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An attempt to further enhance the accuracy and reliability of M-iWMLR localization algorithm using a new weight matrix that was formulated with two-tier RSS data normalization is presented. The two-tier normalization: data clipping and z-score normalization were applied to form a new weight matrix in this work. Data clipping was first applied to reduce significantly the effects of outliers on the RSS data while z-score normalization provides data consistency. The new localization algorithm herein, referred to as Ext.M-iWMLR algorithm is carefully evaluated by the use of location accuracy (location error), root mean square error (RMSE), range of error, and R2 score metrics. This algorithm is validated with the Modified Improved Weighted Multiple Linear Regression (M-iWMLR). The simulation results generated with MATLAB show that the Ext.M-iWMLR algorithm, at 95 percentile reduced the mean location error by 19.45%. The range of error and RMSE are reduced by 11.08% and 17.95%, respectively. Furthermore, the respective R2 scores were increased by 5.71% and 17.17% for the latitude and longitude coordinates. It was established that the new weight matrix formulated through two-step normalization enhanced all the considered metrics.

Monitoring the movement of boats in shallow waters requires a real-time monitoring system. However, for small-size wooden boats, they are still monitored manually, and data is unavailable in real time, which makes it difficult to effectively monitor them. The integration of IoT platforms with the boat monitoring system is a challenging task, especially in the transport system. This paper has the objective of developing an architectural model of a modified LoRaWAN-based boat monitoring system that is connected to a GPS-based mobile device and base station. The proposed architectural model is an integration of Bluetooth Low Energy (BLE) and LoRaWAN networks, which are also tested in real time to solve the boat traffic monitoring issues. The field tests with parameters of signal transmission, location coordinates, and position of the boats are also presented. The analysis result shows the proposed model is suitable for waters with high noise levels, especially in shallow water and delta rivers. The signal noise can be reduced by extracting the real-time data. In addition, signal interference can be minimized. The performance of this system is also compared to the reference system in real conditions, which shows an adequate correlation result. This proof of concept forms an important basis for deploying it for large-scale applications and commercialization capabilities. Doi: 10.28991/ESJ-2023-07-04-011 Full Text: PDF

In the ever-evolving construction industry, the incorporation of the Industrial Internet of Things (IIoT) through Low-power Wireless Area Networks (LPWAN), such as LoRaWAN, has emerged as a practical solution for addressing the challenges posed by the limited 5G cellular coverage found in solutions like NB-IoT and LTE-M, especially when deployed in remote locations. Open-source LPWAN platforms like The Things Network (TTN) and ChirpStack have played a pivotal role in fostering the adoption of LoRa technology by providing a mature and cost-effective ecosystem that facilitates efficient device resource management. Within this context, maintaining continuous surveillance of LPWAN network gateways becomes critically important, requiring a meticulous examination of status indicators and an evaluation of the communication quality. This paper introduces a structured approach for extracting data from TTN to create a comprehensive gateway monitoring system. The methodology encompasses various aspects, including ensuring seamless server connectivity, specifically focusing on efficient information management and integration of real-world construction data. This foundational work sets the stage for a more in-depth exploration of the diverse management components within the network ecosystem. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices increasingly contribute to critical infrastructures, necessitating robust security measures. LoRaWAN, a low-power IoT network, employs the Advanced Encryption Standard (AES) with a 128-bit key for encryption and integrity, balancing efficiency and security. As computational capabilities of devices advance and recommendations for stronger encryption, such as AES-256, emerge, the implications of using longer AES keys (192 and 256 bits) on LoRaWAN devices' energy consumption and processing time become crucial. Despite the significance of the topic, there is a lack of research on the implications of using larger AES keys in real-world LoRaWAN settings. To address this gap, we perform extensive tests in a real-world LoRaWAN environment, modifying the source code of both a LoRaWAN end device and open-source server stack to incorporate larger AES keys. Our results show that, while larger AES keys increase both energy consumption and processing time, these increments are minimal compared to the time on air. Specifically, for the maximum payload size we used, when comparing AES-256 to AES-128, the additional computational time and energy are, respectively, 750 m s and 236 μJ. However, in terms of time on air costs, these increases represent just 0.2% and 0.13%, respectively. Our observations confirm our intuition that the increased costs correlate to the number of rounds of AES computation. Moreover, we formulate a mathematical model to predict the impact of longer AES keys on processing time, which further supports our empirical findings. These results suggest that implementing longer AES keys in LoRaWAN is a practical solution enhancing its security strength while not significantly impacting energy consumption or processing time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

LoRaWAN is a communication protocol designed especially for Internet of Things (IoT) applications that offers benefits such as long-distance connection and low power consumption. Due to the characteristics of LoRaWAN, this technology has gained great popularity in various IoT applications, such as environmental monitoring, smart agriculture, and applications in the areas of health and mobility, among others. Given this situation, the objective of this work is to provide an in-depth overview of LoRaWAN technology in terms of its applications, as well as the devices that have been used for the development of such applications. Additionally, this work reviews what other areas of LoRaWAN have been covered in different scientific articles, i.e., performance improvement and security. Among the main results of this study though analyzing previous works, we can say that most of them have been developed in the area of environmental monitoring and have used low-cost devices such as Arduinos, Raspberry Pis, and relatively low-cost commercial products such as those of the Semtech and STMicroelectronics brands. The analysis of the present work shows objectively and formally that LoRaWAN technology can be applied in various applications and that there are many studies that try to optimize its performance and security. This paper seeks to identify and describe the most relevant applications of LoRaWAN in different sectors, such as agriculture, health, and environmental monitoring, among others, and the challenges and solutions found in each area. This literature review will provide a valuable reference to understand the potential and opportunities offered by LoRaWAN technology. [ABSTRACT FROM AUTHOR]

Coverage limitation due to signal attenuation is one of the main challenges facing single-hop LoRaWAN when deployed in IoT applications that require low power consumption and operate over a wide area network. The challenge can be figured out using the dense deployment of typical LoRaWAN gateways to ensure devices can transfer their data to the network server successfully. However, this is considered a costly path because of the requirement of deploying a larger number of gateways. To cope with this, a multi-hop communication strategy can be utilized to extend the coverage of employed LoRaWAN networks, eliminating the need for dense gateway deployment. Thus, this paper proposes a Listen-To-Talk-based Multi-hop LoRaWAN LTM-LoRaWAN, a multi-hop relaying system for a wide area LoRaWAN network. It supports out-of-range devices to deliver their traffic to the gateway without using complex routing mechanisms or routing topology construction. Moreover, the proposed system is dynamic and does not require any special entities or assign specific tasks to specific devices. The paper provides a detailed description of the design and configuration of the proposed LTM-LoRaWAN. Using simulation, the feasibility of the proposed system was demonstrated. The evaluation result showed that the proposed system outperformed one-hop LoRaWAN and achieved better reliability in traffic delivery. [ABSTRACT FROM AUTHOR]

COVID-19 has had a devastating impact worldwide, including in care homes where there have been substantial numbers of cases among a very vulnerable population. A key mechanism for managing exposure to the virus and targeting interventions is contact tracing. Unfortunately, environments such as care homes that were most catastrophically impacted by COVID-19 are also those least amenable to traditional contact tracing. A promising alternative to recall and smartphone-based contact tracing approaches is the use of discrete wearable devices that exploit Bluetooth Low Energy (BLE) and Long-Range Wide Area Network (LoRaWAN) technologies. However, the real-world performance of these devices in the context of contact tracing is uncertain. A series of experiments were conducted to evaluate the performance of a wearables system that is based on BLE and LoRaWAN technologies. In each experiment, the number of successful contacts was recorded and the physical distance between two contacts was compared to a calculated distance using the Received Signal Strength Indication (RSSI) to determine the precision, error rate, and duration of proximity. The overall average system contact detection success rate was measured as 75.5%; when wearables were used as per the manufacturer's guidelines the contact detection success rate increased to 81.5%, but when obstructed by everyday objects such as clothing or inside a bag the contact detection success rate was only 64.2%. The calculated distance using RSSI was close to the physical distance in the absence of obstacles. However, in the presence of typical obstacles found in care home settings, the reliability of detection decreased, and the calculated distance usually appeared far from the actual contact point. The results suggest that under real-world conditions there may be a large proportion of contacts that are underestimated or undetected. [ABSTRACT FROM AUTHOR]

Long Range Wide Area Network (LoRaWAN) technology has established new concepts for long-range wireless communication, being widely used in the implementation of IoT solutions. Therefore, it is crucial to validate the coverage of the signal and to know the distance at which a LoRAWAN communication system can be connected. This document investigates LoRaWAN technology for cases based on urban environments, so it can be used as a guide for those projects that require predicting the connection distance range of a LoRaWAN link. In addition, it serves as a tool for the reader when it comes to predicting the coverage of Long-Range Wide Area Network (LoRaWAN) technology. Measurements were made in a LoRaWAN network deployed in urban environments, where RSSI measurements were made in the city of Bogotá D.C., Colombia. Experimentally RSSI values were compared with four different propagation models at a frequency of 915 MHz in urban environments. The contribution of this work is an adjustment to widely used prediction models, according to the recommendation of the International Telecommunications Union (ITU) ITU-R P.1546, which allows estimating coverage in scenarios with characteristics similar to Bogotá D.C. This allows to know with precision the coverage before implementing the LoRaWAN communications system at 915 MHz. The results of comparing field measurements with fitted propagation models show that the Okumura model is the best predictor of coverage with a minimum error rate. [ABSTRACT FROM AUTHOR]

Nowadays, LoRaWAN has become one of the most widely deployed low-power wide-area technologies suitable for Internet of Things applications. Sensor nodes powered by battery sources with a finite lifetime are the most common in these applications. Deploying a massive number of these nodes that send sporadic traffic increases collisions. In this paper, we propose a two-step algorithm in order to increase LoRaWAN network throughput, extend its lifetime, and therefore achieve high energy efficiency. First, we formulate a multi-objective optimization problem that jointly maximizes the network throughput and minimizes power consumption. Then, the power control step reduces the transmission power to the minimum allowed level, which preserves the reliability of communications. Our algorithm derives the optimal spreading factor and transmission power configuration for all nodes in LoRaWAN networks with multiple gateways. The results show that the proposed algorithm achieves higher energy efficiency compared to adaptive data rate (ADR) and other relevant state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

LoRa technology has gained popularity as one of the most widely used standards for device interconnection due to its ability to cover long distances and energy efficiency, making it a suitable choice for various Internet of Things (IoT) monitoring and control applications. In this sense, this work presents the development of a visual support tool for creating IoT devices with LoRa and LoRaWAN connectivity. This work significantly advances the state of the art in LoRa technology by introducing a novel visual support tool tailored for creating IoT devices with LoRa and LoRaWAN connectivity. By simplifying the development process and offering compatibility with multiple hardware solutions, this research not only facilitates the integration of LoRaWAN technology within educational settings but also paves the way for rapid prototyping of IoT nodes. The incorporation of block programming for LoRa and LoRaWAN using the Arduinoblocks framework as a graphical environment enhances the capabilities of the tool, positioning it as a comprehensive solution for efficient firmware generation. In addition to the visual tool for firmware generation, multiple compatible hardware solutions enable easy, economical, and stable development, offering a comprehensive hardware and software solution. The hardware proposal is based on an ESP32 microcontroller, known for its power and low cost, in conjunction with an RFM9x module that is based on SX127x LoRa transceivers. Finally, three successfully tested use cases and a discussion are presented. [ABSTRACT FROM AUTHOR]

In maritime settings, effective communication between vessels and land infrastructure is crucial, but existing technologies often prove impractical for energy-sensitive IoT applications, like deploying sensors at sea. In this study, we explore the viability of a low-power, cost-effective wireless communication solution for maritime sensing data. Specifically, we conduct an experimental assessment of the Azorean Long Range Wide Area Network (LoRaWAN) coverage. Our tests involve positioning the gateway at the island's highest point and installing end nodes on medium-sized fishing vessels. Through measurements of received signal strength indicator (RSSI), signal-to-noise ratio (SNR), and lines of sight (LOS), we showcase the potential of LoRaWAN transmissions to achieve communication distances exceeding 130 km in a LOS-free scenario over the ocean. These findings highlight the promising capabilities of LoRaWAN for reliable and long-range maritime communication of sensing data. [ABSTRACT FROM AUTHOR]

In the past decade, Long-Range Wire-Area Network (LoRaWAN) has emerged as one of the most widely adopted Low Power Wide Area Network (LPWAN) standards. Significant efforts have been devoted to optimizing the operation of this network. However, research in this domain heavily relies on simulations and demands high-quality real-world traffic data. To address this need, we monitored and analyzed LoRaWAN traffic in four European cities, making the obtained data and post-processing scripts publicly available. For monitoring purposes, we developed an open-source sniffer capable of capturing all LoRaWAN communication within the EU868 band. Our analysis discovered significant issues in current LoRaWAN deployments, including violations of fundamental security principles, such as the use of default and exposed encryption keys, potential breaches of spectrum regulations including duty cycle violations, SyncWord issues, and misaligned Class-B beacons. This misalignment can render Class-B unusable, as the beacons cannot be validated. Furthermore, we enhanced Wireshark's LoRaWAN protocol dissector to accurately decode recorded traffic. Additionally, we proposed the passive reception of Class-B beacons as an alternative timebase source for devices operating within LoRaWAN coverage under the assumption that the issue of misaligned beacons can be addressed or mitigated in the future. The identified issues and the published dataset can serve as valuable resources for researchers simulating real-world traffic and for the LoRaWAN Alliance to enhance the standard to facilitate more reliable Class-B communication. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) enables us to gain access to a wide range of data from the physical world that can be analyzed for deriving critical state information. In this regard, machine learning (ML) is a valuable tool that can be used to develop models based on observed physical data, leading to efficient analytical decisions, including anomaly detection. In this work, we address some key challenges for applying ML in IoT applications that include maintaining privacy considerations of user data that are needed for developing ML models and minimizing the communication cost for transmitting the data over the IoT network. We consider a representative application of the anomaly detection of ECG signals that are obtained from a set of low-cost wearable sensors and transmitted to a central server using LoRaWAN, which is a popular and emerging low-power wide-area network (LPWAN) technology. We present a novel framework utilizing federated learning (FL) to preserve data privacy and appropriate features for uplink and downlink communications between the end devices and the gateway to optimize the communication cost. Performance results obtained from computer simulations demonstrate that the proposed framework leads to a 98% reduction in the volume of data that is required to achieve the same level of performance as in traditional centralized ML. [ABSTRACT FROM AUTHOR]

The transit service in a city should be the most efficient, least polluting, most accessible, and sustainable means of transportation for its citizens. However, serious shortcomings have been detected, mainly in medium-sized cities in developing countries. These shortcomings are related to a lack of user information, insecurity, low service availability, and repeated stops in inappropriate and/or unauthorized places. Some of these shortcomings contribute to high accident rates and traffic congestion. The development of tools to improve the characteristics and conditions of transit service in cities has become an imperative need to improve the quality of life of citizens and city sustainability. Transit service tracking is relevant in aspects such as online location information to travelers and control by transport companies for compliance with speed limits, schedules, routes, and stops. This research proposes a transit vehicle tracking system based on the Internet of Vehicles (IoV) in Vehicle-to-Roadside (V2R) classification. The proposed system is ideal for the use of electric vehicles due to the low power consumption of the tracking device. This system uses Intelligent Transportation Systems (ITS) tracking service architecture, Long Range (LoRa) communication technology, and its LoRa Wide Area Network (LoRaWAN) protocol. Additionally, the system offers real-time location prediction in the absence of position data. The IoV tracking device integrates a GPS-LoRa module card with an Inertial Measurement Unit (IMU). A location prediction algorithm was implemented to train and store a prediction model with previously collected data from tracking devices. To evaluate the developed model, a case study in the city of Popayán (Colombia) was implemented, using three routes for testing. The results of the system implementation were satisfactory, obtaining an average coverage of 60.4% of the routes in the final field tests through LoRa communication. For the remaining 39.6% of the routes, location data prediction was used, with an average accuracy of 177 m with respect to the real location. Considering the obtained results, a tracking system such as the one proposed in this article can be used in the transit systems of medium-sized cities in developing countries to improve service quality and fleet control. [ABSTRACT FROM AUTHOR]

Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia.

Abstract 1. Information provided by tracking studies using remote telemetry is providing ecologists with invaluable new insights into animal behaviour and movement strategies. Here we describe a new type of GNSS (Global Navigation Satellite System) tracking device currently under development and nearing commercialisation, which transmits data via LoRaWAN (long range wide area network) gateways. These tags have the potential to be a low weight and power consumption solution for tracking the movement of animals at high resolution. 2. We characterise the position accuracy and data transmission range, including uplinks and downlinks, for the tracker using a series of ground-based field tests. Data transmission range was tested by visiting locations with line of sight to the LoRaWAN Gateway at distances up to 75 km and recording whether data transmission was completed successfully from each location. These tests were complemented by a trial deployment of six devices on griffon vultures Gyps fulvus. 3. These LoRa tags reliably provided accurate position estimates, particularly on more frequent acquisition cycles. At 1-min intervals the GNSS location bias was 4.71 m in the horizontal plane and 5 m in the vertical plane while precision, measured by standard deviation, was 3.9 m in horizontal space and 7.7 m in vertical space. Ground-based range tests confirmed data transmission from a maximum distance of 40.7 km. Initial results from a deployment on griffon vultures yielded useful information about flight speeds, altitude, and transmission range (up to 53.4 km). 4. With consistent GNSS position accuracy and the ability to transmit data over tens of kilometres, the LoRa tags demonstrated potential for monitoring animal movement over large areas. The small size and power needs of the device allow for flexibility in which combination of battery, solar panel, and housing they are paired with. The tags can be assembled in housing formats ranging in size from less than 5 g for deployment on Kestrel sized birds to 80 g for deployment on large birds such as vultures. The devices are particularly suitable for philopatric (site-faithful) species because LoRa gateways can be installed near breeding sites to maximise opportunities for data transmission. Our findings are informative for studies seeking to use LoRa for tracking birds and other animals using the miro-Nomad or a different type of GPS-LoRa logger.

Despite the ability of Low-Power Wide-Area Networks to offer extended range, they encounter challenges with coverage blind spots in the network. This article proposes an innovative energy-efficient and nature-inspired relay selection algorithm for LoRa-based LPWAN networks, serving as a solution for challenges related to poor signal range in areas with limited coverage. A swarm behavior-inspired approach is utilized to select the relays’ localization in the network, providing network energy efficiency and radio signal extension. These relays help to bridge communication gaps, significantly reducing the impact of coverage blind spots by forwarding signals from devices with poor direct connectivity with the gateway. The proposed algorithm considers critical factors for the LoRa standard, such as the Spreading Factor and device energy budget analysis. Simulation experiments validate the proposed scheme’s effectiveness in terms of energy efficiency under diverse multi-gateway (up to six gateways) network topology scenarios involving thousands of devices (1000–1500). Specifically, it is verified that the proposed approach outperforms a reference method in preventing battery depletion of the relays, which is vital for battery-powered IoT devices. Furthermore, the proposed heuristic method achieves over twice the speed of the exact method for some large-scale problems, with a negligible accuracy loss of less than 2%.

Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral.

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.

The aim of this paper is to discuss the usability of vibrations as energy sources, for the implementation of energy self-sufficient wireless sensing platforms within the Industrial Internet of Things (IIoT) framework. In this context, this paper proposes to equip vibrating assets like machinery with piezoelectric sensors, used to set up energy self-sufficient sensing platforms for hard-to-reach positions. Preliminary measurements as well as extended laboratory tests are proposed to understand the behavior of commercial piezoelectric sensors when employed as energy harvesters. First, a general architecture for a vibration-powered LoRaWAN-based sensor node is proposed. Final tests are then performed to identify an ideal trade-off between sensor sampling rates and energy availability. The target is to ensure continuous operation of the device while guaranteeing a charging trend of the storage component connected to the system. In this context, an Ultra-Low-Power Energy-Harvesting Integrated Circuit plays a crucial role by ensuring the correct regulation of the output with very high efficiency.

In the majority of Internet of Things (IoT) applications, persistent and stable operation is a crucial requirement. While environmental energy-harvesting technologies can enhance IoT’s persistence, they do not guarantee stability. Therefore, we aim to address the stability challenges in solar-powered IoT (SP-IoT) by employing wireless power transmission (WPT) through unmanned aerial vehicles (UAVs). This study focuses on determining the optimal charging mobility of drones for WPT to enhance the stability of nodes operating in a wide area network (WAN)-based SP-IoT environment. The proposed scheme identifies nodes with insufficient solar energy harvesting and defines the optimal charging mobility parameters (hovering position, hovering time, and moving path) to efficiently transmit the drone’s energy to these nodes in a balanced manner. The experimental results confirm that the proposed scheme significantly improves the stability of solar-powered IoT nodes by optimally utilizing the limited energy of the drone.

Low-Power Wide-Area Networks constitute a leading, emerging Internet-of-Things technology, with important applications in environmental and industrial monitoring and disaster prevention and management. In such sensor networks, external detectable events can trigger synchronized alarm report transmissions. In LoRaWANs, and more generally in networks with a random access-based medium access algorithm, this can lead to a cascade of frame collisions, temporarily resulting in degraded performance and diminished system operational capacity, despite LoRaWANs’ physical layer interference and collision reduction techniques. In this paper, a novel scheduling algorithm is proposed that can increase system reliability in the case of such events. The new adaptive spatial scheduling algorithm is based on learning automata, as well as previous developments in scheduling over LoRaWANs, and it leverages network feedback information and traffic spatial correlation to increase network performance while maintaining high reliability. The proposed algorithm is investigated via an extensive simulation under a variety of network conditions and compared with a previously proposed scheduler for event-triggered traffic. The results show a decrease of up to 30% in average frame delay compared to the previous approach and an order of magnitude lower delay compared to the baseline algorithm. These findings highlight the importance of using spatial information in adaptive schemes for improving network performance, especially in location-sensitive applications.

Vehicular wireless networks are one of the most valuable tools for monitoring platforms in the automotive domain. At the same time, Internet of Things (IoT) solutions are playing a crucial role in the same framework, allowing users to connect to vehicles in order to gather data related to their working cycle. Such tasks can be accomplished by resorting to either cellular or non-cellular wireless technologies. While the former can ensure low latency but require high running costs, the latter can be employed in quasi-real-time applications but definitely reduce costs. To this end, this paper proposes the results of two measurement campaigns aimed at assessing the performance of the long-range wide-area network (LoRaWAN) protocol when it is exploited as an enabling technology to provide vehicles with connectivity. Performances are evaluated in terms of packet loss (PL) and received signal strength indicator (RSSI) in wireless links. The two testing scenarios consisted of a transmitter installed on a motorbike running on an elliptical track and a receiver placed in the centre of the track, and a transmitter installed on the roof of a car and a receiver placed next to a straight road. Several speeds were tested, and all the spreading factors (SFs) foreseen by the protocol were examined, showing that the Doppler effect has a marginal influence on the receiving performance of the technology, and that, on the whole, performance is not significantly affected by the speed. Such results prove the feasibility of LoRaWAN links for vehicular network purposes.

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. [ABSTRACT FROM AUTHOR]

In this research, the main objective is to design a smart meter to measure electricity consumption in households with communications based on long-range wide area network (LoRaWAN) wireless technology. Two devices have been designed, a smart meter for electrical energy in households (SMEEH) and a LoRa network supervisor (LNS), which optimises the LoRAWAN parameters using the teaching–learning-based optimisation (TLBO) algorithm. This algorithm allows obtaining the parameters' spreading factor, bandwidth, and code rate so that the minimum value of the packet loss rate (PLR) is reached, and the load profiles of the households are modelled in real time using cloud data storage. The algorithm implemented in the LNS determines the most appropriate parameters of the LoRaWAN by checking data traffic in real time. The household's electrical energy measuring system obtains data through sensors. Load profiles of households obtained by measuring the voltage, current, and active power with the SMEEH using the TLBO algorithm are more accurate. They allow the consumer and/or company to adjust the time and perform actions such as energy management and tariff adjustments. In addition, LoRaWAN optimisation means less data loss, more reliable load profiles, mean relative errors lower than 7.7%, and improved network performance. The TBLO algorithm uses short computation times to get the best solution for the LoRaWAN in real time and achieves a PLR of around 20% using the LNS. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) is a process where, through sensors-based electronic systems and output data analysis, the structural integrity of a building or an infrastructure is observed and determined through periodically sampled measurements for material and geometric variations, ensuring safety and minimizing risk factors. Over the years, the approach to this problem evolved both in terms of system technology, with the use of more accurate sensors, and the parameters used to determine the building status, i.e., the extraction of the damage index. In these terms, structural modal harmonics-based analysis is acquiring more and more relevance. In this paper, we propose a fully custom master multi-slave system used for SHM purposes, capable of acquiring data from six different channels coming from two independent accelerometers suitably configured as to synchronously produce spectrograms and static trim of the monitored structure. A test of the system has been performed on a modular structure subject to random perturbation. Time and frequency domain results were analyzed before and after structural alterations. Their effects were studied in a 500 Hz bandwidth, showing a drift in the resonant frequency of the structure from 145 Hz to 152 Hz, and, additionally, a harmonic displacement: medium-frequency components experienced a 50 Hz drift, while low-frequency components collapsed into two harmonics at 60 Hz and 90 Hz. These tests proved the system's functionality and the feasibility of this method for structural integrity analysis and, eventually, damage evaluation. [ABSTRACT FROM AUTHOR]

One of the essential components in LoRaWAN is managing the server with all existing components, including Device Management, Data forwarding, Network security, and real-time network monitoring. It is necessary to manage the uplink and downlink of the LoRaWAN server and maintain the quality of data transmission from the LoRa end node to the LoRaWAN server. Depending on the service provider, the LoRaWAN Server provides space for payload, uplink, and downlink data. How many times in a day, week, month, or even year? This discussion depends on the LoRaWAN server provider. Some are open sources, and some provide free uplink and downlink services. Settings on the LoRaWAN Server will determine how much data will enter the server or the network size. Security is also an essential parameter, e.g., encryption or authentication, supported by the feature requirements used on the server, which will determine the type of communication that LoRa will apply, for example, multi-communication or analysis data. Centralized management, Security, scalability, and cost-effectiveness are essential parameters if LoRaWAN is managed well. [ABSTRACT FROM AUTHOR]

At present, the influence of the surrounding environment on the health of humans is attracting increasing attention, and the indoor environment plays a significant role in people's health. Therefore, it is necessary to effectively monitor indoor environments in real time. With the increase of end‐devices, the rapid growth of data is exerting a pressure on the network bandwidth. To solve this problem, a LoRaWAN monitoring system for indoor environments based on embedded edge computing is proposed here. A hardware architecture of end‐devices and the workflow of the LoRaWAN gateway are designed. In the gateway, edge computing is applied to manage the integrated access device and clean data. The experimental results show that the data reduction rate increased obviously after using edge computing. The environmental data can be transmitted in real time, and the communication efficiency of the system can be improved. [ABSTRACT FROM AUTHOR]

The planning of metering network infrastructure based on the concept of the Internet of Things primarily involves the choice of available radio technology. Then, regardless of the type and availability of power sources, energy conservation should be one of the main optimization criteria. For this reason, LPWANs operating in unlicensed ISM bands appear to be a suitable solution in urban environments due to their sub 1 GHz propagation properties. High signal penetration and coverage make them applicable in urban areas with buildings and various obstacles. Therefore, this article presents solutions developed to support the planning process of implementing a LoRaWAN network infrastructure aimed at monitoring and collecting electricity meter data in smart cities. To this end, an algorithm has been proposed to support the selection of the number of LoRaWAN gateways and their deployment, as well as the selection of transmission parameters at the measurement nodes with a particular focus on geographic data from real maps. [ABSTRACT FROM AUTHOR]

Low power wide area networks (LPWANs) are made to survive conditions of extensive installation. Technological innovations, including Global Network Operator, Long Range Wide Area Network (LoRaWAN), Narrowband Internet of Things (NB-IoT), Weightless, Sigfox, etc., have adopted LPWANs. LoRaWAN is currently regarded to be one of the most cutting-edge and intriguing technology for the widespread implementation of the IoT. Although LoRaWAN offers the best features that make it fit with Internet - of - things specifications, there are still certain technical issues to overcome, such as link coordination, resource allocation and reliable transmission. In LoRaWAN, End-devices transmit randomized uplink frames to the gateways using un-slotted random-access protocol. This randomness with the restrictions placed on the gateways is a reason that leads to a considerable decline in network performance, in particular downlink frames. In this paper, we propose a new approach to increase Acknowledgement (ACK) messages throughput. The suggested method takes advantage of both class A and class B features to enhance and assist LoRaWAN's reliability by ensuring that an ACK message is sent for every confirmed uplink while retaining the minimum energy level that is utilized by nodes. [ABSTRACT FROM AUTHOR]

Long Range Wide Area Network (LoRaWAN) has been developed to meet the requirements for the enormous device-to-device communication of Internet of Things (IoT) networks, which consist of a large number of participating devices spread over large coverage areas with low data rates and low power consumption. It supports communications in both directions, uplink, and downlink directions. However, the downlink communication in the current LoRaWAN raises the bottleneck issue at gateways due to the used gateway selection algorithm. This paper proposes a novel gateway selection algorithm based on the duty cycle time-off values for the existing gateways, Duty Cycle Gateway Selection (DCGS), to direct acknowledgment packets as downlink traffic towards the most suitable gateway. Thus, the proposed system avoids subsequent retransmission of previously sent traffic that leads to excessive traffic overloading the network. The proposed system avoids exhausting a gateway duty cycle with downlink traffic by distributing the downlink traffic among available gateways based on the duty cycle time off. DCGS is evaluated using FloRa and INET frameworks in the well-known network simulator OMNeT++. The result shows the superior performance of the proposed approach over the existing Signal-to-Noise ratio (SNR) based selection mechanism. It clearly indicates that the DCGS maintains a better confirmed packet delivery rate while reducing number of retransmissions, collisions, and power consumption. [ABSTRACT FROM AUTHOR]

Context. The emergence of the Internet of Things (IoT) has led to the development of various low-power wide area network (LPWAN) technologies that are designed to provide transmission of small data packets over long distances with minimal energy consumption. The two most well-known LPWAN technologies are LoRaWAN and Sigfox. This study aims to compare the energy efficiency of these two technologies to determine their suitability for use in autonomous solutions. Objective. The objective of this study is to compare the energy efficiency of LoRaWAN and Sigfox technologies for IoT devices. The comparison will help determine which technology is better for autonomous solutions when devices need to operate for extended periods of time without frequent battery replacements. Method. In this work, taking into account the specifications of the investigated radio technologies, mathematical modeling of the time of data transmission or reception is used depending on the payload, and information on the power supply current is taken from official datasheets for the components of the investigated devices. Results. The results of the study show that both LoRaWAN and Sigfox are energy-saving technologies, but LoRaWAN is generally more energy-efficient than Sigfox. In addition, LoRaWAN has adaptive modes and significantly more manual settings, which in some cases further reduces the energy per bit of data compared to Sigfox. Conclusions. LoRaWAN is the best choice for autonomous solutions where energy efficiency is crucial. This study provides valuable information for designers and developers of IoT devices, allowing them to make informed decisions when choosing LPWAN technologies for their autonomous solutions. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) may be considered as an emerging paradigm that has led to the transformation of the physical world into an advanced system comprising interconnected devices on an unparalleled scale. To accommodate the spectrum, need of these numerous IoT devices concept of cognitive radio technology may be considered a boon. The cognitive radio (CR) aims to alleviate the spectrum crunch problem through detection of the available spectrum holes and their efficient utilization. This paper incorporates the spectrum sensing concept in the LoRaWAN network for better utilization of the spectrum. A non-relay and relay-based type of communication has been investigated to analyze the enhancement in the detection probability of the sensing technique for a LoRaWAN network in terms of minimal sensing time, maximum coverage, and low signal-to-noise (SNR). The effect of sensing time, distance and SNR on detection probability for 915 MHz and 866.4 MHz LoRa bands has been analyzed and critically evaluated at uplink bandwidth of 125 kHz and 250 kHz respectively using MATLAB. The simulated results validate the performance improvement through multiple relays in a LoRaWAN communication model in context to a higher probability of detection for energy detection-based spectrum sensing. [ABSTRACT FROM AUTHOR]

LoRaWAN has imposed itself as a promising and suitable technology for massive machine-type communications. With the acceleration of deployment, improving the energy efficiency of LoRaWAN networks has become paramount, especially with the limitations of throughput and battery resources. However, LoRaWAN suffers from the Aloha access scheme, which leads to a high probability of collision at large scales, especially in dense environments such as cities. In this paper, we propose EE-LoRa, an algorithm to improve the energy efficiency of LoRaWAN networks with multiple gateways via spreading factor selection and power control. We proceed in two steps, where we first optimize the energy efficiency of the network, defined as the ratio between the throughput and consumed energy. Solving this problem involves determining the optimal node distribution among different spreading factors. Then, in the second step, power control is applied to minimize the transmission power at nodes without jeopardizing the reliability of communications. The simulation results show that our proposed algorithm greatly improves the energy efficiency of LoRaWAN networks compared to legacy LoRaWAN and relevant state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Resource allocation in LoRaWAN is expressed as an optimization problem of spreading factor allocation and channel allocation, especially when there are a large number of connected devices in LoRaWAN, to ensure the fairness of throughput among LoRa users with limited spectrum resources. Firstly, the matching theory is introduced. LoRa users and channels, and LoRa users and spreading factors are used as matching parties to maximize their utility. Therefore, a matching-based channel and spreading factor assignment algorithm is proposed. Based on the matching theory, with the goal of maximizing utility, by optimizing the results of network channel and spreading factor allocation, the minimum channel capacity achieved in LoRaWAN is maximized. A fair airtime initialization algorithm is proposed to ensure the fairness of the throughput of each group of parameters. The simulation results show that the fair airtime initialization algorithm can obtain better initial allocation results than other allocation schemes. The matching-based channels and spreading factors assignment algorithm can significantly increase the LoRa network data extraction rate and greatly reduce network energy consumption. [ABSTRACT FROM AUTHOR]

Long Range Wide Area Network (LoRaWAN) has been developed to meet the requirements for the enormous device-to-device communication of Internet of Things (IoT) networks, which consist of a large number of participating devices spread over large coverage areas with low data rates and low power consumption. It supports communications in both directions, uplink, and downlink directions. However, the downlink communication in the current LoRaWAN raises the bottleneck issue at gateways due to the used gateway selection algorithm. This paper proposes a novel gateway selection algorithm based on the duty cycle time-off values for the existing gateways, Duty Cycle Gateway Selection (DCGS), to direct acknowledgment packets as downlink traffic towards the most suitable gateway. Thus, the proposed system avoids subsequent retransmission of previously sent traffic that leads to excessive traffic overloading the network. The proposed system avoids exhausting a gateway duty cycle with downlink traffic by distributing the downlink traffic among available gateways based on the duty cycle time off. DCGS is evaluated using FloRa and INET frameworks in the well-known network simulator OMNeT++. The result shows the superior performance of the proposed approach over the existing Signal-to-Noise ratio (SNR) based selection mechanism. It clearly indicates that the DCGS maintains a better confirmed packet delivery rate while reducing number of retransmissions, collisions, and power consumption. [ABSTRACT FROM AUTHOR]

This paper elucidates the importance of creating backup communication channels in emergencies when access to cellular and satellite communication systems can be limited. The purpose of the study is to substantiate the use of LoRaWAN technology as a basis for building reliable backup communication facilities. A block diagram of the device has been proposed, which is used as an example for modeling the autonomy time of the device. It is shown that, depending on the mode of operation, this time ranges from 150 to 900 days. To estimate the radio accessibility zone of the device, the maximum transmission range was modeled using various models of radio wave propagation. The results of the investigations have shown that the developed emergency communication system based on the LoRaWAN protocol is effective and superior to other wireless communication technologies under certain conditions. [ABSTRACT FROM AUTHOR]

In the area of low-power wireless networks, one technology that many researchers are focusing on relates to positioning methods such as fingerprinting in densely populated urban areas. This work presents an experimental study aimed at quantifying mean location estimation error in populated areas. Using a dataset provided by the University of Antwerp, a neural network was implemented with the aim of providing end-device location. In this way, we were able to measure the mean localization error in areas of high urban density. The results obtained show a deviation of less than 150 m in locating the end device. This offset can be decreased up to a few meters, provided that there is a greater density of nodes per square meter. This result could enable Internet of Things (IoT) applications to use fingerprinting in place of energy-consuming alternatives.

Low power wide area network (LPWAN) technologies have become an integral part of Internet-of-Things (IoT) applications due to their ability to meet key requirements such as long range, low cost, massive device numbers, and low energy consumption. Among all available LPWAN technologies, LoRaWAN has garnered significant interest from both industry and academia. Due to wide communication range of LoRaWAN, the coverage of gateway (GW) may overlap and the packet transmissions from nodes in the overlapping area would collide, which deteriorates the network performance. How to tune the backoff parameter settings to reduce collision and maximize the network throughput in multi-GW LoRaWAN is still an open issue. To solve this issue, in this paper, we first propose a low-complexity model for multi-GW LoRaWAN, which divides nodes into different groups based on GWs they can communicate with. Key performance metrics, i.e., the network throughput, access delay and the probability of successful transmission, are derived as functions of backoff parameter and input rate. As the throughput performance crucially depends on whether each group is saturated or not, we propose an iterative algorithm to analyze the stability of the multi-GW LoRaWAN, based on which we further develop an iterative algorithm to tune the backoff parameter of each group iteratively for the network throughput optimization. The simulation results validate the effectiveness of the proposed algorithms and reveal the trade-off between fairness and efficiency.

Long Range Wide Area Network (LoRaWAN) is currently one of the leading communication technologies for the Internet of Things (IoT) connectivity. It offers long-range and wide-area communication at low-power, low cost and low data rate. However, several studies demonstrated that LoRaWAN exhibits excessive collisions and thus performance degradation especially at large scale. This is mainly due to the ALOHA-based channel access technique adopted for uplink communications in LoRaWAN. In this work, we present a new schedule-based schema which allows a deterministic allocation of time, channel and spreading factor, for a collision-free uplink communication in LoRaWAN. For the sake of interoperability, our schema does not involve any additional synchronization phase for end-devices, or major changes in LoRaWAN specification as suggested in most of the existing studies in the literature. The performance evaluation of our proposed scheme proved an outstanding improvement of the network performance in terms of latency and energy-consumption. For instance, for an inter-packet transmission interval equal to 1800s (one packet each 30 min) and a cell size equal to 1000 end-devices, results show that using the proposed schedule-based schema, the uplink communication latency and energy consumption are reduced respectively by 89% and 78%, compared to the original LoRaWAN legacy class A.

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.

Low-power wide-area networks (LPWAN) are commonly used because they meet the requirements of Internet-of-Things (IoT) networks with a large number of end devices, such as high network scalability, wide area coverage, low data rates, and delay tolerance while consuming very little energy. The LoRa wide-area network (LoRaWAN) is one of the most popular solutions, supporting three types of medium access control (MAC) options to handle distinct application demands. Class B shortens downlink frame transmission latency while maintaining low energy consumption in the end device. This article analyzes the operation of gateways with class B devices to determine the events that influence scalability and performance, presents an analytical model to describe these systems, and proposes an optimization mechanism called Adaptive Beacon Period Configurator (ABPC). ABPC changes the time-related parameters configuration to improve the usability of these networks in dynamic scenarios. The proposed solution is then simulated and tested against the analytical model. The tradeoff between the waiting time between messages, the probability of reception, and the energy consumption of an end device is shown in the results, describing how traffic density increases impacts in these Key Performance Indicators (KPI) and how to try to guarantee these requirements in a network deployment.