward smart city advancement to overcome the present and future challenges of urban crowding. Millions of smart devices communicate with each other to provide more innovative services ranging from smart health to smart homes. Smart city services endeavor urban socioeconomic development with the most significant opportunities to improve the quality and ease of life. One of the promising key enablers and drivers in the smart city is the Long Range Wide Area Network (LoRaWAN). Inevitably, this imperative promotes smart cities for urban socioeconomic and the caliber of life enrichment. Security and privacy threats must be considered when the aforementioned Low-Power Wide Area Networking (LPWAN) technology is deployed in smart cities. Considering smart cities, security and privacy threats are prominent issues that may hamper the adoption and growth of smart city services. The existing literature discussed the possible threats in various smart city services and did not focus on LPWAN layer-wise vulnerabilities and threats exploration. To fill this gap, this research work narrowed down and presented the layer-wise vulnerability and threats exploration of smart homes using LoRaWAN-based use case deployment. We further elucidated the importance of federated learning, enabling reliable management of smart home services.

The rapid expansion of Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN) protocol technologies in large-scale Internet of Things (IoT) deployments highlights the need for precise and analytically grounded energy consumption (EC) estimation of battery-powered LoRa end devices (DVs). Since LoRa DV instantaneous EC strongly depends on key transmission parameters, primarily including spreading factor (SF), transmit (Tx) power, and LoRa message packet size (PS), accurate modelling of their combined influence is essential for optimizing LoRa end DV lifetime, ensuring energy-efficient network operation, and supporting transmission parameter-adaptive communication strategies. Motivated by these needs, this paper presents a comprehensive multiple linear regression modelling framework for quantifying LoRa end DV EC during one transmission and reception LoRa end DV Class A communication cycle. The study is based on extensive high-resolution electric-current measurements collected over 69 measurement sets spanning different combinations of SFs, Tx power levels, and PS values. Based on measurement results, a total of 14 multiple linear regression models are developed, each capturing the joint impact of two transmission parameters while holding the third fixed. The developed regression models are mathematically formulated using linear, interaction, and polynomial terms to accurately express nonlinear EC behavior. Detailed statistical accuracy assessments demonstrate excellent goodness of fit of the developed EC multiple linear regression models. Complementary numerical analyses of regression models EC data distribution further validate regression models’ reliability, and highlight transmission parameter-driven variability of Lora end DV EC. The results of numerical analyses for LoRa end DV EC data distribution show that specific combinations of SF, Tx power, and PS transmit parameters amplify or mitigate EC differences, demonstrating that their joint variability patterns can significantly alter instantaneous energy demand across operating conditions. These interactions underscore the importance of modelling parameters together, rather than in isolation. The developed regression models provide interpretable mathematical formulations of instantaneous LoRa end DV EC prediction for transmission at different combinations of transmission parameters, and offer practical value for energy-aware configuration, battery-lifetime planning, and optimization of LoRa network-based IoT systems.

Long-Range Wide Area Network (LoRaWAN), as Low Power Wide Area Network that connects battery-powered devices, has gained much attention lately. The LoRaWAN technology uses ALOHA as the medium access control where the End Devices (EDs) transmit data randomly and re-transmit up to eight times if collisions occur, which is not feasible in large networks. Several techniques, including synchronization and scheduling schemes, to address the challenge have been reported. However, the existing synchronization and scheduling algorithms transmit synchronization messages randomly using one super frame with fixed time slots that accommodate devices using different Spreading Factors (SFs). This phenomenon can result in collisions, idle slots, and inefficient energy use, hence limiting the LoRaWAN network scalability. To alleviate the aforementioned problems, this work proposes a dynamic Multi-Frame Multi-Spreading Factor (MFMSF) scheduling algorithm with slotted synchronization approach. In the proposed algorithm, the time slots are assigned dynamically to EDs on first to synchronize first to be assigned basis. It was revealed that the proposed dynamic MFMSF TDMA algorithm is more energy-efficient than the existing algorithm in a denser network with devices arranged in a circular disk with different radii. The packet delivery rate for the dynamic MFMSF TDMA is about 99% signifying reduced collisions during data transmission. This implies that the use of the proposed dynamic MFMSF scheduling algorithm in LoRaWAN allows large number of EDs to communicate with reduced collisions and enhanced energy efficiency of the battery-powered devices.