LoRa Nodes 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.

IoT-based real-time analysis of battery management system with long range communication and FLoRa

In the current scenario, the world is focused on renewable energy generation to achieve sustainability by 2030 regarding clean and affordable energy. Lithium-ion (Li-ion)-based Battery Energy storage (BES) is a prominent approach that is widely adopted for managing large-scale renewable energy generation. Battery Management Systems (BMS) play a critical role in optimizing battery performance of BES by monitoring parameters such as overcharging, the state of health (SoH), cell protection, real-time data, and fault detection to ensure reliability. Previous studies have concluded that the implementation of Internet of Things (IoT) with LoRa ensures effective real-time monitoring of the BMS of Li-ion batteries. This study proposed and implemented a customized LoRa and IoT-based hardware system with a gateway to acquire parameters such as terminal voltage, current, charge voltage, charge current, cycle, temperature, state of charge (SoC), and SoH, and log them into the cloud server. An OMnet++-based Framework for LoRa (FLoRa) simulation was implemented to analyze the power consumption and residual energy of the customized LoRa nodes. The simulation was configured with a spread factor of 7, a carrier frequency of 433 MHz, a bandwidth of 125 KHz, and a transmission power of 2 dBm. The simulation results indicated that Node 3 had the highest mean power consumption (0.028233) and total energy consumption (0.146592), whereas Node 0 exhibited the lowest mean power consumption (0.023413) and total energy consumption (0.070204). Additionally, a comprehensive dataset encompassing voltage, current, and time was created and utilized for precise calculations of the battery's capacity and state of health, with potential use in future predictions.

Enhancing Fishing Efficiency with Geographic Information System and Optimized Methods

Traditional fishing techniques frequently lack efficiency and optimization, resulting in fishermen obtaining unsatisfactory yields. This study presents a novel approach by incorporating Geographic Information System (GIS) technology, notably utilizing Leaflet, to improve fishing techniques. The suggested system incorporates a LoRa node tool that logs the journeys of fishermen, offering comprehensive itineraries and data on the distribution of fish and unfavorable weather conditions. Notable outcomes were attained by employing the haversine approach to compute distances between the LoRa Gateway and different data points. The approach exhibited a negligible error margin of 0.157% in contrast to the calculations performed in Excel. In addition, the GPS accuracy testing produced remarkable results, with latitude and longitude errors of 0.000116% and 0.000002%, respectively. The LoRa system demonstrated a range of RSSI performance, with values ranging from -57 dBM at 50 meters to -121 dBM at 1500 meters. This range of performance guarantees dependable transmission of data over significant distances. The findings underscore the GIS-based strategy's efficacy in enhancing the effectiveness and precision of conventional fishing methods, presenting a promising technical improvement for the fishing sector.

Evaluation of LoRa Network Performance for Water Quality Monitoring Systems

Conserving water resources from scarcity and pollution is the basis of water resource management and water quality monitoring programs. However, due to industrialization and population growth in Malaysia, which have resulted in poor water quality in many areas, this program needs to be improved. A smart water quality monitoring system based on the internet of things (IoT) paradigm was designed to analyze water conditions in real time and enable effective water management. Long-range (LoRa) application of the low-power, wide-area networking concept has become a phenomenon in IoT smart monitoring applications. This study proposes the implementation of a LoRa network in a water quality monitoring system-based IoT approach. The LoRa nodes were embedded with measuring sensors pH, turbidity, temperature, total dissolved solids, and dissolved oxygen, in the designated water stations. They operate at a transmission power of 14 dB and a bandwidth of 125 kHz. The network properties were tested with two different antenna gains of 2.1 dBi and 3 dBi, with three different spread factors of 7, 9, and 12. The water stations were located on the Sungai Pantai and Sungai Anak Air Batu rivers on the Universiti Malaya campus, Malaysia. Following a dashboard display and K-means analysis of the water quality data received by the LoRa gateway, it was determined that both rivers are Class II B rivers. The results from the evaluation of LoRa performance on the received strength signal indicator, signal noise ratio, loss packet, and path loss at best were −83 dBm, 7 dB, <0%, and 64.41 dB, respectively, with a minimum received sensitivity of −129.1 dBm. LoRa has demonstrated its efficiency in an urban environment for smart river monitoring purposes.

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.

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.

Development of Fish Distribution Information System for Digital Virtual Assitant based on Harvesine with LoRA Node

The pressing issue of suboptimal and inefficient traditional fishing practices among local communities. Traditional fishermen often operate with inadequate equipment, resulting in less-than-optimal fishing outcomes. In this context, the lack of modern tools and techniques hinders the effectiveness of fish capture. Addressing these challenges is crucial for enhancing the productivity and sustainability of traditional fishing practices. The study aims to develop an advanced Fish Distribution Information System tailored for a Digital Virtual Assistant using the Haversine method and LoRA nodes. This system provide instantaneous, location-specific details about fish distribution and involves designing a data logger serving as repository for travel histories, utilizing a website as the user interface interfacing with a Leaflet map in a Geographic Information System (GIS). The Haversine method is employed for precise distance computations between LoRA Gateway, information about fish distribution, and unfavorable weather conditions within the specified area. the resukt show that The distance displayed on the website with the haversine method with excel calculation comparison produces an error calculation of 0.157%, while the percentage error for GPS latitude accuracy is 0.000116% and longitude percentage is 0.000002%. RSSI performance at LoRA ranges from -57dBM at a distance of 50m and -121dBM at a distance of 1500m.

Development of LoRaWAN-based IoT system for water quality monitoring in rural areas

This article delineates the design and deployment of an innovative real-time water quality monitoring system tailored for rural regions, focusing on monitoring the water resource quality parameters. We propose a solar-powered, waterproof, portable, and Internet of Things (IoT)-enabled solution that leverages Long Range Wide Area Network (LoRaWAN) technology. Central to this system is a sophisticated LoRa node outfitted with an array of sensors for capturing key water parameters, such as pH, total dissolved solids, turbidity and temperature. A conjunction of an Arduino microcontroller-based board and a LoRa shield facilitates real-time data capture and transmission to a LoRaWAN gateway. The acquired data is transmitted to The Things Network server, which is seamlessly integrated with a ThingSpeak web-based IoT server and ThingView mobile applications. We incorporate a solar cell with a solar shield to ensure sustainable energy provision for powering the entire system through a rechargeable battery. This allows users to access vital water quality information online simultaneously and continuously in real-time. As a testament to its robustness, the system was empirically tested at Gambang Lake to demonstrate its effectiveness, functionality, buoyancy, and waterproof capabilities. We further validated the results by comparing them with laboratory sample analysis findings. Experimental evaluations confirmed the system's reliability, as evidenced by the strong agreement between the water conditions measured using our solution and those obtained from laboratory instruments. Moreover, our system efficiently and remotely updated data across multiple IoT platforms using the LoRa radio interface over the LoRaWAN gateway.

LoRaute: Routing Messages in Backhaul LoRa Networks for Underserved Regions

LoRa technology endows unprecedented ability to connect isolated geographical landscapes and build community networks that serve specific purposes. As the network grows, coherent routing of messages becomes imperative to meet the network’s objectives and Quality of Service (QoS) requirements. However, despite the recent rise in research and development centered around LoRa networks, not much research addresses the routing mechanisms in LoRa networks. Moreover, the LoRa routing mechanisms must run on low-power and resource-constrained devices, as these networks primarily target far-off locations or volatile environments such as volcanoes. Hence, this work proposes routing mechanisms (LoRaute) for LoRa networks that help route messages considering the messages’ QoS requirements. Also, a multi-purpose network hardware is proposed, which serves as a LoRa network base station or a WiFi to LoRa bridge for TCP/IP communication. Additionally, this work furnishes and assesses the implementation of the LoRa network and the routing mechanisms in a real environment. The implementation results indicate a seamless and rapid setup of multi-hop LoRa networks. Moreover, the implementation achieves a routing table record size of 9B, 24.45ms routing latency, and 171mA peak current consumption by the proposed LoRa node. Finally, the proposed system serves as a backhaul network for essential long-range communications, as demonstrated by the experimental setup.

Smart Traffic Light Prototype Implementation using LoRa Technology

Smart traffic light systems have the potential to significantly improve traffic flow and safety in both urban and suburban areas. Most smart traffic lights were deployed in a city with adequate infrastructure. However, building a smart traffic light in suburban areas can be challenging due to the limited availability of communication infrastructure. This paper aims to build and implement a smart traffic light system in a suburban area by incorporating LoRa (Long Range) technology for communication. LoRa is a low-power wireless technology that allows long-distance communication between devices and has low data rates and power consumption. It was created especially for Internet of Things (IoT) devices that run on batteries. The peer-to-peer method was used, which utilized three LoRa nodes for enabling the communication between traffic lights. Usability testing was conducted to determine the impact of the smart traffic light system on traffic flow and safety. The result of the testing shows that the system is necessary to improve traffic management in suburban areas, and the respondents were positive about its potential benefits. Received Signal Strength (RSS) was measured in two different environments: paddy field and residential areas in Kampung Alor Ara, Arau, Perlis. Results show a slight difference in RSS indicator versus distance for the placement of traffic lights in three-junction lanes. The optimum distance between any two traffic lights is 100m in the paddy field area. While for residential areas, the optimum distance is only 50m. The successful implementation of this prototype demonstrates the potential of LoRa technology in enhancing traffic management in suburban areas. Further research such as implementing artificial intelligence (AI) and utilizing advanced LoRa devices and antennas is needed to explore the scalability and long-term sustainability of the system.

Long-Range Network of Air Quality Index Sensors in an Urban Area.

In recent times the escalating pollution within densely populated metropolitan areas has emerged as a significant and pressing concern. Authorities are actively grappling with the challenge of devising solutions to promote a cleaner and more environmentally friendly urban landscapes. This paper outlines the potential of establishing a LoRa node network within a densely populated urban environment. Each LoRa node in this network is equipped with an air quality measurement sensor. This interconnected system efficiently transmits all the analyzed data to a gateway, which subsequently sends it to a server or database in real time. These data are then harnessed to create a pollution map for the corresponding area, providing users with the opportunity to assess local pollution levels and their recent variations. Furthermore, this information proves valuable when determining the optimal route between two points in the city, enabling users to select the path with the lowest pollution levels, thus enhancing the overall quality of the urban environment. This advantage contributes to alleviating congestion and reducing excessive pollution often concentrated behind buildings or on adjacent streets.

Enabling large-scale low-power LoRa data transmission via multiple mobile LoRa gateways

Low-Power Wide Area Networks (LPWANs) enable large-scale Internet of Things (IoT) applications. LoRa is a promising LPWAN technology, but LoRa nodes are battery-powered, so their lifetime depends on energy efficiency. In practice, LoRa nodes have short battery life because long transmission distances to gateways incur high energy costs. With expensive battery replacement, particularly in large networks, it is crucial to reduce LoRa nodes’ energy consumption. Existing low-power transmission techniques focus on static or single mobile gateways, consuming substantial remote node energy unfit for large-scale networks. This paper proposes integrating LoRa with mobility to minimize node energy use by shortening transmission distances. We design the first large-scale mobile LoRa system, MLoRaDrone, using multiple unmanned aerial vehicles (UAVs) equipped with LoRa gateways flying near nodes. First, we present a low-power mechanism for reliable node sensing and UAV communication. Next, we develop a LoRa transmission parameter assignment strategy encompassing a distributed ADMM-based optimal transmission policy and a 32-approximation channel allocation. Moreover, we propose a UAV trajectory scheduling scheme with a 4-approximation minimizing node energy and UAV flight paths. Evaluations on varied scales verify MLoRaDrone effectiveness for different node counts and UAV paths. Compared to baselines, MLoRaDrone reduces 3000-node energy consumption by up to 35.56× within 25 km2.

Massive multi-player multi-armed bandits for IoT networks: An application on LoRa networks

More and more manufacturers, as part of the transition towards Industry 4.0, are using Internet of Things (IoT) networks for more efficient production. The wide and extensive expansion of IoT devices and the variety of applications generate different challenges, mainly in terms of reliability and energy efficiency. In this paper, we propose an approach to optimize the performance of IoT networks by making the IoT devices intelligent using machine learning techniques. We formulate the optimization problem as a massive multi-player multi-armed bandit and introduce two novel policies: Decreasing-Order-Reward-Greedy (DORG) focuses on the number of successful transmissions, while Decreasing-Order-Fair-Greedy (DOFG) also guarantees some measure of fairness between the devices. We then present an efficient way to manage the trade-off between transmission energy consumption and packet losses in Long-Range (LoRa) networks using our algorithms, by which LoRa nodes adjust their emission parameters (Spreading Factor and transmitting power). We implement our algorithms on a LoRa network simulator and show that such learning techniques largely outperform the Adaptive Data Rate (ADR) algorithm currently implemented in LoRa devices, in terms of both energy consumption and packet losses.

Jamming of LoRa PHY and Countermeasure

LoRaWAN forms a one-hop star topology where LoRa nodes send data via one-hop uplink transmission to a LoRa gateway. If the LoRa gateway can be jammed by attackers, it may not be able to receive any data from any nodes in the network. Our empirical study shows that although the LoRa physical layer (PHY) is robust and resilient by design, it is still vulnerable to synchronized jamming chirps. Potential protection solutions (e.g., collision recovery, parallel decoding) may fail to extract LoRa packets if an attacker transmits synchronized jamming chirps at higher power. To protect the LoRa PHY from such attacks, we propose a new protection method that can separate LoRa chirps from jamming chirps by leveraging their difference in power domain. We note that the new protection solution is orthogonal to existing solutions that leverage the chirp misalignment in the time domain or the frequency disparity in the frequency domain. We conduct experiments with COTS LoRa nodes and software-defined radios with varied experiment settings such as different spreading factors, bandwidths, and code rates. Results show that synchronized jamming chirps at high power can jam all previous solutions, whereas our protection solution can effectively protect LoRa gateways from the jamming attacks.

Low-Power IoT for Monitoring Unconnected Remote Areas.

This paper deals with IoT devices deployed in remote areas without terrestrial Internet connectivity. We consider connecting IoT devices on the ground to the Internet through an aerial system based on an Unmanned Aerial Vehicle (UAV) for smart agriculture and environmental monitoring. The UAV flying over the remote area receives data from distributed IoT devices. The transmissions between the ground sensors and the UAV are carried out via LoRa. We have proposed a synchronization protocol for the opportunistic communication of LoRa IoT devices with a gateway onboard the UAV to save node battery life. Class A LoRa nodes on the ground transmit only when the UAV is expected to pass close to them; otherwise, they stay in the sleeping state most of the time. This paper provides a detailed description of the formulation of the synchronization protocol. The UAV's flying dynamics have been considered for characterizing its speed and the time of visibility of each IoT sensor. Our model has allowed an analytical approach that can help to determine the best settings for LoRa transmissions. Finally, experiments have been carried out to assess the path loss attenuation, and a laboratory setup of the synchronization protocol has been implemented for the preliminary validation of our scheme.

LPWC: Long preamble wake-up communication protocol for a LoRa network

LoRa is widely used in various applications, which has gained increasing popularity in the field of Internet of Things (IoT). However, in legacy LoRa protocols, bidirectional communications and low power consumption cannot be achieved simultaneously, hindering the further development of LoRa. In this study, a long preamble wake-up communication (LPWC) protocol is proposed to alleviate the aforementioned issue. This scheme is performed at both sides of communication: (1) LoRa nodes are designed to sleep periodically to save more power; (2) LoRa gateway must send packets with long preamble to maintain the reliability of downlink communication. In addition, an energy model is built to prove that an optimal cycle period exists for LoRa nodes to save more energy. Then we implement simulations to evaluate the performance of the proposed method in various cases. Results show that LPWC outperforms LoRaWAN Class B mode in terms of power conservation and packet latency.

Energy-Efficient LoRa Routing for Smart Grids.

Energy-efficient routing protocols in Internet of Things (IoT) applications are always of colossal importance as they improve the network's longevity. The smart grid (SG) application of the IoT uses advanced metring infrastructure (AMI) to read and record power consumption periodically or on demand. The AMI sensor nodes in a smart grid network sense, process, and transmit information, which require energy, which is a limited resource and is an important parameter required to maintain the network for a longer duration. The present work discusses a novel energy-efficient routing criterion in an SG environment realised using LoRa nodes. Firstly, a modified LEACH protocol-cumulative low-energy adaptive clustering hierarchy (Cum_LEACH) is proposed for cluster head selection among the nodes. It uses the cumulative energy distribution of the nodes to select the cluster head. Furthermore, for test packet transmission, multiple optimal paths are created using the quadratic kernelised African-buffalo-optimisation-based LOADng (qAB_LOADng) algorithm. The best optimal path is selected from these multiple paths using a modified version of the MAX algorithm called the SMAx algorithm. This routing criterion showed an improved energy consumption profile of the nodes and the number of active nodes after running for 5000 iterations compared to standard routing protocols such as LEACH, SEP, and DEEC.

IRIS: A low duty cycle cross-layer protocol for long-range wireless sensor networks with low power budget

This paper presents a lightweight cross-layer protocol relIable Routing with coordInated medium acceSs control (IRIS) which is designed for long-range pipeline Wireless Sensor Networks (WSNs) with extremely low power budget, typically seen in a range of monitoring applications. IRIS includes functions of network discovery, medium access control and routing designed for heavily energy constrained networks. The protocol is able to operate with less than 1% duty cycle, thereby conforming to ISM band spectrum regulations in the 868MHz band. The duty cycle can be flexibly configured to meet other regulations/power budgets as well as to improve the route forming performance. Simulation results show a guaranteed route formation in different network topologies with various protocol configurations. An analytical model is presented to validate the simulation results of route formation time. System robustness against unreliable wireless connections and node failures are also demonstrated by simulations. A network of 71 attenuated LoRa nodes is implemented to evaluate the capability of IRIS. Long-term operation of the network shows consistent performance compare with the simulations.

Effects of Body Shadowing in LoRa Localization Systems

Applications where a person carries environmental sensors benefit from knowing the position of the individual. Researchers can locate the device by analyzing the radio signals used for sending information. However, it is necessary to know the impact of body obstruction on communication links and, therefore, on the localization system. The paper studies this impact by using field tests employing one LoRa node with an omnidirectional antenna in an outdoor parking lot with vegetation. The tests use four LoRa gateways located in the corners of the area to measure the signals with and without body obstruction. The node was located on the subject’s chest to maximize signal obstruction. Results show that signal strength decreases by an average of 3 dB on links with body shadowing. Additionally, the study uses these signals with machine learning and Kalman filter localization algorithms. Finally, the results show that body shadowing affects location accuracy in most methods. These results are useful for IoT researchers that need to locate people carrying LoRaWAN devices but cannot use GPS/GNSS due to energy constraints.

Machine learning based Air Pollution Monitoring and predicting cause for pollution

Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Air pollution has a direct impact on human health through the exposure of pollutants and particulates, which has increased the interest in air pollution and its impacts among the scientific community. The main causes associated with air pollution are the burning of fossil fuels, agriculture, exhaust from factories and industries, residential heating, and natural disasters. The Environmental Protection Agency (EPA) tracks the pollution level by calculating the amount of ground-level ozone (O3), Sulphur dioxide (SO2), particulates matter (PM10 and PM2.5), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen dioxide (NO2) present in the air molecule. These substances are in compositions of a common index, called the Air Quality Index (AQI), indicating how clean or polluted the air is currently or forecasted to become in areas. This work deliberates the implementation of cloud based IoT system for air quality monitoring in which the sensors are used to calculate CO, PM2.5 and PM10, O3, SO2 and NOx pollution level with environmental condition like temperature and humidity. The obtained information can be updated in cloud platform using Lora nodes and Lora Gateway. The information fetched from the cloud is transmitted to the Machine learning models which contains the detailed dataset for the pollutants and these models accurately predict the day-wise pollutant concentrations and display them using an application. This work presents the detailed analysis for predicting the cause of pollution by using Support Vector Machine (SVM), Random forest algorithm and K-nearest neighbours (KNN) algorithm.