Long Range Wide Area Networks Research Articles

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.

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.

Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks

LoRaWAN is a low-power wide-area network (LPWAN) technology that is well suited for industrial IoT (IIoT) applications. One of the challenges of using LoRaWAN for IIoT is the need to collect data from a large number of devices. Polling is a common way to collect data from devices, but it can be inefficient for LoRaWANs, which are designed for low data rates and long battery life. LoRaWAN devices operating in two specific modes can receive messages from a gateway even when they are not sending data themselves. This allows the gateway to send commands to devices at any time, without having to wait for them to check for messages. This paper proposes various polling mechanisms for industrial IoT applications in LoRaWANs and presents specific considerations for designing efficient polling mechanisms in the context of industrial IoT applications leveraging LoRaWAN technology.

Review of LoRaWAN: Performance, Key Issues and Future Perspectives

In the last few years, the Low-powered wide area networks (LPWAN) have gained popularity and massively deployed, especially in smart cities and agriculture, due to their advantages, such as energy efficiency, extensive coverage, and low cost. The long-range wide area networks (LoRaWAN) protocol is a new technology. It attracts the attention of many research centers worldwide as it allows data transmission at a low cost across long distances. This article reviews the performance of LoRa and LoRaWAN for both investigations in indoor and outdoor environments. Moreover, a performance analysis of this technology is made by focusing on five main indicators, which are coverage, time on air (TOA), packet error rate (PER), received signal strength indication (RSSI), and signal-to-noise ratio (SNR) while considering technical characteristics is included. The investigation settings were divided into two categories: simulation and testbed in a real-world application. Consequently, a table of summary of indicators used by the researcher was made to make it easier for other researchers to find references for their topics relevant to this review. Next, the identified key issues and solutions are discussed in this review. The issues discussed significantly affect the performance of various monitoring technologies regarding energy management, quality of service, coverage, signal interference, and data handling. Finally, this review will discuss the future perspective, provide valuable suggestions for future research works, and lead toward improving current LoRaWAN technology.

LoRaSync: Energy efficient synchronization for scalable LoRaWAN

AbstractLow‐power wide‐area networks connect a large number of battery‐powered wireless devices over long distances. Among them, long range wide area networks (LoRaWAN) implement a pure ALOHA medium access scheme to save device energy by minimizing the radio usage. However, frame collisions restrain the network scalability when the traffic load increases. In this context, synchronization can be used to exploit the available bandwidth more efficiently by controlling the timing of frame transmissions and reducing the collision probability. Such strategy allows to increase the network throughput at the cost of an extra energy demand due to the inherent overhead. In that, the entailed network scenario is still supposed to address low power applications. Therefore this article timely presents LoRaSync, an energy‐efficient synchronization scheme designed for LoRa networks of any size. An accurate clock drift model was established based on measurements made on real cheap devices, and leveraged to support the design of LoRaSync. Our mechanism has been used to evaluate the same ALOHA‐based random access but on a time‐slotted basis, thus increasing the maximum achievable throughput compared to the legacy access. Throughput and energy efficiency models are established to evaluate the performances of a LoRaSync‐operated network. These models are validated with a simulation environment mimicking large‐scale deployments, and then used to determine the most energy efficient slot size for any traffic load. As a final proof of concept, LoRaSync has been implemented and tested on a LoRa testbed to demonstrate the feasibility of our solution on real hardware.

Multi-Objective Optimization of Gateway Location Selection in Long-Range Wide Area Networks: A Tradeoff Analysis between System Costs and Bitrate Maximization

LoRaWANs play a critical role in various applications such as smart farming, industrial IoT, and smart cities. The strategic placement of gateways significantly influences network performance optimization. This study presents a comprehensive analysis of the tradeoffs between system costs and bitrate maximization for selecting optimal gateway locations in LoRaWANs. To address this challenge, a rigorous mathematical model is formulated to incorporate essential factors and constraints related to gateway selection. Furthermore, we propose an innovative metaheuristic algorithm known as the M-VaNSAS algorithm, which effectively explores the solution space and identifies favorable gateway locations. The Pareto front and TOPSIS methods are employed to evaluate and rank the generated solutions, providing a robust assessment framework. Our research findings highlight the suitability of a network model comprising 144 gateways tailored for the Ubon Ratchathani province. Among the evaluated algorithms, the M-VaNSAS method demonstrates exceptional efficiency in gateway location selection, outperforming the PSO, DE, and GA methods.

AI-ERA: Artificial Intelligence-Empowered Resource Allocation for LoRa-Enabled IoT Applications

Adaptive data rate (ADR) is a widely adopted resource assignment approach in long-range wide-area networks (LoRaWANs) for static Internet of Things (IoT) applications such as smart grids and metering. Blind ADR (BADR) has been recommended for mobile IoT applications such as pet and industrial asset tracking. However, ADR and BADR cannot provide appropriate measures to alleviate the massive packet loss problem caused by the unsuitable spreading factors (SFs) assigned to end devices when they are mobile. This paper proposes a novel proactive approach—“artificial intelligence-empowered resource allocation” (AI-ERA)—to address the resource assignment issue in static and mobile IoT applications. The AI-ERA approach consists of two modes, namely offline and online modes. First, a deep neural network (DNN) model is trained with a dataset generated at ns-3 in the offline mode. Second, the proposed AI-ERA approach utilizes the pre-trained DNN model in the online mode to proactively assign an efficient SF for the end device before each uplink packet transmission. The proactive behavior of the AI-ERA improved the packet success ratio by an average of 32% and 28% in static and mobility scenarios compared with the typical LoRaWAN ADR, respectively.

Multi-objective Stochastic Gradient Based ADR Mechanism for Throughput and Latency Optimization in LoRaWAN

Background: In Long Range Wide Area Networks (LoRaWAN), the goal of Adaptive Data Rate (ADR) is to allocate resources to End Devices (ED) like Transmission Power (TP) and Spreading Factor (SF). The EDs are designed in a way that they can choose optimal configuration resource parameters from a set of LoRa physical layer parameters. The SF parameter has to be chosen correctly, as an incorrect one may cause collisions and interference if multiple nodes have the same SF. This paper focuses on throughput and latency optimization using an effective ADR mechanism for LoRaWAN-based IoT networks. Objective: The objective of this study is to maximize the total throughput. SF should be used by multiple nodes as it will have less Time on Air (ToA), but it may cause collision, contention, and co-spreading factor interference problems. The idea is to find an optimal SF allocation to end devices and the optimal number of total devices using the same SF to avoid collision and interference. Methods: This paper proposes a multi-objective stochastic gradient descent method to solve the constrained optimization problem for optimizing throughput and latency. Results: This work compares throughput and latency results for the static, quasi-static, and dynamic environments. Trade-offs between latency and throughput for the simulated scenarios are also presented. Conclusion: The simulation results show that the throughput obtained using this technique is higher than the naive ADR approach and the existing gradient descent methods.

Design of a Multi-Standard UWB-LoRa Antenna Structure and Transceiver Board for High-Accuracy and Long-Range Localization Applications

Long-Range Wide-Area Networks (LoRaWAN) allow the transmission of data via radio link from sensors, which are potentially isolated or difficult to access, to gateways and servers that are connected to cellular networks for data processing, exchange, or relay, with low transmission power. This concept employs Long-Range (LoRa) modulation and has led to the emergence of many applications for the monitoring and tracking of objects. However, due to its characteristic of a low data rate for low-power communication, the transmission of information with LoRa technology is not suitable for the fast real-time monitoring of data. Additionally, due to its narrow bandwidth, an attempt to perform localization through the LoRa modulation technique will result in very limited accuracy because of its inability to resolve multipath problems. Thus, in this paper, we propose a multi-standard Ultra-Wide Bandwidth (UWB) and LoRa end-device that is capable of measuring location with high accuracy using UWB technology and then transmitting the location information through LoRa method to gateways and the Internet of Things Network. The results of measurements in indoor and outdoor scenarios show a UWB localization accuracy that is of sub-meter level, being between 10 and 33 cm, and a UWB range of 124 m in Line-of-Sight (LOS) and 55 m in Non-Line-of-Sight (NLOS) applications, respectively.

Efficient graph-based gateway placement for large-scale LoRaWAN deployments

Low Power Wide Area Networks with Long Range Wide Area Networks (LoRaWANs) as one of their most prominent representatives are promising solutions for future Internet of Things deployments. The technology is characterized by low energy requirements leading to long battery lifetimes. However, the drawbacks are limited throughput rates and the unreliable nature of the LoRa technology. Specifically its random channel access approach leads to significant message collisions and thus, data loss in larger deployments. From a network planning point of view, the reduction of potentially colliding messages at the frequency bands in combination with limiting the transmission duration of messages is a promising approach. For that reason, we present a novel graph-based gateway placement approach. We show that our approach performs similar to state-of-the-art related work in the worst case and reduces the required number of gateways by up to 40% while reducing the collision probability by up to 70%. Furthermore, we overcome the challenge of scalability of our approach when placing gateways in very large networks. By splitting the problem into smaller problem instances by pre-clustering, we can solve arbitrarily large instances efficiently without a significant increase in number of required gateways in real networks.

LoRaWAN Gateway Placement in Smart Agriculture: An Analysis of Clustering Algorithms and Performance Metrics

The use of Wireless Sensor Networks (WSN) in smart agriculture has emerged in recent years. LoRaWAN (Long Range Wide Area Networks) is widely recognized as one of the most suitable technologies for this application, due to its capacity to transmit data over long distances while consuming little energy. Determining the number and location of gateways (GWs) in a production setting is one of the most challenging tasks of planning and building this type of network. Various solutions to the LoRaWAN gateway placement problem have been proposed in the literature, utilizing clustering algorithms; however, few works have compared the performance of various strategies. Considering all these facts, this paper proposes a strategy for planning the number and localization of LoRaWAN GWs, to cover a vast agricultural region. Four clustering algorithms were used to deploy the network GWs: K-Means and its three versions: Minibatch K-Means; Bisecting K-Means; and Fuzzy c-Means (FCM). As performance metrics, uplink delivery rate (ULDR) and energy consumption were used, to provide subsidies for the network designer and the client, with which to choose the best setup. A stochastic energy model was used to evaluate power consumption. Simulations were performed, considering two scenarios: Scenario 1 with lower-medium concurrence, and Scenario 2 with higher-medium concurrence. The simulations showed that the use of more than two GWs in Scenario 1 did not lead to significant improvements in ULDR and energy consumption, whereas, in Scenario 2, the suggested number of GWs was between 11 and 15. The results showed that for Scenario 1, the FCM algorithm was superior to all alternatives, regarding the ULDR and mean energy consumption, while the K-Means algorithm was superior with respect to maximum energy consumption. In relation to Scenario 2, K-Means caused the best ULDR and mean consumption, while FCM produced the lowest maximum consumption.

Optimizing Resources and Increasing the Coverage of Internet-of-Things (IoT) Networks: An Approach Based on LoRaWAN.

A resource optimization methodology is proposed for application in long range wide area networks (LoRaWANs). Using variable neighborhood search (VNS) and a minimum-cost spanning tree algorithm, it reduces the implementation and the maintenance costs of such low power networks. Performance evaluations were conducted in LoRaWANs with LoRa repeaters to increase coverage, in scenario where the number and the location of the repeaters are determined by the VNS metaheuristic. Parameters such as spread factor (SF), bandwidth and transmission power were adjusted to minimize the network's total energy per useful bit (Ebit) and the total data collection time. The importance of the SF in the trade-off between (Ebit) and time on-air is evaluated, considering a device scaling factor. Simulation results, obtained after model adjustments with experimental data, show that, in networks with few associated devices, there is a preference for small values of SF aiming at reduction of Ebit. The usage of large SF's becomes relevant when reach extensions are required. The results also demonstrate that, for networks with high number of nodes, the scaling of devices over time become relevant in the fitness function, forcing an equal distribution of time slots per SF to avoid discrepancies in the time data collection.

Improving the Sustainability of Confirmed Traffic in LoRaWANs Through an Adaptive Congestion Scheme

The scalability of long-range wide area networks (LoRaWANs) is known to be very sensitive to the presence of traffic from gateways (GWs) to end devices (downlink (DL) traffic). The protocol supports confirmed traffic, for which DL traffic is generated in the form of acknowledgments (ACKs). Research has shown that even a limited number of ACKs quickly cause network congestion, negatively impacting scalability. This article introduces a mechanism, the adaptive congestion scheme (ACS), which aims to monitor the congestion caused by DL traffic and take steps to reduce it. Currently, the ACS supports one counteraction in the form of a newly developed algorithm called groupedPackets (also introduced in this article). This algorithm reduces the number of sent confirmed packets by requesting that confirmed nodes (nodes that only send confirmed traffic) aggregate their application packets. Simulations showed that this algorithm improved the successful delivery of both unconfirmed traffic and confirmed traffic. When traffic volumes are low, the algorithm has a minimal impact, but at high network packet arrival rates (higher than 0.1 pkt/s), the successful delivery of especially confirmed traffic increased significantly.

Development of a smart sensing unit for LoRaWAN-based IoT flood monitoring and warning system in catchment areas

This study introduces a novel flood monitoring and warning system (FMWS) that leverages the capabilities of long-range wide area networks (LoRaWAN) to maintain extensive network connectivity, consume minimal power, and utilize low data transmission rates. We developed a new algorithm to measure and monitor flood levels and rate changes effectively. The innovative, cost-effective, and user-friendly FMWS employs an HC-SR04 ultrasonic sensor with an Arduino microcontroller to measure flood levels and determine their status. Real-time data regarding flood levels and associated risk levels (safe, alert, cautious, or dangerous) are updated on The Things Network and integrated into TagoIO and ThingSpeak IoT platforms through a custom-built LoRaWAN gateway. The solar-powered system functions as a stand-alone beacon, notifying individuals and authorities of changing conditions. Consequently, the proposed LoRaWAN-based FMWS gathers information from catchment areas according to water level risks, triggering early flood warnings and sending them to authorities and residents via the mobile application and multiple web-based dashboards for proactive measures. The system's effectiveness and functionality are demonstrated through real-life implementation. Additionally, we evaluated the performance of the LoRa/LoRaWAN communication interface in terms of RSSI, SNR, PDR, and delay for two spreading factors (SF7 and SF12). The system's design allows for future expansion, enabling simultaneous data reporting from multiple sensor monitoring units to a server via a central gateway as a network.

Analysis of a Novel Media Access Control Protocol for LoRa

Long-range wide-area networks (LoRaWANs) are emerging technologies for the Internet of Things. They provide ultralong-range communication and low power consumption but have poor scalability due to the ALOHA media access method. In this article, we first present a novel medium access control (MAC) protocol which is time-slot-based and channel contention constrained to enhance the network performance in terms of throughput and energy consumption. We propose an analytical model which is based on three assumptions and Markov chains to analyze the performance. Various protocol and network scale settings are considered during the analysis of the performance of the network. We performed simulations in MATLAB to verify the validity of the assumptions and the accuracy of the model. We show that our new protocol enhances the throughput by approximately two times as compared to ALOHA in LoRaWAN networks, while the average energy consumption per packet is significantly reduced if the optimum protocol settings are used.

Chirp-spreaded OFDM-MFSK with differentially encoded phase for applications in low-power wide-area networks

For the unlicensed frequency bands, long-range wide-area networks (LoRaWAN) is a widespread solution for low-power wide-area networks. The physical layer of LoRaWAN is long range (LoRa) which uses a combination of chirp spread spectrum (CSS) and M-ary frequency-shift keying (M-FSK) for transmission. LoRa is known to be highly robust and well-suited for low-complexity implementation, but the weak aspect is its low spectral efficiency. Orthogonal frequency division multiplexing M-FSK (OFDM-MFSK) is a technique which combines M-FSK with OFDM. The bandwidth is divided into several parallel M-FSK transmissions, i.e., more than one sub-carrier is active at a time. This leads to an increased peak-to-average power ratio of the transmit signal. On the other hand, it allows to combine OFDM-MFSK with differential phase-shift keying (DPSK) to increase the data rate and thus the spectral efficiency. The reception of OFDM-MFSK as well as the DPSK demodulation can be done non-coherently. Therefore, they hold many similar design principles as LoRa. We propose a combination of OFDM-MFSK with differentially encoded phase and CSS. Simulation results show, that LoRa and OFDM-MFSK with CSS show similar performance with respect to power and bandwidth efficiency. Adding DPSK leads to an improved spectral as well as power efficiency.

Analysis of LoRaWAN Transactions for TEG-Powered Environment-Monitoring Devices

Long-Range (LoRa) transmission technology is potentially a suitable solution in abundant applications such as smart cities, smart industries, smart health, and others, although it is challenging and complex to implement. LoRa is a non-cellular modulation technology for Long-Range Wide-Area Networks (LoRaWAN) and is suitable for Internet of Things (IoT) solutions through its long-range and low-power consumption characteristics. The present paper provides a comprehensive analysis of LoRa wireless transactions through several measurements, which differ in LoRa parameter configuration. The results showed dependency of the power consumed by the transaction on the selected Effective Isotropic Radiated Power (EIRP). The quantity of energy consumed by the transaction also significantly depends on the selected data rate (combination of the spread factor and bandwidth) and payload.

LoRaWAN Based Indoor Localization Using Random Neural Networks

Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%.

LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective

Long range wide area networks (LoRaWANs) have recently received intense scientific, research, and industrial interest. LoRaWANs play a pivotal role in Internet of Things (IoT) applications due to their capability to offer large coverage without sacrificing the energy efficiency and, thus the battery life, of end-devices. Most published contributions assume that LoRaWAN gateways (GWs) are plugged into the energy grid; thus, neglecting the network lifetime constraint due to power storage limitations. However, there are several verticals, including precision agriculture, forest protection, and others, in which it is difficult or even impossible to connect the GW to the power grid or to perform battery replacement at the end-devices. Consequently, maximizing the networks’ energy efficiency is expected to have a crucial impact on maximizing the network lifetime. Motivated by this, as well as the observation that the overall LoRaWAN network energy efficiency is significantly affected by the selected communication protocol, in this paper, we identify and discuss critical aspects and research challenges involved in the design of a LoRaWAN communication protocol, under an energy efficiency perspective. Building upon our findings, research directions towards a novel GreenLoRaWAN communication protocol are given, focusing on achieving energy efficiency, robustness, and scalability.

LoRa-LiSK: A Lightweight Shared Secret Key Generation Scheme for LoRa Networks

Physical-layer security (PLS) schemes use the randomness of the channel parameters, namely, channel state information (CSI) and received signal strength indicator (RSSI) to generate the secret keys. There has been limited work in PLS schemes in long-range (LoRa) wide-area networks (LoRaWANs) which hinder their widespread application. Limitations observed in existing studies include the requirement of high correlation between channel parameter measurements for secret key generation in the proposed schemes and the evaluation of the schemes has only been done in either fully indoor or outdoor environments. The real-world wireless sensor networks (WSNs) and LoRa use cases might not meet both requirements thus making the current PLS schemes inappropriate for these systems. By considering the limitations found in existing PLS schemes, this article proposes LoRA-LiSK, a practical and efficient shared secret key generation scheme for LoRa networks. Our proposed LoRa-LiSK scheme consists of several preprocessing techniques (timestamp matching, two sample Kolmogorov–Smirnov tests, and a Savitzky–Golay filter), multilevel quantization, information reconciliation using Bose–Chaudhuri–Hocquenghem (BCH) codes, and finally, privacy amplification using secure hash algorithm SHA-2. The LoRa-LiSK scheme is extensively evaluated on real WSN/IoT devices in practical application scenarios: 1) indoor to outdoor and 2) LoRa static and mobile outdoor links. It outperforms existing schemes by generating keys with channel parameter measurements of low correlation values (0.2–0.6), while still essentially achieving high key generation rates, and low key disagreement rates (10%–20%). The scheme updates a key in approximately 1 h using an application profile with high transmission rate compared to 3 h reported by existing works while still respecting the duty cycle regulation. It also incurs less communication overhead compared to the existing works.