LoRa Technology Research Articles

Synchronous LoRa sensor nodes for modal identification in footbridge vibration monitoring

AbstractThis article aims at presenting a preliminary investigation of using long-range and low-cost LoRa (Long Range) technology and two synchronous LoRa sensor nodes for cost-effective footbridge structural health monitoring (SHM). Two sensor nodes with LoRa modules and accelerometers were employed for vibration monitoring and a new attempt was made to use synchronous LoRa nodes for modal identification. In this article, a modal identification method based on a lightweight synchronization concept was proposed. This method is able to identify the fundamental mode of vibrating beam structures. Meanwhile, maximum accelerations can also be tracked periodically from the simultaneously recorded acceleration data by the synchronous LoRa nodes. Specifically, synchronization was achieved through the wireless peer-to-peer (P2P) communication between the two LoRa nodes, with the aim of initializing simultaneous acceleration recordings. The fundamental frequency and the phase information derived from the on-board calculation of the synchronized nodes can then be used to effectively identify the vertical bending mode and torsion mode. A series of laboratory tests were also conducted on a beam structure for the purpose of validation. The test results showed that the fundamental mode of the vibrating beam can be obtained rapidly and accurately using the synchronized LoRa nodes, with an average synchronization accuracy of 4.45 ms. The maximum acceleration data recorded by the LoRa nodes also showed high accuracy when compared with the raw acceleration data collected from a commercial Bluetooth accelerometer node. The fundamental frequencies obtained from both types of nodes also compare reasonably. The proposed modal identification method using two synchronous LoRa sensor nodes provides a basis for the development of a low-cost footbridge SHM system with the integration of IoT techniques. An attempt has been made to perform a preliminary field test on a cable-stayed footbridge, where the fundamental frequency and the mode shape type of the footbridge were successfully identified and its serviceability condition was also found to satisfy the code requirements.

DDS-P: Stochastic models based performance of IoT disaster detection systems across multiple geographic areas

Effective management of catastrophic events in high-risk zones necessitates a holistic technological approach to protect ecosystems, biodiversity, and native populations. Limitations in sensor range and connectivity hamper real-time data gathering in secluded areas, while financial and technical hurdles hinder the creation of cost-effective, automated systems. This study presents stochastic models, the LoRaW protocol, and cloud technology to enhance sensor deployment simulations. Wireless Sensor Networks and LoRa technology are crucial for extensive monitoring and communication infrastructures. Stochastic Petri Net models optimize system components by assessing crucial performance indicators, such as average response time and system utilization, thus improving disaster response and supporting research hypotheses.

Experimental analysis of low-duty cycle campus deployed IoT network using LoRa technology

This study emphases on the implementation and evaluation of a LoRa network in indoor environments through an experimental setup at Amity University, Greater Noida (AUGN) campus. Aimed at scrutinizing the link-level performance and reliability of LoRa technology, extensive measurements have been conducted with prototype device integrating the SX1278 LoRa radio module at 433 MHz. The performance has been analysed in terms of Signal-to-Noise Ratio (SNR), Received Signal Strength Indicator (RSSI), and Packet Reception Rate (PRR) across various line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. The results reveal that network performance is highly sensitive to parameter configurations, such as spreading factor (SF) and coding rate (CR). In LoS environments, SF = 9 and CR = 4/5 provided an RSSI of −89.12 dBm and an SNR of 9.91 dB, while highly obstructive areas saw optimal performance with SF = 7 and CR = 4/7. The study also identified configurations ensuring 100 % packet delivery accuracy and maximum throughput. This work stands out due to its thorough examination of LoRa for indoor IoT applications, notably addressing duty cycle restrictions, a topic often overlooked in existing literature. The valuable insights presented are crucial for maximizing the potential of LoRa in indoor environments with high population density. The findings strongly advocate for the use of LoRa for real-time environmental monitoring and innovative IoT applications within university campuses.

Thermoelectric energy harvesting associated with heat pipes for monitoring highways weather conditions

This work aims to develop a prototype for autonomously monitoring highway weather conditions using thermoelectric energy. The prototype utilizes solar thermal energy, heat absorbed by the asphalt and soil heat to generate, convert, manage, and store energy. The study investigates the optimal functioning of the thermoelectric generator, including utilizing the soil as a heat sink and incorporating passive thermal control devices like heat pipes. A commercial ultra-low power converter model LTC3108 was experimentally tested for efficient energy conversion. The monitoring system is designed to acquire readings from sensors measuring relevant climate parameters such as temperature, atmospheric pressure, and humidity, focusing on ultra-low power characteristics. The acquired data is transmitted via LoRa technology to a server for processing and analysis. The prototype generated 286.24 J of energy with a maximum temperature gradient of 4.25 ºC in the thermoelectric generator. The energy cost of transmitting a 40-byte message through the LoRaWAN module was 0.625 J. The proposed climate data acquisition circuit had a theoretical consumption of 17.69 J. Experimental tests with the LTC3108 converter, and a 0.22 F supercapacitor demonstrated successful data transmission. This work provides a solid foundation for future studies utilizing thermoelectric energy in highway applications, resulting in a prototype suitable for real-world road scenarios.

Development of automatic arc sag measurement equipment for tensioned line tightening construction on overhead transmission lines

Due to the low degree of automation of the existing arc droop measurement device, the tension line digital twin system is not yet able to simulate the change of arc droop in the actual engineering situation in a high degree of real time. To this end, a kind of arc droop automatic measuring equipment applicable to the working condition of tension frame line tightening construction is developed. In terms of hardware, firstly, the STC microcontroller is used to control the motor to achieve free movement of the equipment on the wire. Secondly, the Beidou positioning module is used to collect the spatial coordinates of each point on the wire, thereby obtaining the spatial attitude of the wire and calculating the final sag. Finally, LoRa technology with long transmission distance and low power consumption is selected for wireless transmission of controlled data and collected data. The software system of the device is equipped with the motor's action logic, which cooperates with the hardware to respond to the control commands issued by the terminal. The developed arc droop measurement equipment has been successfully applied in the project, compared with the traditional arc droop observation construction method construction efficiency increased by 28%, the difference between the observation file arc droop and the design arc droop after erection of the line is 1.96%. Digital simulation and remote guidance of arc sag variations by the digital twin system for tension racking lines was realized while ensuring the engineering requirements.

IoT and machine learning models for multivariate very short‐term time series solar power forecasting

AbstractIn solar energy generation, the inherent variability caused by cloud cover and weather events often leads to sudden fluctuations in power outputs. Addressing this challenge, the authors’ study focuses on very short‐term solar irradiance (SI) prediction. Leveraging multivariate time series datasets, the authors improve very short‐term SI predictions. To achieve accurate very short‐term SI predictions, the authors employ machine learning techniques throughout the forecasting process. Additionally, the authors’ work pioneers the integration of the Internet of Things (IoT) into solar power systems, a novel approach in the field. The authors leverage LoRa (long range) technology for low‐cost, low‐power, and long‐range wireless control networks. The authors’ study focuses on SI forecasting using long short‐term memory and bi‐directional long short‐term memory (Bi‐LSTM) models, achieving high accuracy. The SI forecasts are evaluated in terms of root‐mean‐square error (RMSE) and mean absolute error in relation to meteorological data and sky image data. The improvement in performance can be attributed to the Bi‐LSTM's bidirectional nature, allowing it to incorporate future information during training, thereby enhancing its predictive capabilities. Overall, the results suggest that the Bi‐LSTM model is more suitable for accurately forecasting SI, particularly in scenarios requiring short‐term predictions based on rapidly changing environmental factors.

Affordable Green IoT-Based System for Remote Sensing of PM1, PM2.5 and PM10 Particulate Matter

Particulate matter (PM) possesses the capacity to intrude deeply into the respiratory system, even infiltrating the bloodstream. Prolonged exposure to heightened PM concentrations has been causally associated with a spectrum of respiratory ailments, encompassing asthma, bronchitis, and chronic obstructive pulmonary disease (COPD). This paper endeavours to introduce a remote sensing system tailored for the quantification of particulate matter, underpinned by the principles of the Green Internet of Things (GioT). This framework is rooted in the pursuit of an ecologically harmonious infrastructure and energy sustainability. The devised architecture integrates PM 1, PM 2.5, and PM 10 sensors, seamlessly interfaced with Arduino microcontrollers, thereby facilitating real-time data acquisition. To sustainably energize this system, solar cells are harnessed to furnish a reliable power source. Augmenting this configuration is a data communication infrastructure established upon LoRa (Long Range) technology, a hallmark of the Green Internet of Things. Concomitantly, empirical investigations have been conducted to illuminate system performance. These inquiries were conducted in conditions mirroring low particle deposition scenarios, culminating in an observed average error spectrum spanning 5.64% to 6.95%. Further examinations scrutinized the data transmission process through LoRa, unveiling an impressive maximal transmission range of 281 m while conserving data transmission viability. Additionally, an exploration of energy utilization encompassed both empty and comprehensive data transmissions, divulging a marginal disparity of 0.1 Watt per data transmission event. In the final analysis, the conducted assessments affirm the system's fidelity, disclosing an error quotient below the 10% threshold. Most notably, the discerned operational efficiency of the energy supply substantiates its synergy with sustainable energy paradigms. In summation, this study proffers an innovative remote sensing system, harmonizing the nuanced demands of PM quantification with the virtuous principles of the Green Internet of Things. By merging the acumen of PM sensors, solar-driven power provisions, and the efficiency of LoRa communication, the proposed framework establishes a salient benchmark for integrative sensor networks with ecological resonance.

Integration of LoRa-enabled IoT infrastructure for advanced campus safety systems in Taiwan

Amid rising concerns about campus safety in Taiwan, particularly with the global trend towards smart cities, integrating the Internet of Things (IoT) into institutional security frameworks has become pivotal. The paper discusses the implementation of using iBeacons and Long Range (LoRa) technology to locating the student position and ensure his safety in the school campus. It uses Internet of Things (IoT) approach in real time to monitor and locate the student presence in the school compound. This paper unveils an innovative design for a campus security system that harnesses the LoRa technology. In the system, the students are equipped with devices containing Bluetooth Low Energy (BLE) beacons to capture and transmit real-time location data. The system response time to locating student in abnormal locations such as cornered and concealed areas is about one second. By extending this system to cover all individuals on campus, a closely monitored environment and areas is enabled that significantly bolstering the security measures. This not only furnishes a dynamic protective layer for educational institutions but also serves as a proactive deterrent against potential security breaches. Ultimately, this research underscores the transformative potential of merging IoT with campus security to ushering in a new era of student safety. LoRa technology offers advantages in battery life, cost-effectiveness, deployment flexibility, and network coverage etc. Therefore, this paper ultimately provides a method of how to utilize the LoRa technology to develop a campus security system. Unlike artificial intelligence (AI)-based image recognition, which raises concerns about privacy and human rights; the features of LoRa’s long-range communication and low power consumption make it a more suitable choice.

Implementasi Teknologi LoRa untuk Monitoring Real-Time Lampu PJU Berbasis Solar Panel

In the digital age, public road lighting (PJU) monitoring efficiency is becoming essential for effective infrastructure management. The research developed a PJU monitoring system based on LoRa technology and an Arduino microcontroller to monitor the operating conditions of PJU solar panels in real time. The system uses the LoRa 433 MHz module for remote data communication and is equipped with a voltage sensor to monitor batteries and solar panels as well as an ACS712 current sensor to measure current consumption on LED lights. The data is displayed on an I2C 16x2 LCD screen, making monitoring easy. LoRa technology offers the advantages of broad communication range and low power consumption. The development method used is prototyping, including needs analysis, system design, implementation, testing, and maintenance. Test results show that the system works well, with sensors providing adequate accuracy and LoRa communication enabling remote data access. The system improves the efficiency and accuracy of PJU monitoring, as well as reduces time and effort in the monitoring process. Overall, the system is an effective solution for PJU management in the digital age.

Design of IIOT Device Based on LoRa for Parsing Data Directly to SCADA System

As the revolution industry 4.0 was started as well as the productivity increases, the need for data analysis increases. Some industries have wide production area and they need long wire to connected between office for transferring data. A wireless system is one of solution to overcome this issue so that the technique of parsing data to expand coverage area for both local and remote systems. In this research, a device that supports IIOT (Industry on Internet of Things) will be designed by utilizing LoRa technology connected to a SCADA (Supervisory Control and Data Acquisition) system for direct parsing data. By using a SCADA system concept and incorporating wireless system as well as connecting to internet so the industrial system can be monitoring from anywhere so productivity can be increased. To test the proposed design, the two scenarios of performance test have been done, they are communicating distance test and the parsing data between two nodes LoRa as well as connecting to SCADA system. The first scenario results in the maximum communication distance that can still carried out reaching 400m. Meanwhile, the experience of second scenario on the IIOT device prototype produces accurate data so that it can be implemented in the wider industry

Advancing automotive high-performance computing: integrating direction API with LORA technology in unified machine vision for future smart cars

Advancing automotive high-performance computing: integrating direction API with LORA technology in unified machine vision for future smart cars

Inner External DQN LoRa SF Allocation Scheme for Complex Environments

In recent years, with the development of Internet of Things technology, the demand for low-power wireless communication technology has been growing, giving rise to LoRa technology. A LoRa network mainly consists of terminal nodes, gateways, and LoRa network servers. As LoRa networks often deploy many terminal node devices for environmental sensing, the limited resources of LoRa technology, the explosive growth in the number of nodes, and the ever-changing complex environment pose unprecedented challenges for the performance of the LoRa network. Although some research has already addressed the challenges by allocating channels to the LoRa network, the impact of complex and changing environmental factors on the LoRa network has yet to be considered. Reasonable channel allocation should be tailored to the situation and should face different environments and network distribution conditions through continuous adaptive learning to obtain the corresponding allocation strategy. Secondly, most of the current research only focuses on the channel adjustment of the LoRa node itself. Still, it does not consider the indirect impact of the node’s allocation on the entire network. The Inner External DQN SF allocation method (IEDQN) proposed in this paper improves the packet reception rate of the whole system by using reinforcement learning methods for adaptive learning of the environment. It considers the impact on the entire network of the current node parameter configuration through nested reinforcement learning for further optimization to optimize the whole network’s performance. Finally, this paper evaluates the performance of IEDQN through simulation. The experimental results show that the IEDQN method optimizes network performance.

Development of intelligent traction walking plate for tension overhead construction

AbstractIn the process of tension wire construction of transmission lines, the key state quantities such as tension, inclination and spatial position of the traction walking plate directly affect the construction safety. At present, the construction personnel use visual observation to obtain the state quantity of traction walking plate in the process of tension wire construction. This monitoring method is not only inefficient, and there are personal subjective factors of judgment errors. For this reason, this paper develops a kind of intelligent traction walking plate for tension wire construction, based on the principle of resistance strain type and gyroscope to obtain the tension and inclination of the traction walking plate, respectively, using two front cameras and two rear cameras to obtain the spatial position of the traction walking plate, and finally completing the real‐time transmission of the monitoring data through the Lora technology. The developed intelligent traction walking plate was applied in Shandong Heze Yongfeng 220 kV transmission line project on 11 October 2020, which improved the construction efficiency by 15.71% while ensuring the construction safety.

Implementing AI on low-power embedded devices for digital water meter identification and data transfer via lora network

This study introduces an artificial intelligence system implemented on the ESP32-CAM platform, aimed at conducting optical character recognition (OCR) on water meters. Leveraging LoRa technology for data transmission ensures efficient energy utilization and convenient long-range communication capabilities. The system achieves an impressive accuracy rate of 98.2% in identifying water meter readings, showcasing its reliability. Proposed as a feasible solution, it offers the advantages of low energy consumption, cost-effectiveness, and flexibility in widespread deployment, particularly leveraging existing water meter infrastructure. Thus, this research presents a promising choice for various applications beyond merely reading water meter readings. Its efficient and accurate OCR functionality makes it suitable for diverse scenarios, ranging from smart city initiatives to industrial automation processes. With its ability to integrate seamlessly into existing infrastructure and deliver reliable results, this system stands poised to revolutionize OCR applications in various domains, contributing to enhanced efficiency and productivity.

Long-Term Stability of Low-Cost IoT System for Monitoring Water Quality in Urban Rivers

Monitoring water quality in urban rivers is crucial for water resource management since point and non-point source pollution remain a major challenge. However, traditional water quality monitoring methods are costly and limited in frequency and spatial coverage. To optimize the monitoring, techniques such as modeling have been proposed. These methods rely on networks of low-cost multiprobes integrated with IoT networks to offer continuous real-time monitoring, with sufficient spatial coverage. But challenges persist in terms of data quality. Here, we propose a framework to verify the reliability and stability of low-cost sensors, focusing on the implementation of multiparameter probes embedding six sensors. Various tests have been developed to validate these sensors. First of all, a calibration check was carried out, indicating good accuracy. We then analyzed the influence of temperature. This revealed that for the conductivity and the oxygen sensors, a temperature compensation was required, and correction coefficients were identified. Temporal stability was verified in the laboratory and in the field (from 3 h to 3 months), which helped identify the frequency of maintenance procedures. To compensate for the sensor drift, weekly calibration and cleaning were required. This paper also explores the feasibility of LoRa technology for real-time data retrieval. However, with the LoRa gateways tested, the communication distance with the sensing device did not exceed 200 m. Based on these results, we propose a validation method to verify and to assure the performance of the low-cost sensors for water quality monitoring.

Parasitic Loaded Shorting Pin Based Compact Multi-slot LoRa Antenna for Military Application

In this paper, work on a compact multi-slot patch antenna with a parasitic load and shorting-pin has been presented for its use in long range (LoRa) defense applications. Initially the antenna is designed and simulated using HFSS and a parametric study has been carried out for achieving an optimized antenna configuration. This is followed by the fabrication of antenna prototype based on the optimal performances obtained from the simulated results. Further, the return loss along with free space co-polar and cross-polar radiation pattern measurements has been carried out for the fabricated antenna. The experimental results show a good comparison with that of the simulated one. The proposed antenna resonates at an operating frequency of 866 MHz, providing a -10 dB bandwidth from 856 MHz-877 MHz, which covers the wireless standard used for LoRa technology. Finally, a comparative analysis of the proposed antenna with the recently reported works is presented.

LoRaAid: Underground Joint Communication and Localization System Based on LoRa Technology

LoRaAid: Underground Joint Communication and Localization System Based on LoRa Technology

LoRa-Powered IoT Messaging System for Internet-Free Communication

Abstract: This paper describes a novel message system that makes use of LoRa technology and the ESP32 IoT chip to deliver dependable communication in places with spotty internet access. The ESP32 module is integrated into the system architecture as an access point, and WebSocket protocol is used to enable messaging services. As a key center for communication, the ESP32- based access point facilitates flawless message exchanges between users. The key innovation lies in its incorporation of the LoRa RA-02 module, which enables RF-based communication among devices. The messaging system guarantees connectivity even in remote locations or areas with inadequate internet infrastructure by utilizing LoRa technology. This capability is especially helpful in situations when regular internet connectivity is unavailable or inconsistent, including in low-resource contexts, rural populations, or areas affected by disasters. Because of the messaging system's easy-to-use and accessible architecture, users can communicate without an internet connection. This lessens the need for internet connections and lowers related expenses, improving communication accessibility and affordability for marginalized communities.

Emergency Communication System for Hills and Forest Region Using IOT

This project presents an innovative approach to establishing robust communication systems in remote areas devoid of traditional network infrastructure. By harnessing the power of IoT and LoRa technology, the project proposes a Long Distance Communication System that not only facilitates connectivity but also supports critical emergency evacuation procedures. The cornerstone of this system is the LoRa Mesh Library, which enables a decentralized network topology, ensuring seamless communication even in the most challenging environments via ad hoc network. The project further explores QoS routing and traffic scheduling within 802.11 wireless mesh networks to guarantee consistent and quality communication links over long distances. Additionally, it integrates Secure IT, a security framework utilizing Firebase, Google Map, and Node.js, to provide real-time location-based alerts, enhancing the safety of individuals in emergency situations.Thisabstractencapsulatestheproject’smultifacetedapproach to overcoming the limitations of current communication infrastructures, offering a scalable, secure, and efficient solution that has the potential to revolutionize connectivity in isolated regions worldwide.

Wireless Patient Monitoring System with LoRa Communication for Remote Healthcare

This study presents a novel approach to remote healthcare through a wireless patient monitoring system using LoRa communication. The system consists of two main parts: a transmitter and receiver. The transmitter collects vital health data, such as body temperature, SpO2, accelerometer and gyroscope measurements, electrocardiogram (ECG) signals, air quality and heart rate. This information will be transmitted wirelessly using LoRa technology to the receiver section, which will transmit the data to the IoT cloud server. In addition, the receiver section is equipped with a GSM module to send an SMS alert in the event of abnormal health parameters. The aim of this system is to provide real-time monitoring of patients in remote areas, enabling timely interventions and improving accessibility to healthcare. The efficiency and reliability of the system make it a promising solution for remote patient monitoring in challenging environments.