Long Range Wide Area Network Research Articles

Prototype of an Emergency Response System Using IoT in a Fog Computing Environment

Currently, the internet of things (IoT) is a technology entering various areas of society, such as transportation, agriculture, homes, smart buildings, power grids, etc. The internet of things has a wide variety of devices connected to the network, which can saturate the central links to cloud computing servers. IoT applications that are sensitive to response time are affected by the distance that data is sent to be processed for actions and results. This work aims to create a prototype application focused on emergency vehicles through a fog computing infrastructure. This technology makes it possible to reduce response times and send only the necessary data to cloud computing. The emergency vehicle contains a wireless device that sends periodic alert messages, known as an in-vehicle beacon. Beacon messages can be used to enable green traffic lights toward the destination. The prototype contains fog computing nodes interconnected as close to the vehicle as using the low-power whole area network protocol called a long-range wide area network. In the same way, fog computing nodes run a graphical user interface (GUI) application to manage the nodes. In addition, a comparison is made between fog computing and cloud computing, considering the response time of these technologies.

Traffic Clearance for Ambulance during Pandemic Situation and Road Accidents using LORAWAN Network

Intelligent Transportation System (ITS) plays an important role in handling pandemic situation and disaster management. Due to rapid urbanization, there is a requirement for implementing an effective traffic control system not only to avoid heavy congestion but also to make a better solution for ambulance clearance which would help to save the human life. The proposed work intends to implement an effective traffic control system using Long-Range Wide Area Network (LoRaWAN) that provides seamless traffic clearance for ambulances, so that they reach the hospitals without any delay. Cupcarbon, a Wireless Sensor Network (WSN) simulator, is used to evaluate the performance of the proposed work. The simulation involves a case study considering an accident zone in Coimbatore city and the performance of the proposed system is compared with that of existing systems. The simulation results prove that LoRaWAN can be used to effectively control the traffic lights with a wider coverage range, as compared to existing systems.

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.

Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning.

This paper aims at proposing an augmented sensing method for estimating volumetric water content (VWC) in soil for Internet of Underground Things (IoUT) applications. The system exploits an IoUT sensor node embedding a low-cost, low-precision soil moisture sensor and a long-range wide-area network (LoRaWAN) transceiver sending relative measurements within LoRaWAN packets. The VWC estimation is achieved by means of machine learning (ML) algorithms combining the readings provided by the soil moisture sensor with the received signal strength indicator (RSSI) values measured at the LoRaWAN gateway side during broadcasting. A dataset containing such measurements was especially collected in the laboratory by burying the IoUT sensor node within a plastic case filled with sand, while several VWCs were artificially created by progressively adding water. The adopted ML algorithms are trained and tested using three different techniques for estimating VWC. Firstly, the low-cost, low-precision soil moisture sensor is calibrated by resorting to an ML model exploiting only its raw readings to estimate VWC. Secondly, a virtual VWC sensor is shown, where no real sensor readings are used because only LoRaWAN RSSIs are exploited. Lastly, an augmented VWC sensing method relying on the combination of RSSIs and soil moisture sensor readings is presented. The findings of this paper demonstrate that the augmented sensor outperforms both the virtual sensor and the calibrated real soil moisture sensor. The latter provides a root mean square error (RMSE) of 3.33%, a virtual sensor of 8.67%, and an augmented sensor of 1.84%, which improves down to 1.53% if filtered in post-processing.

Real-time monitoring of early-age compressive strength of concrete using an IoT-enabled monitoring system: an investigative study

Determination of the early-age compressive strength of concrete is essential for quality assurance, safety, and economy of construction projects. Due to manual operation on construction site, conventional maturity meters are not efficient for live monitoring of the early-age concrete strength. Higher levels of automated and computerised improvements have been made possible by recent developments in wireless communications, sensor technologies, and data processing methods across the construction industry. For real-time monitoring of the early-stage concrete strength, the current study presents an innovative Internet of Things (IoT)-enabled system developed by concrete data sensors (CDS), an Australian-owned private business. The CDS sensor system (the system) communicates with temperature sensors via long-range wide-area network and is linked to a cloud-based platform for data storage. The suggested system’s effectiveness was assessed using three concrete mixtures and developed maturity relationships. It was observed that the predicted early-age compressive strength of the mixes matches well with the actual compressive strength and that the system can effectively automate the characterisation of maturity.

Fight Fire with Fire: Detecting Forest Fires with Embedded Machine Learning Models Dealing with Audio and Images on Low Power IoT Devices.

Forest fires are the main cause of desertification, and they have a disastrous impact on agricultural and forest ecosystems. Modern fire detection and warning systems rely on several techniques: satellite monitoring, sensor networks, image processing, data fusion, etc. Recently, Artificial Intelligence (AI) algorithms have been applied to fire recognition systems, enhancing their efficiency and reliability. However, these devices usually need constant data transmission along with a proper amount of computing power, entailing high costs and energy consumption. This paper presents the prototype of a Video Surveillance Unit (VSU) for recognising and signalling the presence of forest fires by exploiting two embedded Machine Learning (ML) algorithms running on a low power device. The ML models take audio samples and images as their respective inputs, allowing for timely fire detection. The main result is that while the performances of the two models are comparable when they work independently, their joint usage according to the proposed methodology provides a higher accuracy, precision, recall and F1 score (96.15%, 92.30%, 100.00%, and 96.00%, respectively). Eventually, each event is remotely signalled by making use of the Long Range Wide Area Network (LoRaWAN) protocol to ensure that the personnel in charge are able to operate promptly.

LoRaWAN CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin

One of the challenges of the Internet of Things (IoT) is to provide connectivity to devices around the globe. Long Range (LoRa) is one of the most practical technologies due to its low-power and long-range capabilities to be used by the Low Earth Orbit (LEO) CubeSat. This study aims to evaluate the performance of the Long Range Wide Area Network (LoRaWAN) in receiving uplink messages from ground sensor nodes at an altitude of 550 km with a maximum elevation angle of 0°. An Adaptive Data Rate (ADR) is applied for the dynamic signal reception with respect to receiving signal strength. In this study, the path loss is simulated using attenuation levels from 30–150 dB to ensure that the signal connectivity success rate is at a minimum elevation angle and to perform the link analysis under various conditions. The results show that the LoRaWAN signals are successfully received with an SNR of −21 at the 150 dB attenuation from the end nodes because of its spread-spectrum technique which allows the system to detect signals under the noise floor.

Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning

Low Power Wide Area Network (LPWAN) technologies have been exponentially growing because of the tremendous growth of the Internet of Things (IoT) devices across the globe. Several LPWAN technologies have been utilized by the researchers to address certain issues like increased number of collisions, retransmissions, delay, and energy consumption. However, Long Range Wide Area Network (LoRaWAN) is the most suitable and attractive technology in terms of delay optimization, low cost and efficient energy consumption. The main issue which arises in LoRaWAN is because of its high packet drop rate due to collision. The reason behind this packet drop rate is the MAC scheme known as Pure Aloha used by LoRaWAN for the transmission of the frames. Long Range (LoRa) End Devices (EDs) initiate communication with Pure Aloha that leads to a high number of retransmissions. These retransmissions further enhance the delay in LoRa networks. This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). By using ASA with GMM, the retransmissions are reduced which optimizes the delay in LoRaWAN. The results show that in our approach, Packet Collision Rate (PCR) is reduced by 39% as compared to conventional LoRaWAN. In addition, the Packet Success Ratio (PSR) is also increased by 39% as compared to the conventional LoRaWAN and Dynamic Priority Scheduling Technique (PST). Further, the delay is optimized by 91% and 79%. This research could be effective for the environments where the critical data of patients need to be sent with optimised retransmissions and minimum delay towards gateways.

Experimental Evaluation of Trilateration-Based Outdoor Localization with LoRaWAN

Long Range Wide Area Network (LoRaWAN) in the Internet of Things (IoT) domain has been the subject of interest for researchers. There is an increasing demand to localize these IoT devices using LoRaWAN due to the quickly growing number of IoT devices. LoRaWAN is well suited to support localization applications in IoTs due to its low power consumption and long range. Multiple approaches have been proposed to solve the localization problem using LoRaWAN. The Expected Signal Power (ESP) based trilateration algorithm has the significant potential for localization because ESP can identify the signal’s energy below the noise floor with no additional hardware requirements and ease of implementation. This research article offers the technical evaluation of the trilateration technique, its efficiency, and its limitations for the localization using LoRa ESP in a large outdoor populated campus environment. Additionally, experimental evaluations are conducted to determine the effects of frequency hopping, outlier removal, and increasing the number of gateways on localization accuracy. Results obtained from the experiment show the importance of calculating the path loss exponent for every frequency to circumvent the high localization error because of the frequency hopping, thus improving the localization performance without the need of using only a single frequency.

Environmental Temperature and Humidity Monitoring at Agricultural Farms using Internet of Things & DHT22-Sensor

Many monitoring systems have been designed and tested by researchers for monitoring environment temperature and humidity for different industrial applications and smart homes using DHT22 or DHT11 with Arduino Uno and wireless communication module. DHT22 is found to have better specifications in terms of performance and accuracy for measuring both temperature and humidity compared to DHT11. Arduino UNO is a microcontroller commonly used in scientific research. There are other more powerful microcontrollers available in the market. We chose ESP32 as its CPU is more powerful and can run upto 240 MHz, whereas Arduino UNO can run upto 16 MHz. In terms of connectivity, there is no Wi-Fi or Bluetooth functionality built in Arduino board, whereas ESP-32 has Wi-Fi and Bluetooth functions built in, which makes it more suitable for IoT projects. This paper aims at using Long Range Wide Area Network (LoRaWAN) Technology to monitor environmental temperature and humidity at the agricultural farm remotely, using DHT22 in conjunction with TTGO T-Beam as it has an ESP32 development board and also includes a GPRS module. ESP32 can be connected to the internet using Wi-Fi. The sensor connected to the T-Beam sends data to the Cloud through the internet. In this experiment, we used the Firebase platform to save data in the database. This information can also be received on the serial monitor as well as on smartphones and Web/App by the concerned personnel. The T-Beam module is a reliable module for data communication and covers up to 15Kms range. The LoRaWAN is used as a gateway to connect devices wirelessly to the internet and manages communication between end nodes. The buzzer is set to turn on for temperature ≥ 32 ℃ and humidity ≤ 60%. The system is designed for monitoring environmental temperature and humidity at agricultural farms it can also be used in other applications like smart homes, industries to control temperature/humidity, poultry farms and environment monitoring in specific locations/areas.

Design of an IoT-Based Monitoring System as a Part of Prevention of Thermal Events in Mining and Landfill Waste Disposal Sites: A Pilot Case Study

This case study deals with the design of a hybrid system for the prevention of thermal events in mining waste disposal sites and landfills. The overall design, real implementation, optimization, and experimental verification of the functionality of the entire system are described in detail. Both experimental platforms are built on the Internet of Things (long range wide area network (LoRaWAN) and Sigfox) basis and meet the conditions for autonomous long-term on-site monitoring. The data collected are periodically transmitted wirelessly to a database repository, which processes relevant parameters for the operators of dispatching workplaces. The study is focused on a combination of surface and depth measurement methods. The experimental results clearly confirm the functionality of the proposed solutions, which will enable timely interventions and elimination of underground and surface combustions. Thanks to centralized data collection, a unique database has also been created, which can be used for the implementation of prediction algorithms (based, for example, on machine learning or artificial intelligence).

A Comprehensive Study on LPWANs With a Focus on the Potential of LoRa/LoRaWAN Systems

The Internet of Things (IoT) ecosystem emerges rapidly and paves the way towards collecting huge amounts of data that can feed innovative information-based systems. The connectivity infrastructure that will support the IoT ecosystem encompasses deployments that define the so-called Low-Power Wide Area Networks (LPWANs). LPWANs are an attractive solution for IoT service provisioning, since they incorporate energy-efficient and cost-effective technologies. In this context, a comprehensive study is provided to assist in the currently increasing research and development interest on LPWANs. First, recent cloud-based and open-source approaches for data management are discussed, while the major key wireless technologies for network access are presented. Second, the potential of Long Range (LoRa) modulation and Long Range Wide Area Network (LoRaWAN) protocol, as key LPWAN wireless technologies in unlicensed spectrum bands, is further analyzed. The fundamental principles of these technologies are presented, and a thorough study on the relevant research activities is conducted.

Blockchain-Based Key Management and Authentication Scheme for IoT Networks With Chaotic Scrambling

Internet of Things (IoT) systems have the great advantage of connecting massive low energy-consumption devices for the information delivery via wireless channels. However, IoT devices suffer from malicious attacks or eavesdropping due to the broadcasting property of wireless channels. With the aim of enhancing the security performances, we first propose a chaotic scrambler, which utilizes the natural high security property of chaotic sequences being aperiodic and sensitive to initial values, to improve the security of signal transmissions. Then, we propose to utilize the traceable and tamper-proof blockchain to integrate the confidential chaotic key into the chain for user identify management and authentications. Subsequently, theoretical security, reliability and throughput capacities are analyzed, and simulation results are provided to validate the design and the theoretical analysis. Furthermore, we present the practical hardware implementation board for the proposed secure chaotic scrambling following the long range wide area network (LoRaWAN) specifications, which demonstrates that the security performances of IoT devices can be effectively enhanced.

A Duty Cycle-Based Gateway Selection Algorithm for LoRaWAN Downlink Communication

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.

An IIoT-Based Approach to the Integrated Management of Machinery in the Construction Industry

In recent years, considerable advances in connecting industrial equipment to the Internet allowed a higher level of operational efficiency, productivity, and automation. Nevertheless, the construction industry still presents challenging requirements, like long-distance wireless communication, interconnection of out-of- coverage areas, constant mobility of machinery, and support for legacy and proprietary systems. All these requirements pose different challenges when compared with traditional smart factory Industry 4.0 solutions. This paper discusses some of the current key questions regarding fleet management in the construction industry, identifies requirements for heavy-duty machinery, and proposes an Industrial Internet of Things (IIoT)-based solution for the integrated monitoring of such machinery. Also, it proposes and presents an open, innovative solution for the integrated management of non-standardized civil construction vehicle technologies. The developed prototype uses the J1939 protous and protocol to collect machinery status and Long-Range Wide Area Network (LoRaWAN) to communicate the monitored data. The solution was assessed at the construction site of the new port of Sines in Portugal. A detailed description of the proposed system is provided, along with information on trials and evaluation results regarding its performance. Additionally, the paper provides insights into open issues and challenges in this field and how our solution can contribute to overcoming them.

An Online Parsing Framework for Semistructured Streaming System Logs of Internet of Things Systems

This article presents a novel log abstraction framework based on neural open information extraction (OpenIE) and dynamic word embedding principles. Though various log parsing frameworks are proposed in the literature, the existing frameworks are modeled on predefined heuristics or auto-regressive methodologies that work well in offline scenarios. However, these frameworks are less suitable for dynamic self-adaptive systems, such as the Internet of Things (IoT), where the log outputs have diverse contextual variations and disparate time irregularities. Therefore, it is essential to move away from these traditional approaches and develop a systematic model that can effectively analyze log outputs in real-time and increase the system up-time of IoT networks so that they are almost always available. To address these needs, the proposed framework used OpenIE along with term frequency/inverse document frequency (TF/IDF) vectorization for constructing a set of relational triples (aka triple-sets). Additionally, a dynamic pretrained encoder–decoder architecture is utilized to imbibe the positional and contextualized information in its resultant outputs. The adopted methodology has enabled the proposed framework to extract richer word representations with dynamic contextualization of time-sensitive event logs to enhance further downstream activities, such as failure prediction and prognostic analysis of IoT networks. The proposed framework is evaluated on the system event log traces accumulated from a long range wide-area network (LoRaWAN) IoT gateway to proactively determine the probable causes of its various failure scenarios. Additionally, the study also provided a comparative analysis of its mathematical representations with that of the current state-of-the-art (SOTA) approaches to project the advantages and benefits of the proposed model, particularly from its data analytics standpoint.

Data Processing with Predictions in LoRaWAN

In this paper, the potential to reduce the energy consumption of end devices operating in a LoRaWAN (long-range wide-area network) is studied. An increasing number of IoT components communicating over wireless networks are powered by external sources. Designers of communication systems are concerned with extending the operating time of IoT, hence the need to look for effective methods to reduce power consumption. This article proposes two algorithms to reduce the energy consumption of end devices. The first algorithm is based on the use of a measured value prediction, and the second algorithm optimizes the antenna gain of the end device. Both algorithms have been implemented and tested. The test experiments for reducing energy consumption were conducted independently for the cases with the first algorithm and then for the second algorithm. The possibilities of reducing energy consumption were also investigated for the case when both algorithms work together. The proposed predictive algorithm reduced energy consumption the least. Better results in reducing energy consumption were guaranteed by the algorithm optimizing antenna power. The greatest gain was achieved using both algorithms simultaneously. Tests of the developed algorithms, in laboratory conditions and in conditions with a change in the distance between the end device and the LoRa gateway, confirmed the possibility of reducing energy consumption during the transmission of measurement data in a low-energy wireless LoRaWAN. Reducing electric energy consumption by even a few percent for a single device can result in significant savings on a global scale.

UAV‐based LoRaWAN flying gateway for the internet of flying things

SummaryThe Internet of Things (IoT) is one of the paradigms related to the evolution of telecommunication networks which is contributing to the evolution of numerous use cases, such as smart city and smart agriculture. However, the current communication infrastructure and wireless communication technologies are not always able to guarantee a proper service for these IoT scenarios. Smart solutions are needed to overcome current terrestrial network limitations offering a cost‐effective way to extend the current terrestrial network coverage. For example, temporary extensions “on‐request” of the terrestrial infrastructure may be a viable solution to allow collecting data generated by nodes outside the current network coverage. Flying objects can help achieve this goal. Various studies supported the use of unmanned aerial vehicles (UAVs) as intermediate nodes between IoT devices and the network. However, such solutions have not been exhaustively tested yet in real‐case scenarios. This paper proposes an efficient solution to collect data from multiple IoT sensors in rural and remote areas based on UAVs. It describes the implementation of the proposed UAV‐based Long RangeWide Area Network (LoRaWAN) flying gateway able to collect data directly from LoRaWAN sensors during its flight, keep them stored in an onboard memory, and forward them at the end of its flying path to a platform where the authorized users can access them. A prototype of the gateway has been developed to assess the proposed solution through both indoor and outdoor tests aiming to test its feasibility both in terms of communication performance and UAV‐required hardware resources.

LoRaWAN: Lost for Localization?

Nowadays, the flexible localization solution for various devices for workplace safety is one of the most demanding research questions. Notably, it is expected to provide an acceptable level of precision in different types of environments empowered by wearable technology and Internet-of-Things (IoT) devices. Existing leading localization technologies are adapted for certain conditions, for example, Wi-Fi, Bluetooth low energy (BLE), and ultra-wideband (UWB) are used for indoor areas and various global navigation satellite system (GNSS)-based ones for outdoors. This work focuses on investigating the long-range wide-area network (LoRaWAN) (868-MHz band) as a potential candidate to bridge this gap, being one of the most reliable and recognized communication technologies for the Industrial IoT (IIoT). In the past, the research community had a lot of critics with respect to the applicability of LoRaWAN for localization, while the vision is facing tremendous change over the past two years. The purpose of this work is to assess the feasibility of LoRaWAN as a localization solution for work safety applications in the industrial scenario from different angles. The work is based on two measurement campaigns conducted at the Brno University of Technology (BUT), Brno, Czech Republic, and the University Politechnica of Bucharest (UPB), Bucharest, Romania. The campaigns cover both indoor and outdoor scenarios and provide the practical limitations of the positioning in standalone and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -NN) powered localization systems. According to the results, LoRaWAN-based localization with relatively dense gateways (GWs) deployment allows for achieving a meter-level accuracy, which may be suitable for the localization of workers.

Implementation of a Secure LoRaWAN System for Industrial Internet of Things Integrated With IPFS and Blockchain

Low-power, low-cost, and long-range connectivity for the Industrial Internet of Things (IIoT) networks are the key stipulations, nowadays. However, implementing a cost-effective, flexible, and feasible system considering server and networking security is still an open challenge. In this article, a complete end-to-end long-range wide area network (LoRaWAN) system has been demonstrated by implementing blockchain-based secure distributed data management, which is applicable in various secure IIoT applications. Dynamic data collected by multiple LoRa sensors are encrypted in a LoRa server, and the encrypted content is automatically stored in the InterPlanetary file system (IPFS) to ensure data confidentiality, integrity, and availability. To achieve data consistency, the content IDs collected from the IPFS are stored in the quorum blockchain with consortium setup using a smart contract. The consortium network is maintained by the Raft consensus algorithm employing seven nodes. The design architecture of the hardware used for both LoRa transmitting node and gateway has been described in comprehensive manners. The performance of the LoRaWAN system is analyzed by the received signal strength indicator, the communications range, and packet loss rate metrics in both line-of-sight and nonline-of-sight test systems. The data management scheme is implemented in Python, and the performance is evaluated in terms of transaction time and block size.