Long Range Wide Area Network Network Research Articles

A hybrid convolutional neural network-transformer method for received signal strength indicator fingerprinting localization in Long Range Wide Area Network

In recent years, low-power wide area networks (LPWANs), particularly Long-Range Wide Area Network (LoRaWAN) technology, are increasingly being adopted into large-scale Internet of Things (IoT) applications thanks to having the ability to offer cost-effective long-range wireless communication at low-power. The need to provide location-stamped communications to IoT applications for meaningful interpretation of physical measurements from IoT devices has increased demand to incorporate location estimation capabilities into LoRaWAN networks. Fingerprint-based localization methods are increasingly becoming popular in LoRaWAN networks because of their relatively high accuracy compared to range-based localization methods. This work proposes hybrid convolutional neural networks (CNNs)-transformer fingerprinting method to localize a node in a LoRaWAN network. CNNs are adopted to complement the strengths of the Transformer by adding the ability to capture local features from input data and consequently allow the Transformer, through the attention mechanism, to effectively learn global dependencies from the input data. Specifically, the proposed method works by first learning the local location features from the input data using the CNNs and passing the resulting information to the transformer encoder to learn global features from the input data. The output of the transformer encoder is then concatenated with information learned at the local level and then passed through the regressor for the final location estimation. With a localization performance of 290.71 m mean error achieved, the proposed method outperformed similar state-of-the-art works in the literature evaluated on the same publicly available LoRaWAN dataset.

Time-Allocation Adaptive Data Rate: An Innovative Time-Managed Algorithm for Enhanced Long-Range Wide-Area Network Performance

Currently, a variety of Low-Power Wide-Area Network (LPWAN) technologies offer diverse solutions for long-distance communication. Among these, Long-Range Wide-Area Network (LoRaWAN) has garnered considerable attention for its widespread applications in the Internet of Things (IoT). Nevertheless, LoRaWAN still faces the challenge of channel collisions when managing dense node communications, a significant bottleneck to its performance. Addressing this issue, this study has developed a novel “time allocation adaptive Data Rate” (TA-ADR) algorithm for network servers. This algorithm dynamically adjusts the spreading factor (SF) and transmission power (TP) of LoRa (Long Range) nodes and intelligently schedules transmission times, effectively reducing the risk of data collisions on the same frequency channel and significantly enhancing data transmission efficiency. Simulations in a dense LoRaWAN network environment, encompassing 1000 nodes within a 480 m × 480 m range, demonstrate that compared to the ADR+ algorithm, our proposed algorithm achieves substantial improvements of approximately 30.35% in data transmission rate, 24.57% in energy consumption, and 31.25% in average network throughput.

Extend low-power LoRaWAN coverage device for IoT applications

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

Flood Monitoring and Alerting System

Abstract: Floods pose significant threats to communities worldwide, necessitating the development of efficient monitoring and alerting systems. This abstract presents an IoT-based Flood Monitoring and Alerting System that leverages the power of LoRaWAN (Long Range Wide Area Network) technology to provide real-time flood detection, monitoring, and timely alerts. The system consists of strategically placed water level sensors that use ultrasonic or pressure-based technology to accurately measure water levels. The sensor data is wirelessly transmitted to a central gateway through the LoRaWAN network, known for its long-range and low-power capabilities. A cloud-based platform processes and analyzes the collected data, identifying abnormal water level patterns and potential flood conditions. In case of a flood, the system triggers alerts via mobile notifications, email, and SMS to relevant authorities and stakeholders. Users can access real-time flood information, historical data, and visualizations through a user-friendly web or mobile interface. The system offers cost-effectiveness, extended range, low power consumption, early flood detection, timely response, data-driven decision-making, and has a brighter potential for future enhancements

Unveiling LoRa's Oceanic Reach: Assessing the Coverage of the Azores LoRaWAN Network from an Island.

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.

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning.

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.

An optimization of multiple gateway location selection in long range wide area network networks

The adoption of smart agricultural technology in rural areas is still limited in terms of network infrastructure supported. As a result, farmers continue to practice traditional farming that mainly focuses on human labor and requires experience in planning the production of agricultural products in unstable weather conditions, which makes the farmers highly risky. Currently, long range (LoRa) technology is a smart agriculture support tool that will enable the Internet of Things devices to a large number of end nodes distributed over a wide geographical area. They could access cloud computing from a long distance, kilometers, for processing via long range wide area network (LoRaWAN) communication protocol. When choosing a multiple gateway location for LoRaWAN networks, big networks must consider the spatial distribution of clients, radio signal propagation, and the cap on the number of devices served access. In this study, a mathematical model is developed to optimize coverage. The LINGO modeling program, an exact software method, was used to test the model. The findings indicated that the best six gateways at the optimal LoRaWAN gateway location. The gateways can provide signal coverage for all end nodes and can manage the capacity of the LoRaWAN gateway to support the proper number of end nodes.

Dynamic multi-frame multi-spreading factor scheduling algorithm for LoRaWAN

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.

Enhancing Cyber Security of LoRaWAN Gateways under Adversarial Attacks.

The Internet of Things (IoT) has disrupted the IT landscape drastically, and Long Range Wide Area Network (LoRaWAN) is one specification that enables these IoT devices to have access to the Internet. Former security analyses have suggested that the gateways in LoRaWAN in their current state are susceptible to a wide variety of malicious attacks, which can be notoriously difficult to mitigate since gateways are seen as obedient relays by design. These attacks, if not addressed, can cause malfunctions and loss of efficiency in the network traffic. As a solution to this unique problem, this paper presents a novel certificate authentication technique that enhances the cyber security of gateways in the LoRaWAN network. The proposed technique considers a public key infrastructure (PKI) solution that considers a two-tier certificate authority (CA) setup, such as a root-CA and intermediate-CA. This solution is promising, as the simulation results validate that about 66.67% of the packets that are arriving from an illegitimate gateway (GW) are discarded in our implemented secure and reliable solution.

A Multiple End-Devices Authentication Scheme for LoRaWAN

With the advancement of the Internet of Things, the LoRa Alliance produced the Long-Range Wide-Area Network (LoRaWAN) Specification, allowing end-devices to transit through a gateway and join the LoRa network after completing a join procedure. When an end-device joins the LoRaWAN network, it must send a join request message to the network server and wait for the network server to verify such request under the current LoRaWAN join protocol. However, as the number of end-devices grows exponentially, network server verification messages will grow linearly with the number of end-devices. This paper proposes an authentication system for multiple end-devices that complies with the LoRa Alliance’s specifications and decreases the joining latency imposed by the network server verifying messages. The proposed authentication system is formally secure against the server and end-device impersonation. In addition, we assess the authentication overhead and compare it to the standard approach.

Re-Learning EXP3 Multi-Armed Bandit Algorithm for Enhancing the Massive IoT-LoRaWAN Network Performance.

Long-Range Wide Area Network (LoRaWAN) is an open-source protocol for the standard Internet of Things (IoT) Low Power Wide Area Network (LPWAN). This work’s focal point is the LoRa Multi-Armed Bandit decentralized decision-making solution. The contribution of this paper is to study the effect of the re-learning EXP3 Multi-Armed Bandit (MAB) algorithm with previous experts’ advice on the LoRaWAN network performance. LoRa smart node has a self-managed EXP3 algorithm for choosing and updating the transmission parameters based on its observation. The best parameter choice needs previously associated distribution advice (expert) before updating different choices for confidence. The paper proposes a new approach to study the effects of combined expert distribution for each transmission parameter on the LoRaWAN network performance. The successful transmission of the packet with optimized power consumption is the pivot of this paper. The validation of the simulation result has proven that combined expert distribution improves LoRaWAN network’s performance in terms of data throughput and power consumption.

Data Reliability and Sensors Lifetime in Bridge Health Monitoring using LoRaWAN-Zigbee

The Wireless Sensor Network (WSN) is regarded as the fastest expanding technological trend in recent years due its application in a variety of sectors. In the monitoring region, several sensor nodes with various sensing capabilities are installed to gather appropriate data and communicate it to the gateway. The proposed system of the heterogeneous WSN employing LoRaWAN-Zigbee based hybrid communication is explored in this research study. To communicate in a network, two Long–Range Wide Area Network (LoRaWAN) sensor clusters and two Zigbee sensor clusters are employed, together with two Zigbee and LoRaWAN converters. The suggested Golden eagle shepherd optimization (GESO) method then forms Zigbee as well as LoRaWAN networking clusters. Furthermore, depending on energy usage and data packet size, the fitness of each sensor node is assessed using the Dynamic Intelligent Reasoning Based Neural (DIRN) approach. MATLAB software is used to implement and execute this study. When the Zigbee network’s transmission distance is 650 m and the LoRaWAN network’s transmission range is 3.5 km, the system can function with a packet loss rate of less than 0.04 percent. This study shows significant gains in the performance of the system when compared to traditional approaches based on digital findings obtained on software solutions.

A Secure and LoRaWAN Compatible User Authentication Protocol for Critical Applications in the IoT Environment

Security and privacy are two main concerns in the critical applications in the Internet of Things environments. Long Range Wide Area Network (LoRaWAN) is a protocol, which effectively allows long-range communication for battery-constrained end devices in IoT environments, and it is accepted and used by individuals and industry. In order to facilitate the use of this technology and gain the trust of users, it is necessary to assure security and privacy for the information collected by end devices. The user authentication and key establishment protocols are very paramount in this regard. Although there are some authentication schemes in the literature, they could not be applied in the LoRaWAN networks. Thus, in this article, we introduce a new secure user authenticated key establishment scheme for LoRaWAN networks. The proposed scheme provides mutual authentication among participants, and it allows a user and an end device to establish a secure session key between themselves without trusting the network server unconditionally and completely. In order to prove that the proposed scheme is secure, we constructed formal proof employing the real-or-random model. Besides, we employed Proverif and automated validation of internet security protocols and applications tool to confirm that it satisfies authentication and security characteristics. Further, we show that our proposal is efficient with respect to computation, communication and storage costs in end devices. Eventually, we exhibit a practical demonstration of our proposal applying the NS2 simulator.

Toward a Live BBU Container Migration in Wireless Networks

Cloud Radio Access Networks (Cloud-RANs) have recently emerged as a promising architecture to meet the increasing demands and expectations of future wireless networks. Such an architecture can enable dynamic and flexible network operations to address significant challenges, such as higher mobile traffic volumes and increasing network operation costs. However, the implementation of compute-intensive signal processing Network Functions (NFs) on the General Purpose Processors (General Purpose Processor) that are typically found in data centers could lead to performance complications, such as in the case of overloaded servers. There is therefore a need for methods that ensure the availability and continuity of critical wireless network functionality in such circumstances. Motivated by the goal of providing highly available and fault-tolerant functionality in Cloud-RAN-based networks, this paper proposes the design, specification, and implementation of live migration of containerized Baseband Units (BBUs) in two wireless network settings, namely Long Range Wide Area Network (LoRaWAN) and Long Term Evolution (LTE) networks. Driven by the requirements and critical challenges of live migration, the approach shows that in the case of LoRaWAN networks, the migration of BBUs is currently possible with relatively low downtimes to support network continuity. The analysis and comparison of the performance of functional splits and cell configurations in both networks were performed in terms of fronthaul throughput requirements. The results obtained from such an analysis can be used by both service providers and network operators in the deployment and optimization of Cloud-RANs services, in order to ensure network reliability and continuity in cloud environments.

On the Use of LoRaWAN for Mobile Internet of Things: The Impact of Mobility

A long-range wide-area network (LoRaWAN) targets both mobile and static Internet of Things (IoT) applications; it is suited to IoT applications, which require a large coverage area while consuming less power at a low data rate; it provides a solution for transferring data between IoT devices with a minimum cost in terms of power, at the expense of higher latency. LoRaWAN was designed for static low-power long-range networks. However, several IoT solution applications involve the use of mobility. Therefore, this study investigates the usage of LoRaWAN in the field of mobile Internet of Things applications such as bike rentals, fleet monitoring, and wildlife and animal tracking applications. Using the OMNeT++ simulator, two different well-known mobility models are used to investigate the influence of mobility on the performance of mobile LoRaWAN. The results show that intense LoRaWAN networks can operate under a high velocity and varying traffic load. It can be observed that the random waypoint model combination yields a better performance, but at the cost of higher collisions and energy consumption. As a consequence, the results suggest the reconsideration of mobile IoT solutions over LoRaWAN.

Improving the Convergence Period of Adaptive Data Rate in a Long Range Wide Area Network for the Internet of Things Devices

A Long-Range Wide Area Network (LoRaWAN) is one of the most efficient technologies and is widely adopted for the Internet of Things (IoT) applications. The IoT consists of massive End Devices (EDs) deployed over large geographical areas, forming a large environment. LoRaWAN uses an Adaptive Data Rate (ADR), targeting static EDs. However, the ADR is affected when the channel conditions between ED and Gateway (GW) are unstable due to shadowing, fading, and mobility. Such a condition causes massive packet loss, which increases the convergence time of the ADR. Therefore, we address the convergence time issue and propose a novel ADR at the network side to lower packet losses. The proposed ADR is evaluated through extensive simulation. The results show an enhanced convergence time compared to the state-of-the-art ADR method by reducing the packet losses and retransmission under dynamic mobile LoRaWAN network.

Low-Cost Implementation of Reactive Jammer on LoRaWAN Network

The Low-Power Wide-Area Network (LPWA) has already started to gain a notorious adoption in the Internet of Things (IoT) landscape due to its enormous potential. It is already employed in a wide variety of scenarios involving parking lot occupancy, package delivery, smart irrigation, smart lightning, fire detection, etc. If messages from LPWA devices can be manipulated or blocked, this will violate the integrity of the collected information and lead to unobserved events (e.g., fire, leakage). This paper explores the possibility that violates message integrity by applying a reactive jamming technique that disrupts a Long Range Wide Area Network (LoRaWAN) network. As shown in this paper, using low-cost commodity hardware based on Arduino platform, an attacker can easily mount such an attack that would result in completely shutting down the entire LoRaWAN network with high probability. Several countermeasures are introduced to reduce the possibility of jamming attacks.

Exploratory approach for network behavior clustering in LoRaWAN

The interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters’ contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach.

LoRaWAN Energy Optimization with Security Consideration

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

Investigation of the Performance of TDoA-Based Localization Over LoRaWAN in Theory and Practice.

The paper deals with the localization in a low-power wide-area-network (LPWAN) operating long-range wide-area-network (LoRaWAN) technology. The LoRaWAN is, today, one of the most widely used connectivity-enabling technologies for the battery-powered smart devices employed in a broad range of applications. Many of these applications either require or can benefit from the availability of geolocation information. The use of global positioning system (GPS) technology is restrained by the bad propagation of the signal when the device is hidden indoors, and by energy consumption such a receiver would require. Therefore, this paper focuses on an alternative solution implying the use of the information readily available in the LoRaWAN network and application of the time difference of arrival (TDoA) method for the passive geolocation of end-devices in the network. First, the limits of geolocation services in networks that use narrow-band communication channels are discussed, as well as the relevant challenges faced by the TDoA approach. Then, we select five classic TDoA algorithms and evaluate their performance using simulation. Based on these results, we select the two providing the best accuracy (i.e., Chan’s and Foy’s). These algorithms were tested by the field measurements, using the specially designed low-cost gateways and test devices to estimate their real-life performance.