LoRa Signals Research Articles

Design and performance analysis of integrated sensing and communication scheme based on LoRa signals

LoRa (Long Range) has garnered widespread adoption in Internet of Things (IoT) communications due to its extensive transmission range and robust resistance to interference. Furthermore, its capability to linearly discern deviations across both frequency and time domains renders it an ideal signal for sensing applications. Consequently, this article proposes an Integrated Sensing and Communication (ISAC) scheme based on the LoRa signal. On the communication side, while the modulation and demodulation processes of the LoRa signal are currently kept confidential, this article has theoretically derived its principles and further implemented the communication functionality of LoRa through experimental simulations. On the sensing side, through theoretical derivation and experimental simulation, the influence of different modulation information on the peak amplitude and position of pulse compression was analyzed. Two signal processing schemes are proposed to overcome the impact of modulation information, thereby enabling the sensing function. The communication and sensing performance of the proposed ISAC scheme was evaluated. Experimental results demonstrated that, even in low signal-to-noise ratio (SNR) environments, the communication function maintained a low bit error rate (BER), while the sensing function achieved a high target detection rate, both of which exhibited excellent performance.

Performance of LoRa SX1278 Using Yagi Antenna

This study examines the use of Yagi antennas to extend the LoRa SX1278 transceiver's range, focusing on enhancing communication distance and reducing signal loss. LoRa, a low-power wireless technology, enables long-distance transmission ideal for sensor data, reaching up to 15 km in rural areas and 1-5 km in urban environments. By adjusting impedance, the Yagi antenna improves LoRa signal range and quality by focusing transmission directionally, reducing interference. The test results show that the yagi antenna has better coverage and signal strength than the spring antenna. Testing horizontal conditions with 3 different locations shows that antenna design greatly affects the transceiver of data. The Yagi antenna with modified impedance has the ability to transmit the farthest distance of 6250 meters. Horizontal testing with a fairly short distance is not too significant a difference in the rssi value of the two antennas, but in terms of reception category, the spring antenna is still not good. Test results show that the Yagi antenna significantly outperforms the spring antenna, maintaining better signal strength at longer distances due to its effective electromagnetic energy focus.

A Comprehensive Survey on Deep Learning-Based LoRa Radio Frequency Fingerprinting Identification.

LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns that requires attention. Existing device identification mechanisms use cryptography which has two major issues: (1) cryptography is hard on the device resources and (2) physical attacks might prevent them from being effective. Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. We discuss various categories of radio frequency fingerprinting techniques along with hardware imperfections that can be exploited to identify an emitter. Furthermore, we describe different deep learning algorithms implemented for the task of LoRa device classification and summarize the main approaches and results. We discuss several representations of the LoRa signal used as input to deep learning models. Additionally, we provide a thorough review of all the LoRa RF signal datasets used in the literature and summarize details about the hardware used, the type of signals collected, the features provided, availability, and size. Finally, we conclude this paper by discussing the existing challenges in deep learning-based LoRa device identification and also envisage future research directions and opportunities.

A Long-Range Signal-Based Target Localization Algorithm

Indoor target localization is pivotal across various applications, encompassing security monitoring, behavioral analysis, and elderly care. This work proposes an advanced target localization algorithm that harnesses antennas endowed with sensing capabilities to capture the phase change in the signal ratio, derived from the signal amplitude. This phase change, indicative of the target’s movement direction, is analyzed alongside the front and rear arrival angle information and signal amplitude characteristics obtained from LoRa signals. The algorithm, through a comprehensive examination of the phase change patterns, amalgamated with arrival angle data and signal amplitude characteristics, effectively estimates the precise location of the target. Experimental validations underscore the algorithm’s efficacy in determining the target’s location during continuous walking activity. Conducted within a 6 m × 12 m open platform, the algorithm achieves an average localization error of 48.5 cm, underscoring its superior performance compared to existing methodologies.

Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications

LoRa technology, renowned for its low-power, long-range capabilities in IoT applications, faces challenges in real-world scenarios, including fading channels, interference, and environmental obstacles. This paper aims to study the reliability of LoRa in Non-Line-of-Sight (NLoS) conditions and in noisy and mobile environments for Industrial IoT (IIoT) applications. Experimental measurements consider factors like vegetation and infrastructure, introducing mobility to replicate NLoS conditions. Utilizing an open-source LoRa Physical Layer (PHY) Software-Defined Radio (SDR) prototype developed with GNU Radio, we assess communication reliability through metrics such as Block Error Rate (BLER), Signal-to-Noise-Interference-plus-Noise Ratio (SINR), and data rate. The study reveals the estimated overall reliability of the LoRa signal at 90.23%, emphasizing specific configuration details. This work contributes to the broader field of LoRa communication, encompassing hardware, software, protocols, and management, enhancing our understanding of LoRa’s dependability in challenging IIoT environments.

Enhancing Human Activity Recognition with LoRa Wireless RF Signal Preprocessing and Deep Learning

This paper introduces a novel approach for enhancing human activity recognition through the integration of LoRa wireless RF signal preprocessing and deep learning. We tackle the challenge of extracting features from intricate LoRa signals by scrutinizing the unique propagation process of linearly modulated LoRa signals—a critical aspect for effective feature extraction. Our preprocessing technique involves converting intricate data into real numbers, utilizing Short-Time Fourier Transform (STFT) to generate spectrograms, and incorporating differential signal processing (DSP) techniques to augment activity recognition accuracy. Additionally, we employ frequency-to-image conversion for the purpose of intuitive interpretation. In comprehensive experiments covering activity classification, identity recognition, room identification, and presence detection, our carefully selected deep learning models exhibit outstanding accuracy. Notably, ConvNext attains 96.7% accuracy in activity classification, 97.9% in identity recognition, and 97.3% in room identification. The Vision TF model excels with 98.5% accuracy in presence detection. Through leveraging LoRa signal characteristics and sophisticated preprocessing techniques, our transformative approach significantly enhances feature extraction, ensuring heightened accuracy and reliability in human activity recognition.

A LoRa signal denoising method based on deep learning

A LoRa signal denoising method based on deep learning

Joint channel training and passive beamforming design for intelligent reflecting surface-aided LoRa systems

<abstract><p>In order to examine the potential and synergetic aspects of intelligent reflecting surface (IRS) techniques for Internet-of-Things (IoT), we study an IRS-aided Long Range (LoRa) system in this paper. Specifically, to facilitate the acquisition of accurate channel state information (CSI) for effective reflection of LoRa signals, we first propose an optimal training design for the least squares channel estimation with LoRa modulation, and then, by utilizing the acquired CSI, we develop a high-performing passive beamforming scheme based on a signal-to-ratio (SNR) criterion. Numerical results show that the proposed training design considerably outperforms the baseline schemes, and the proposed passive beamforming design results in a significant improvement in performance over that of the conventional LoRa system, thereby demonstrating the feasibility of extending coverage areas of LoRa systems with the aid of IRS.</p></abstract>

Prototype Design of a Fishing Boat Safety Monitoring System Using LoRa and Microsensor Devices

This paper reports a prototype of a low-cost tracking and monitoring system to address the challenges faced by Indonesian fishermen. The lack of safety equipment in their boats exposes them to high risks of work-related accidents and illnesses. Data from the National Basarnas Center reveals a staggering 24,000 annual fatalities among fishermen during their activities. These issues stem from a combination of factors: poorly designed boats, low prioritization of safety, and the lack of readily available preventive measures. Moreover, the development of telecommunications infrastructure in aquatic areas presents its unique obstacles. In response, this study proposes a prototype design for a "Fishing Boat Safety Monitoring System" utilizing LoRa and Microsensors for proactive and preventive measurement by tracking the boat position and sending the data via a long-distance wireless transmission with Low-Power Wide Area Network (LPWAN) scheme based on frequency-spread spectrum technology. This LPWAN serves as the substitution for cellular network which is usually not available in the ocean. The tracking system uses a low-cost TTGO T-Beam LoRa32 V1.1 Microcontroller Unit (MCU) board that has an embedded SX1276 LoRa module and Neo-6M GPS module. The system also uses a GY-25 gyroscope microsensor. The system implemented a 923 MHz LoRa signal for point-to-point communication between the transmitter to receiver. This research has successfully yielded a developed device capable of tracking the location of boats up to 2 km from the shoreline with -113 dBm received signal strength indicator (RSSI) and around 60% of data quality of service (QoS). Further research will explore the use of high-gain antennas and signal amplifiers integration with embedded LoRa on the MCU board to expand the coverage area of the LoRa signal.

Wall Matters

Wireless sensing has demonstrated its potential of utilizing radio frequency (RF) signals to sense individuals and objects. Among different wireless signals, LoRa signal is particularly promising for through-wall sensing owing to its strong penetration capability. However, existing works view walls as a "bad" thing as they attenuate signal power and decrease the sensing coverage. In this paper, we show a counter-intuitive observation, i.e., walls can be used to increase the sensing coverage if the RF devices are placed properly with respect to walls. To fully understand the underlying principle behind this observation, we develop a through-wall sensing model to mathematically quantify the effect of walls. We further show that besides increasing the sensing coverage, we can also use the wall to help mitigate interference, which is one well-known issue in wireless sensing. We demonstrate the effect of wall through two representative applications, i.e., macro-level human walking sensing and micro-level human respiration monitoring. Comprehensive experiments show that by properly deploying the transmitter and receiver with respect to the wall, the coverage of human walking detection can be expanded by more than 160%. By leveraging the effect of wall to mitigate interference, we can sense the tiny respiration of target even in the presence of three interferers walking nearby.

On the Quasi-Orthogonality of LoRa Modulation

LoRa (Long Range), a low power and wide area modulation scheme based on chirp spread spectrum, is the most popular and widely adopted Internet-of-Things (IoT) technique in industry. A notable and interesting property of LoRa modulation is the quasi-orthogonality of signals modulated under different spreading factors (SFs). Unfortunately, in the literature, there has been no analytical effort to establish the theoretical validity of such quasi-orthogonality. This paper, for the first time, theoretically tackles the quasi-orthogonality of the LoRa modulation. First, we derive in both continuous-and discrete-time domains the cross-correlation between two non-synchronized LoRa signals with different SFs, based on which we analyze the quasi-orthogonality of the LoRa modulation and draw some useful engineering insights. Particularly, we analytically show that in the continuous-time domain, the quasi-orthogonality is guaranteed if one of the SFs of the two LoRa signals is large enough; while, in the discrete-time domain, the quasi-orthogonality is ensured if the maximum of the SFs is large enough. Furthermore, for practical values of the SF, the maximum squared magnitudes of the cross-correlation in the continuous-and discrete-time domains are shown to be 1.14% and 1.08%, respectively, compared to their peak values. We demonstrate the validity and accuracy of our analysis through extensive numerical simulations.

Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa

This study proposes a new approach to gait recognition using LoRa signals, taking into account the challenging conditions found in underground coal mines, such as low illumination, high temperature and humidity, high dust concentrations, and limited space. The aim is to address the limitations of existing gait recognition research, which relies on sensors or other wireless signals that are sensitive to environmental factors, costly to deploy, invasive, and require close sensing distances. The proposed method analyzes the received signal waveform and utilizes the amplitude data for gait recognition. To ensure data reliability, outlier removal and signal smoothing are performed using Hampel and S-G filters, respectively. Additionally, high-frequency noise is eliminated through the application of Butterworth filters. To enhance the discriminative power of gait features, the pre-processed data are reconstructed using an autoencoder, which effectively extracts the underlying gait behavior. The trained autoencoder generates encoder features that serve as the input matrix. The Softmax method is then employed to associate these features with individual identities, enabling LoRa-based single-target gait recognition. Experimental results demonstrate significant performance improvements. In indoor environments, the recognition accuracy for groups of 2 to 8 individuals ranges from 99.7% to 96.6%. Notably, in an underground coal mine where the target is located 20 m away from the transceiver, the recognition accuracy for eight individuals reaches 93.3%.

Development of LoRa Communication System for Effective Transmission of Data from Underground Coal Mines

Underground coal mining is a challenging and hazardous occupation that requires constant monitoring of environmental parameters to ensure the safety of miners. A handful of research has been carried out in developing wireless monitoring devices to monitor underground mine workings using Wi-Fi and ZigBee, which has limitations such as limited range, interference, reliability, power consumption, and security. The main objective of this study is to develop an Underground Gas Monitoring Device using LoRa communication for the effective transmission of monitored data. The testing was carried out in an underground mine model to measure the propagation of the LoRa signal with a line of sight and non-line of sight. It was observed that the system performs fairly well in both situations. During testing, it was observed that there was a drop in RSSI at 14 m for non-line of sight, and beyond 17 m no signal was received. Hence, for every 15 m, a booster is required to be placed to maintain efficient and reliable propagation of signals. In the event of an increase in gas level beyond the threshold limit in the underground mine, the developed system actuates the siren in the underground, boosters, and on the surface.

Approximate BER Performance of LoRa Modulation With Heavy Multipath Interference

This letter proposes a method to approximate LoRa performance over heavy multipath channels. Firstly, the multipath time delay and LoRa signal duration are combined to analyze the path overlapping interference quantitatively. Then simple BER approximations in coherent and non-coherent detections are derived with Gaussian Q-function and algebraic formulas. With the heavy multipath channel model in 3GPP to mimic the realistic indoor industrial scenarios, the approximate expressions are verified by Monte Carlo simulations. Moreover, performance comparison results show that a high spreading factor leads to severe multipath overlap and may result in BER reduction.

Extension of LoRa Coverage and Integration of an Unsupervised Anomaly Detection Algorithm in an IoT Water Quality Monitoring System

High cost, long-range communication, and anomaly detection issues are associated with IoT systems in water quality monitoring. Therefore, this work proposes a prototype for a water quality monitoring system (IoT-WQMS) based on IoT technologies, which include in the system architecture a LoRa repeater and an anomaly detection algorithm. The system performs the data collection, data storage, anomaly detection, and alarm sending remotely and in real-time for the information to be captured by the multisensor node. The LoRa repeater allowed the spatial coverage of the LoRa communication to extend, making it possible to reach a place where originally there was no coverage with a single LoRa transmitter due to topography and line of sight. The prototype performed well in terms of packet loss rate, transmission time, and sensitivity, extending the long-range wireless communication distance. Indoor multinode testing validation for 29 days of the mean absolute error for average relative errors of water temperature, pH, turbidity, and total dissolved solids (TDS) were 0.65%, 0.30%, and 14.33%, respectively. The anomaly detector identified all erroneous data events due to node sensor recalibration and water recirculation pump failures. The IoT-WQMS increased the reliability of monitoring through the timely identification of any sensor malfunctions and extended the LoRa signal range, which are relevant features in the scope of in situ and real-time water quality monitoring.

Toward Wide-Area Contactless Wireless Sensing

Contactless wireless sensing without attaching a device to the target has achieved promising progress in recent years. However, one severe limitation is the small sensing range. This paper presents Widesee to realize wide-area sensing with only one transceiver pair. Widesee utilizes the LoRa signal to achieve a larger range of sensing and further incorporates drone’s mobility to broaden the sensing area. Widesee presents solutions across software and hardware to overcome two aspects of challenges for wide-range contactless sensing: (i) the interference brought by device mobility and LoRa’s high sensitivity; and (ii) the ambiguous target information such as location when employing just a single pair of transceivers for sensing. We have developed a working prototype of Widesee for human target detection and localization that are especially useful in emergency scenarios such as rescue search, and evaluated Widesee with both controlled experiments and the field study in a high-rise building. Extensive experiments demonstrate the great potential of Widesee for wide-area contactless sensing with a single LoRa transceiver pair hosted on a drone.

UAV-Based Wireless Data Collection from Underground Sensor Nodes for Precision Agriculture

In precision agriculture, information technology is used to improve farm management practices. Thereby, productivity can be increased and challenges with overfertilization and water consumption can be addressed. This requires low-power and wireless underground sensor nodes for monitoring the physical, chemical and biological soil parameters at the position of the plant roots. Three ESP32-based nodes with these capabilities have been designed to measure soil moisture and temperature. A system has been developed to collect the measurement data from the sensor nodes with a drone and forward the data to a ground station, using the LoRa transmission standard. In the investigations of the deployed system, an increase in the communication range between the sensor node and the ground station, from 300 m to 1000 m by using a drone, was demonstrated. Further, the decrease in the signal strength with the increasing sensor node depth and flight height of the drone was characterized. The maximum readout distance of 550 m between the sensor node and drone was determined. From this, it was estimated that the system enables the readout of the sensor nodes distributed over an area of 470 hectares. Additionally, analysis showed that the antenna orientation at the sensor node and the drone influenced the signal strength distribution around the node due to the antenna radiation pattern. The reproducibility of the LoRa signal strength measurements was demonstrated to support the validity of the results presented. It is concluded that the system design is suitable for collecting the data of distributed sensor nodes in agriculture.

Exploring LoRa and Deep Learning-Based Wireless Activity Recognition

Today’s wireless activity recognition research still needs to be practical, mainly due to the limited sensing range and weak through-wall effect of the current wireless activity recognition based on Wi-Fi, RFID (Radio Frequency Identification, RFID), etc. Although some recent research has demonstrated that LoRa can be used for long-range and wide-range wireless sensing, no pertinent studies have been conducted on LoRa-based wireless activity recognition. This paper proposes applying long-range LoRa wireless communication technology to contactless wide-range wireless activity recognition. We propose LoRa and deep learning for contactless indoor activity recognition for the first time and propose a more lightweight improved TPN (Transformation Prediction Network, TPN) backbone network. At the same time, using only two features of the LoRa signal amplitude and phase as the input of the model, the experimental results demonstrate that the effect is better than using the original signal directly. The recognition accuracy reaches 97%, which also demonstrate that the LoRa wireless communication technology can be used for wide-range activity recognition, and the recognition accuracy can meet the needs of engineering applications.

Smartphone-Operated Smart Farm Watering System Using Long-Range Communication Technology

Abstract Keeping proper soil moisture is essential in growing good quality and efficient fruit yield. To that effect, soil moisture level must be controlled, to maintain proper watering. A smartphone application was developed to operate a smart farm watering system. It monitors the soil’s moisture and launches sprayers to water dried areas. The system’s architecture was built in a distributed client-server computing system, in a small computing grid. The grid was built across long range (LoRa) communication networks with the same ID, but different addresses. In terms of integration, the system was built using autonomous microprocessors, which consist of a server and five client microprocessors. A smartphone was used as the server of a central controller, and four moisture detection modules and a water spraying system module were used as autonomous clients. The server was inter-connected with the clients via a star-type topology network in the polling processes. Each client module autonomously analyzes the measured digital voltage of the moisture sensor plugged into the soil. When the server sends queries regarding the status of the moisture level, the client sends the request signal to the server using the LoRa communication technology. The communication between the server and the clients is based on the LoRa communication technology. The LoRa-to-Bluetooth converter is used to connect the Bluetooth and the LoRa signal. The field test was performed in a watermelon field, with an area of approximately 6600 m2. The water spraying system constructed with LoRa communication technology could successfully manage and control the moisture level in the field test.

A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges

Long-range (LoRa) technology is most widely used for enabling low-power wide area networks (WANs) on unlicensed frequency bands. Despite its modest data rates, it provides extensive coverage for low-power devices, making it an ideal communication system for many internet of things (IoT) applications. In general, LoRa is considered as the physical layer, whereas LoRaWAN is the medium access control (MAC) layer of the LoRa stack that adopts a star topology to enable communication between multiple end devices (EDs) and the network gateway. The chirp spread spectrum modulation deals with LoRa signal interference and ensures long-range communication. At the same time, the adaptive data rate mechanism allows EDs to dynamically alter some LoRa features, such as the spreading factor (SF), code rate, and carrier frequency to address the time variance of communication conditions in dense networks. Despite the high LoRa connectivity demand, LoRa signals interference and concurrent transmission collisions are major limitations. Therefore, to enhance LoRaWAN capacity, the LoRa Alliance released many LoRaWAN versions, and the research community has provided numerous solutions to develop scalable LoRaWAN technology. Hence, we thoroughly examine LoRaWAN scalability challenges and state-of-the-art solutions in both the physical and MAC layers. These solutions primarily rely on SF, logical, and frequency channel assignment, whereas others propose new network topologies or implement signal processing schemes to cancel the interference and allow LoRaWAN to connect more EDs efficiently. A summary of the existing solutions in the literature is provided at the end of the paper, describing the advantages and disadvantages of each solution and suggesting possible enhancements as future research directions.