Long Range Wide Area Network Research Articles

RSS based multistage statistical method for attack detection and localization in IoT networks

Accurate and reliable positioning of nodes is a must for location-based services (LBS) in the Internet of things (IoT) networks. The LBS are ubiquitous and an easy target for non-cryptographic attacks that traditional security methods cannot address. In this work, we detect the Received Signal Strength (RSS) based attacks that affect the localization of smart devices in the IoT networks and report the attack tolerance of popular IoT protocols. A two-tier ratio metric method and Residue Under Curve (RUC) metric method is utilized to detect the malicious node in the IoT protocols. We propose to use a novel Geometric, and Arithmetic Mean (GM–AM) ratio as a feature to detect the RSS attacks where GM follows strictly Schur-Concavity property and AM follows non-strict concavity property. We evaluate the performance of the proposed method on real-world IoT testbeds with Wireless Fidelity (Wi-Fi), Zigbee, Bluetooth Low Energy (BLE), and Long-Range Wide-Area Network (LoRaWAN) protocols using the RSS values of these opportunistic signals. Also, the effect of RSS attacks on the localization for different protocols is investigated, and we report the method that provides the least localization error under these attacks.

An Intelligent Real-Time Edge Processing Maintenance System for Industrial Manufacturing, Control, and Diagnostic

This paper presents an artificial intelligence (AI) based edge processing real-time maintenance system for the purposes of industrial manufacturing control and diagnostics. The system is evaluated in a soybean processing manufacturing facility to identify abnormalities and possible breakdown situations, prevent damage, reduce maintenance costs, and increase production productivity. The system can be used in any other manufacturing or chemical processing facility that make use of motors rotating equipment in different process phases. The system combines condition monitoring, fault detection, and diagnosis using machine learning (ML) and deep learning (DL) algorithms. These algorithms are used with data resulting from the continuous monitoring of relevant production equipment and motor parameters, such as temperature, vibration, sound/noise, and current/voltage. The condition monitoring integrates intelligent Industrial Internet of Things (IIoT) devices with multiple sensors combined with AI-based techniques and edge processing. This is done to identify the parameter modifications and distinctive patterns that occur before a failure and predict forthcoming failure modes before they arise. The data from production equipment/motors is collected wirelessly using different communication protocols - such as Bluetooth low energy (BLE), Long range wide area network (LoRaWAN), and Wi-Fi - and aggregated into an edge computing processing unit via several gateways. The AI-based algorithms are embedded in the processing unit at the edge, allowing the prediction and intelligent control of the production equipment/motor parameters. IIoT devices for environmental sensing, vibration, temperature monitoring, and sound/ultrasound detection are used with embedded signal processing that runs on an ARM Cortex-M4 microcontroller. These devices are connected through either wired or wireless protocols. The system described addresses the components necessary for implementing the predictive maintenance (PdM) strategy in soybean industrial processing manufacturing environments. Additionally, it includes new elements that broaden the possibilities for prescriptive maintenance (PsM) developments to be made. The type of ML or DL techniques and algorithms used in maintenance modeling is dictated by the application and available data. The approach presented combines multiple data sources that improve the accuracy of condition monitoring and prediction. DL methods further increase the accuracy and require interpretable and efficient methods as well as the availability of significant amounts of (labeled) data.

LoRa Device 의 이동성을 고려한 GRU 기반 적응형 데이터 전송 속도 제어 알고리즘

Long Range Wide Area Network (LoRaWAN), a representative technology of Low Power Wide Area Network (LPWAN), is a networking technology for the long-range Internet of Things. It features low power, a long distance, and low speed, and each device in LoRaWAN supports adaptive data rate (ADR) technology to save transmission power. For implementing ADR in LoRaWAN, a network server uses algorithms to reduce power consumption by minimizing the time LoRa Radio packets stay in the air and sends a medium access control (MAC) command frame to increase the data rate or adjust transmission power by referring to signal-to-noise ratio (SNR) values. However, because the existing ADR algorithm for the network server sets the appropriate data rate and transmission power based on the maximum SNR value of the recent 20 packets, it does not support mobile devices because the SNR values of the mobile devices vary over time depending on their speeds. This paper introduces an improved ADR, GRU-ADR, that infers the future SNR values using the deep-learning gated recurrent unit (GRU) model to set appropriate data rates and transmission power using the ADR function even in mobile devices. The simulation study based on the OMNeT++ simulator and the Framework for LoRa (FLoRa) shows that GRU-ADR outperforms the existing ADR in packet delivery ratio (PDR) and energy consumption.

Improving Weighted Multiple Linear Regression Algorithm for Radiolocation Estimation in LoRaWAN

In location based services, Weighted Multiple Linear Regression (WMLR) algorithm is used for radio device position estimation. Nevertheless, WMLR provides coarse location estimate, because weights apportioned to the received signal strength (RSS) for each hearable base station during matrix weight formation are not properly distributed. In an attempt to address the problem articulated above, an improved WMLR that enhanced the accuracy of radio device position estimate is proposed in this work. Min-Max scaling was used to determine the weight for each RSS values logged at different BS, as such forming a refined matrix weight. Public on-site outdoor Long Range Wide Area Network (LoRaWAN) RSS data set was used to assess the improved WMLR estimation algorithm on the basis of accuracy. The location accuracy of the proposed method is validated with the existing WMLR algorithm and Federal Communication Commission (FCC) maximum location error benchmark. Results show that the location accuracy of the improved approach outperformed that of the existing WMLR localization method.

Dense Indoor Sensor Networks: Towards passively sensing human presence with LoRaWAN

Sensors have become ubiquitous in buildings but are rarely connected to a network, and their potential to analyse the performance, use, and interaction with a building is not yet fully realised. In the coming years, we expect sensors in buildings to become part of the Internet of Things (IoT) and grow in numbers to form a Dense Indoor Sensor Network (DISN) that allows for unprecedented analysis of the performance, use, and interaction with buildings. Multiple technologies vie for leading this transformation. We explore Long Range Wide Area Network (LoRaWAN) as an alternative for creating indoor sensor networks that extends beyond its original long-distance communication purpose. For the present paper, we developed a DISN with 390 sensor nodes and four gateways and empirically evaluated its performance for two years. Our analysis of more than 86 million transmissions revealed that DISNs achieve a much lower distance coverage compared to estimations from previous research indicating that more gateways are required. In addition, the deployment of multiple gateways decreased the loss of transmissions due to environmental and network factors. Given the complexity of our system, we received few colliding concurrent messages, which demonstrates a gap between the projected requirements of LoRaWAN systems and the actual requirements of real-world applications given sufficient gateways. We also contribute to the modelling of transmissions with our comparison of attenuation models derived from multiple methodologies. Across all models, we find that robust coverage in an indoor environment can be maintained by placing a gateway every 30 m and every 5 floors. Finally, we also investigate the application of DISNs for the passive sensing and visualisation of human presence using a Digital Twin (DT) and a Fused Twins (FT) representation in Augmented Reality (AR). A passive sensing approach allows us to gather relevant data on human use of a building while still preserving privacy via the aggregation process. Immersive in situ visualisations in FT allow for new interactions and new forms of participation. We conclude that DISNs are already technologically feasible today and basing them on Low Power Wide Area Network (LPWAN) offers intriguing possibilities to reduce energy consumption, maintenance cost, and bandwidth use while also enabling new forms of human-building interaction.

Designing and Implementing a Microstrip Antenna on LoRa Frequency for Smart Meter Communication

The long-range wide area network (LoRaWAN) has been widely used for sensor networks, including smart meter (SM). The SM can report power consumption to the control center automatically via a communication link. An antenna is a key component to indicate an effective communication system. The microstrip antenna (MSA) works based on the unidirectional radiation pattern. The advantages of MSA are its light weight, small size, and ability to be easily etched on a print circuit board. These advantages are needed for the SM communication because SMs are installed on electric poles that have limited space. In this pa- per, we implement the MSA for SM communication on LoRaWAN technology that operates at a frequency of 920–925 MHz in Thailand. The proposed MSA is used to design a new path loss model for LoRaWAN in urban areas. Results show that our MSA is effectively close to commercial antennas in a 2.2 km range, with the packet delivery ratio reaching 52.93%. We analyze the results by using a mathematical equation that includes the free space model, the Okumura–Hata model, root mean squared error, and coefficient of determination.

AWS IoT Core and LoRaWAN Based Sensory Network system for Military Search and Rescue Missions

The Indian armed forces is the second largest Military force in the world comprising of 1.4 million+ personnel’s (Active force) and 1.1 million+ reserved personnel’s. Apart from their duty of safeguarding its borders, the Military personnel’s are also engaged in Search and Rescue operations (SAR) across the globe. The SAR is conducted both for Military personnel’s and Civilians. The search and rescue operations are carried on varied demographic regions ranging from Snow areas ( -500 C) to Deserts ( +550 C), also SAR is conducted during natural calamities like Earthquake, Floods, Tsunami, Avalanche, Landslides etc. But the main challenge for any Military personnel in SAR is to identify whom to rescue first. By saying this we mean that if the personnel has the knowledge about the Vitals of the individual he/she is rescuing then he/she will be able to assess the gravity of the situation through those vitals. Vitals like SP02, Blood Pressure, Pulse rate can predict the survivability factor of the individual. So it means that if out of three individuals the Vitals of 1st person are serious and Vitals of the 2nd Person are Normal and the Vitals of the 3rd Person are Null, then it can be predicted that the 1st person should be rescued first and the 2nd person be rescued Second and the 3rd person whose Vitals are Null can be presumed dead and shall be rescued at the last. Now the main challenge comes, and that is how to gather those Vitals. The answer to this problem has been suggested through the experiments conducted in this paper. This paper suggests for a wireless sensor wrist band supporting LoRaWAN protocol ( Long Range Wide Area Network). The LoRaWAN uses LoRa Radio to transmit in low power at long-range wide area network. The aforementioned wristband shall transmit data to the AWS Cloud using AWS IoT Core. The AWS Cloud will aggregate the data received from all the people wearing the wrist band. From the aforesaid data those persons can be easily identified whose Vitals are critical. This will prove to be a Life Saver for Armed forces.

A smart water grid network for water supply management systems

This paper proposes a smart water grids network (SWGN) architecture that combines the advantages of fog computing, internet of things (IoT), long range wide area network (LoRaWAN), and Software-defined networking (SDN). The main aims of the SWG architecture are to optimize data routing and monitor water supply and quality in real-time. SWGN handles a vast amount of data that is collected by IoT devices from different points related to water supply and quality. The data is processed in a distributed way by a number of fog servers that are located at the edge of the network. The fog controllers are deployed at the fog layer in order to take action locally for frequent events. The cloud layer has a cloud controller to take actions globally for infrequent events. The LoRaWAN provides communication technology that allows devices to operate regularly. The SDN technology decouples network traffic to control data routing decisions efficiently. A primitive evaluation under the Mininet emulator, focusing on SDN, shows the feasibility and efficiency of the architecture.

Transfer Reinforcement Learning Aided Distributed Network Slicing Optimization in Industrial IoT

With the growth of the number of Internet of Things (IoT) devices and the emergence of new applications, satisfying distinct QoS in the same physical network becomes more challenging. Recently, with the advance of network functions virtualization and software-defined networking (SDN) technologies, the network slicing technique has emerged as a promising solution. It can divide a physical network into multiple virtual networks, therefore providing different network services. In this article, to meet distinct QoS in industrial IoT, we design a network slicing architecture over the SDN-based long-range wide area network. The SDN controller can dynamically split the network into multiple virtual networks according to different business requirements. On this basis, we proposed a deep deterministic policy gradient (DDPG) based slice optimization algorithm. It enables LoRa gateways to intelligently configure slice parameters (e.g., transmission power and spreading factor) to improve the slice performance in terms of QoS, energy efficiency, and reliability. In addition, to accelerate the training process across multiple LoRa gateways, we leverage the transfer learning framework and design a transfer learning-based multiagent DDPG algorithm.

Performance Evaluation of Communication Systems Used for Internet of Things in Agriculture

The rapid development of Internet of Things (IoT) technology has provided ample opportunity for the implementation of intelligent agricultural production. Such technology can be used to connect various types of agricultural devices, which can collect and send data to servers for analysis. These tools can help farmers optimize the production of their crops. However, one of the main problems that arises in agricultural areas is a lack of connectivity or poor connection quality. For these reasons, in this paper, we present a method that can be used for the performance evaluation of communication systems used in IoT for agriculture, considering metrics such as the packet delivery ratio, energy consumption, and packet collisions. To achieve this aim, we carry out an analysis of the main Low-Power Wide-Area Networks (LPWAN) protocols and their applicability, from which we conclude that those most suited to this context are Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN). After that, we analyze various simulation tools and select Omnet++ together with the Framework for LoRa (FLoRa) library as the best option. In the first stage of the simulations, the performances of LoRa and LoRaWAN are evaluated by comparing the average propagation under ideal conditions against moderate propagation losses, emulating a rural environment in the coastal region of Ecuador. In the second phase, metrics such as the package delivery ratio and energy consumption are evaluated by simulating communication between an increasing number of nodes and one or two gateways. The results show that using two gateways with the Adaptive Data Rate technique can actively increase the delivery ratio of the network while consuming the same amount of energy per node. Finally, a comparison is made between the results of the simulation scenario considered in this project and those of other research works, allowing for the validation of our analytical and simulation results.

An Adaptive LoRaWAN MAC Protocol for Event Detection Applications

In recent years, the Internet of Things (IoT) is growing rapidly and gaining ground in a variety of fields. Such fields are environmental disasters, such as forest fires, that are becoming more common because of the environmental crisis and there is a need to properly manage them. Therefore, utilizing IoT for event detection and monitoring is an effective solution. A technique for monitoring such events over a large area is proposed in this research. This work makes use of the Long-Range Wide Area Network (LoRaWAN) protocol, which is capable to connect low-power devices distributed on large geographical areas. A learning-automata-based hybrid MAC model is suggested to reduce the transmission delay, when a small part of the network produces event packets stemming from an event occurrence that is related to environmental monitoring applications, such as events related to forest fires. The proposed hybrid MAC is evaluated via simulation, which indicates that it achieves significantly higher performance in terms of packet delay, when compared to traditional LoRaWAN schemes.

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.

LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring

This study proposes a smart long-range (LoRa) sensing node to timely collect the air quality information and update it on the cloud. The developed long-range wide area network (LoRaWAN)-based Internet of Things (IoT) air quality monitoring system (AQMS), hereafter called LoRaWAN-IoT-AQMS, was deployed in an outdoor environment to validate its reliability and effectiveness. The system is composed of multiple sensors (NO2, SO2, CO2, CO, PM2.5, temperature, and humidity), Arduino microcontroller, LoRa shield, LoRaWAN gateway, and The Thing Network (TTN) IoT platform. The LoRaWAN-IoT-AQMS is a standalone system powered continuously by a rechargeable battery with a photovoltaic solar panel via a solar charger shield for sustainable operation. Our system simultaneously gathers the considered air quality information by using the smart sensing unit. Then, the system transmits the information through the gateway to the TTN platform, which is integrated with the ThingSpeak IoT server. This action updates the collected data and displays these data on a developed Web-based dashboard and a Graphical User Interface (GUI) that uses the Virtuino mobile application. Thus, the displayed information can be easily accessed by users via their smartphones. The results obtained by the developed LoRaWAN-IoT-AQMS are validated by comparing them with experimental results based on the high-technology Aeroqual air quality monitoring devices. Our system can reliably monitor various air quality indicators and efficiently transmit the information in real time over the Internet.

Remote Tracking of UAV Swarms via 3D Mobility Models and LoRaWAN Communications

Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design an ad hoc tracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a 3D environment. We analyze the trade-off in setting the main parameters of the communication system and Adaptive Data Rate (ADR) scheme, showing how our tracking system can handle large swarms of drones at distances up to 4 km. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a swarm of UAVs, significantly outperforming baseline tracking approaches.

Underwater to above water LoRaWAN networking: Theoretical analysis and field tests

The aim of this paper is to demonstrate the exploitability of Long Range Wide Area Network (LoRaWAN) networking to set up a wireless transmission channel allowing the real time data collection from underwater depths up to more than 1 m. To this aim, a theoretical analysis is carried out to determine the actual feasibility of this link taking into account the technical features of the LoRa channel. Following this analysis, field tests in a swimming pool have been performed analyzing the transmission performance in terms of Received Signal Strength Indicator (RSSI) and Signal to Noise Ratio (SNR) for different transmission parameters and at increasing depths. Moreover, two test campaigns varying the antenna’s sensor node and the packet payload length have been performed. Experimental results have seen a good corroboration of the theoretical analysis with efficient underwater LoRaWAN data transmission at depths up to 110 ±5 cm, considering 14 dBm transmitted power.

LoRaWAN based internet of things (IoT) system for precision irrigation in plasticulture fresh-market tomato

Precision irrigation with sensors has proven to be effective for water saving in crop production. An Internet of Things (IoT) system is necessary for monitoring real-time data from sensors and automating irrigation systems. Long-range wide-area network (LoRaWAN), a type of low-power wide-area network (LPWAN), is a low-cost and easily implemented IoT system which can be used for precision crop irrigation. In this study, a LoRaWAN based IoT system was developed and evaluated as a precision irrigation tool on fresh-market tomato production in a plasticulture system. Four irrigation scheduling treatments were designed and tested, including irrigation based on crop evapotranspiration (ET), soil matric potential sensors (Watermark 200SS-5) at -60 kPa (MP60) or -40 kPa (MP40), and GesCoN fertigation decision support system (DSS). The treatments were arranged based on a randomized complete block design with four replicates. System feasibility, irrigation water use efficiency (iWUE), and crop yield were evaluated. Throughout the season, the overall water use efficiencies were 22.2, 26.5, 27.9, and 28.4 kg·m−3 for ET, MP60, MP40, and GesCoN, respectively. The results indicated that treatment MP60 and GesCoN had 15.2 and 22.1% higher marketable fruit yield than ET, while, MP40 had 12.5% lower marketable yield compared to ET. Overall, the LoRaWAN-based IoT system performed well in terms of power consumption, communication, sensor reading, and valve control. These results suggested that the IoT system can be implemented for precision and automatic irrigation operations for vegetable and other horticultural crops with enhancing crop's water use efficiency and sustainability.

Flexible Index Mapping Scheme for Packet-Level Index Modulation

Long range wide area network (LoRaWAN) attracts attention due to its ability to realize massive machine-type communication (MTC); however, the throughput is low due to its narrow-band transmission and chirp spread spectrum. Packet-level index modulation (PLIM) increases the throughput by utilizing a data packet’s frequency channel and transmission timing as an information-bearing index. This letter proposes a flexible index mapping scheme to fully utilize the available frequency channels and transmission timings to increase the number of bits transmitted by an index and avoid packet collision. Numerical results show that the proposed scheme improves the throughput and significantly reduces the required memory size of end nodes.

Design of IoT Monitoring System Based on LoRaWAN Architecture for Smart Green House

The application of smart agriculture as an applied innovation in overcoming uncertainty over natural conditions and supporting national food security. The utilization of smart agriculture with the Internet of Things (IoT) approach can spur increased added value, competitiveness, and commercial benefits for national food products. In this paper, we proposed a low-cost, modular, and Long-Range Wide-Area Network (LoRaWAN) monitor and collect the environmental data (air temperature and humidity) related to the growth in the smart greenhouse over a while. The proposed architecture has been evaluated on a real farm on a lab scale with RSSI -118 and SNR for 2% for 1000m. A web-based visualization tool for the collected data is also presented to validate the LoRaWAN architecture.

Insights into the Issue of Deploying a Private LoRaWAN.

The last decade has transformed wireless access technologies and crystallized a new direction for the internet of things (IoT). The modern low-power wide-area network (LPWAN) technologies have been introduced to deliver connectivity for billions of devices while keeping the costs and consumption low, and the range of communication high. While the 5G (fifth generation mobile network) LPWAN-like radio technologies, namely NB-IoT (narrowband internet of things) and LTE-M (long-term evolution machine type communication) are emerging, the long-range wide-area network (LoRaWAN) remains extremely popular. One unique feature of this technology, which distinguishes it from the competitors, is the possibility of supporting both public and private network deployments. In this paper we focus on this aspect and deliver original results comparing the performance of the private and public LoRAWAN deployment options; these results should help understand the LoRaWAN technology and give a clear overview of the advantages and disadvantages of the private versus public approaches. Notably, we carry the comparison along the three dimensions: the communication performance, the security, and the cost analysis. The presented results illustratively demonstrate the differences of the two deployment approaches, and thus can support selection of the most efficient deployment option for a target application.

A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials

This article proposes a system based on a long-distance communications system with low economic and energy costs that allows connectivity to be carried out independently from the existing cellular coverage in the area. In addition, it describes the design, development, implementation and analysis of an Internet of Things (IoT) architecture based on Long-Range Wide-Area Network (LoRaWAN). Moreover, the system has been deployed as a prototype, and the behavior and scope of the system have been analyzed in various real environments: urban, rural and natural. The results obtained from the analysis show that the system is suitable for working in areas without coverage such as mountains.