Search Results (58)

Driven by the latest advancements in wireless technology, location-based services have attracted the interest of computing and telecommunication industries, as well as academia, to launch fast and accurate localization systems. The aim of this work is to propose a closed-loop localization framework for large-scale deployments, facilitating both the modeling and continuous monitoring of Activities of Daily Living (ADLs). The proposed system learns from a minimal set of Received Signal Strength Indicator (RSSI) samples, enriches them to cover unmeasured distances, and keeps recalibrating itself with live data. This method delivers a 0.5–0.8 m mean error, improving the error reported in recent studies by 65%. Furthermore, once reliable position estimation is achieved, the proposed framework can detect predefined Activities of Daily Living (ADLs) based on location patterns and movement behaviors, achieving 91% accuracy. This capability opens new opportunities for context-aware services and smart environment applications. Each module of the framework was individually tested and evaluated, demonstrating strong performance both in isolation and as part of the integrated system. Full article

Recently, high-precision WSN (wireless sensor network) ranging and positioning algorithms based on RSSI (Received Signal Strength Indicator) in complex indoor environments have become a popular research topic. This is because RSSI is easy to obtain and more suitable for the large-scale deployment of WSNs. However, WSN ranging and positioning algorithms using RSSI are severely affected by the presence of noise and multipath effects in complex indoor environments. To reduce multipath effects, a multi-channel ranging algorithm was developed. This algorithm must obtain accurate initial parameter values or the target–reference distance in advance; otherwise, it will fall into local optima. We propose an environment-adaptive algorithm for multi-channel ranging optimization based on an innovative evolutionary model with multiple objectives and an existing adaptive extended Kalman filter. This novel model includes a newly created objective function of the relationship between weighted multi-channel RSSI and node distance, which allows it to achieve globally optimal results without requiring extensive training to obtain accurate initial parameter values or the target–reference distance beforehand. Extensive simulations and experiments show that our proposed algorithm always has much higher ranging accuracy than the existing algorithm, regardless of whether the multi-channel RSSI is regular or the number of paths matches. Full article

In recent years, hidden IoT monitoring devices installed indoors have raised significant concerns about privacy breaches and other security threats. To address the challenges of detecting such devices, low positioning accuracy, and lengthy detection times, this paper proposes a hidden device detection and localization system that operates on the Android platform. This technology utilizes the Received Signal Strength Indication (RSSI) signals received by the detection terminal device to achieve the detection, classification, and localization of hidden IoT devices in unfamiliar environments. This technology integrates three key designs: (1) actively capturing the RSSI sequence of hidden devices by sending RTS frames and receiving CTS frames, which is used to generate device channel fingerprints and estimate the distance between hidden devices and detection terminals; (2) training an RSSI-based ranging model using the XGBoost algorithm, followed by multi-point localization for accurate positioning; (3) implementing augmented reality-based visual localization to support handheld detection terminals. This prototype system successfully achieves active data sniffing based on RTS/CTS and terminal localization based on the RSSI-based ranging model, effectively reducing signal acquisition time and improving localization accuracy. Real-world experiments show that the system can detect and locate hidden devices in unfamiliar environments, achieving an accuracy of 98.1% in classifying device types. The time required for detection and localization is approximately one-sixth of existing methods, with system runtime maintained within 5 min. The localization error is 0.77 m, a 48.7% improvement over existing methods with an average error of 1.5 m. Full article

We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just providing a point estimate for a given signal sequence, our model returns the distribution of possible positions as continuous probability density function, which allows for appropriate integration into recursive state estimation systems. The estimation procedure starts by using a kernel to compare incoming data with reference recordings from known positions. Based on the obtained similarities, weights are assigned to the reference positions. An arbitrarily chosen density estimation method is then applied given this assignment. Thus, a continuous representation of the distribution of possible positions in the environment is provided. We apply the solution in a Particle Filter (PF) system for smartphone-based indoor localization. The approach is tested both with radio signal strength (RSS) measurements (Wi-Fi and Bluetooth Low Energy RSSI) and round-trip time (RTT) measurements, given by Wi-Fi Fine Timing Measurement. Compared to distance-based models, which are dedicated to the specific physical properties of each measurement type, our similarity-based model achieved overall higher accuracy at tracking pedestrians under realistic conditions. Since it does not explicitly consider the physics of radio propagation, the proposed model has also been shown to work flexibly with either RSS or RTT observations. Full article

LoRa, as a leading LPWAN technology, plays a pivotal role in enabling long-range, low-power wireless communication, especially in agricultural IoT applications. This study examines the propagation characteristics of 433 MHz LoRa signals in maize fields, focusing on signal attenuation, RSSI, SNR, and packet loss under dense crop conditions. Field experiments were conducted in Wuwei, Gansu Province, with validation tests in Tongliao, Inner Mongolia. The effects of transmitter and receiver antenna heights on signal quality and propagation distance were systematically analyzed. Results show a consistent improvement in signal quality and range with increased antenna height. Path loss models were developed using regression analysis, achieving high predictive accuracy (R² > 0.9). Validation confirmed the models’ reliability, offering valuable insights for deploying wireless sensor networks (WSNs) in agriculture. Future research will integrate machine learning for dynamic modeling and explore variations across crop growth stages. Full article

Traditional received signal strength indicator (RSSI)-based and angle of arrival (AoA)-based positioning methods are highly susceptible to multipath effects, signal attenuation, and noise interference in complex indoor environments, which significantly degrade positioning accuracy. To mitigate the impact of the above deterioration, we propose a deep learning-based Bluetooth indoor positioning system, which employs a Kalman filter (KF) to reduce the angular error in AoA measurements and utilizes a median filter (MF) and moving average filter (MAF) to mitigate fluctuations in RSSI-based distance measurements. In the deep learning network architecture, we propose a convolutional neural network (CNN)–multi-head attention (MHA) model. During the training process, the backpropagation (BP) algorithm is used to compute the gradient of the loss function and update the parameters of the entire network, gradually optimizing the model’s performance. Experimental results demonstrate that our proposed indoor positioning method achieves an average error of 0.29 m, which represents a significant improvement compared to traditional RSSI and AoA methods. Additionally, it displays superior positioning accuracy when contrasted with numerous emerging indoor positioning methodologies. Full article

This study proposes an innovative indoor localization algorithm based on the base station identification and improved black kite algorithm–backpropagation (IBKA-BP) integration to address the problem of low positioning accuracy in agricultural robots operating in agricultural greenhouses and breeding farms, where the Global Navigation Satellite System is unreliable due to weak or absent signals. First, the density peaks clustering (DPC) algorithm is applied to select a subset of line-of-sight (LOS) base stations with higher positioning accuracy for backpropagation neural network modeling. Next, the collected received signal strength indication (RSSI) data are processed using Kalman filtering and Min-Max normalization, suppressing signal fluctuations and accelerating the gradient descent convergence of the distance measurement model. Finally, the improved black kite algorithm (IBKA) is enhanced with tent chaotic mapping, a lens imaging reverse learning strategy, and the golden sine strategy to optimize the weights and biases of the BP neural network, developing an RSSI-based ranging algorithm using the IBKA-BP neural network. The experimental results demonstrate that the proposed algorithm can achieve a mean error of 16.34 cm, a standard deviation of 16.32 cm, and a root mean square error of 22.87 cm, indicating its significant potential for precise indoor localization of agricultural robots. Full article

In response to the current key issues in the field of smart irrigation for farmland, such as the lack of data sources and insufficient integration, a low degree of automation in drive execution and control, and over-reliance on cloud platforms for analyzing and calculating decision making processes, we have developed nodes and gateways for smart irrigation. These developments are based on the EC-IOT edge computing IoT architecture and long range radio (LoRa) communication technology, utilizing STM32 MCU, WH-101-L low-power LoRa modules, 4G modules, high-precision GPS, and other devices. An edge computing analysis and decision model for smart irrigation in farmland has been established by collecting the soil moisture and real-time meteorological information in farmland in a distributed manner, as well as integrating crop growth period and soil properties of field plots. Additionally, a mobile mini-program has been developed using WeChat Developer Tools that interacts with the cloud via the message queuing telemetry transport (MQTT) protocol to realize data visualization on the mobile and web sides and remote precise irrigation control of solenoid valves. The results of the system wireless communication tests indicate that the LoRa-based sensor network has stable data transmission with a maximum communication distance of up to 4 km. At lower communication rates, the signal-to-noise ratio (SNR) and received signal strength indication (RSSI) values measured at long distances are relatively higher, indicating better communication signal quality, but they take longer to transmit. It takes 6 s to transmit 100 bytes at the lowest rate of 0.268 kbps to a distance of 4 km, whereas, at 10.937 kbps, it only takes 0.9 s. The results of field irrigation trials during the wheat grain filling stage have demonstrated that the irrigation amount determined based on the irrigation algorithm can maintain the soil moisture content after irrigation within the suitable range for wheat growth and above 90% of the upper limit of the suitable range, thereby achieving a satisfactory irrigation effect. Notably, the water content in the 40 cm soil layer has the strongest correlation with changes in crop evapotranspiration, and the highest temperature is the most critical factor influencing the water requirements of wheat during the grain-filling period in the test area. Full article

With the rapid advancement of technology, precision agriculture, as a modern agricultural production model, has seen significant progress in recent years. Its widespread adoption is gradually transforming traditional farming methods, providing strong support for the modernization of global agriculture. In particular, the application of positioning technology plays a crucial role in precision agriculture. This paper focuses on an automated agricultural machinery positioning system based on Bluetooth technology. The system uses Bluetooth at the 2.4 GHz frequency for transmission, processing Constant Tone Extension (CTE) and Received Signal Strength Indicator (RSSI) signals collected from blind nodes. The Propagator Direct Data Acquisition (PDDA) algorithm is employed to calculate angle information from CTE signals, while the Two-Ray Ground Reflection Model is applied to manage the correlation between RSSI and distance, making it suitable for outdoor environments. These two types of data are fused for positioning, with an optimized objective function converting the positioning task into an optimization problem. An Adaptive Secretary Bird Optimization Algorithm (ASBOA) is introduced to enhance the accuracy and efficiency of the positioning process. In the simulation, anchor and blind nodes are deployed to simulate a real farm environment. Anchor nodes receive CTE and RSSI signals from blind nodes. Considering that the tags mounted on agricultural machinery are set at a fixed height in real scenarios, the simulation also fixes the tags at this height. We then compare the accuracy of five algorithms in both static and dynamic tracking. The final simulation results indicate that ASBOA achieves satisfactory high-precision positioning, both for static points and dynamic tracking, theoretically meeting the needs for continuous positioning and laying a solid foundation for future field trials. Full article

As a range-free localization algorithm, DV-Hop has gained widespread attention due to its advantages of simplicity and ease of implementation. However, this algorithm also has some defects, such as poor localization accuracy and vulnerability to network topology. This paper presents a comprehensive analysis of the factors contributing to the inaccuracy of the DV-Hop algorithm. An improved proportional integral derivative (PID) search algorithm (PSA) DV-Hop hybrid localization algorithm based on weighted hyperbola (IPSA-DV-Hop) is proposed. Firstly, the first hop distance refinement is employed to rectify the received signal strength indicator (RSSI). In order to replace the original least squares solution, a weighted hyperbolic algorithm based on the degree of covariance is adopted. Secondly, the localization error is further reduced by employing the improved PSA. In addition, the selection process of the node set is optimized using progressive sample consensus (PROSAC) followed by a 3D hyperbolic algorithm based on coplanarity. This approach effectively reduces the computational error associated with the hopping distance of the beacon nodes in the 3D scenarios. Finally, the simulation experiments demonstrate that the proposed algorithm can markedly enhance the localization precision in both isotropic and anisotropic networks and reduce the localization error by a minimum of 30% in comparison to the classical DV-Hop. Additionally, it also exhibits stability under the influence of a radio irregular model (RIM). Full article

The application of foliar fertiliser can rapidly replenish the essential nutrients required by crops. In order to enhance the precision of foliar fertiliser spraying, fertiliser utilisation, and leaf absorption efficiency, this study proposes the implementation of an efficient foliar fertiliser dual-face target precision variable spraying robot system based on computer vision. In this study, we propose the SN-YOLOX Nano-ECA as a real-time classification model for potted plants. The model has parameters and FLOPs of only 0.48 M and 0.16 G, respectively. Following deployment, the classification precision and recall reached 97.86% and 98.52%, respectively, with an FPS of 37.6. A dual-face target precision variable spraying method of foliar fertiliser based on the determination of leaf area and plant height information of potted plants was proposed. A robot platform for the application of foliar fertilisers was developed, and a positioning and navigation system based on the RSSI principle was constructed. The results of the foliar fertiliser spraying experiments demonstrate that the precision of the extracted leaf area and height information is above 97% and 96%, respectively. The navigation system demonstrated distance and angle errors of only 5.598 cm and 0.2245°. The mean discrepancy between the actual and set spraying volumes was 0.46 mL. This robotic system is capable of precise spraying of foliar fertiliser, which provides a new idea and reference for the development of efficient and precise variable spraying technology for foliar fertiliser. Full article

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique. Full article

LoRa (long range) is a communication technology that employs chirp spread spectrum modulation. Among various low-power wide area network (LPWAN) technologies, LoRa offers unique advantages, including low power consumption, long transmission distance, strong anti-interference capability, and high network capacity. Addressing the issue of power-constrained end devices in IoT application scenarios, this paper proposes an adaptive data rate (ADR) algorithm for LoRa networks designed for power-constrained end devices (EDs). The algorithm evaluates the uplink communication link state between the EDs and the gateway (GW) by using a combined weighting method to comprehensively assess the signal-to-noise ratio (SNR), received signal strength indication (RSSI), and packet reception rate (PRR), and calculates a list of transmission power and data rates that ensure stable and reliable communication between the EDs and the GW. By using ED power consumption models, network throughput models, and ED latency models to evaluate network performance, the Zebra optimization algorithm is employed to find the optimal data rate for each ED under power-constrained conditions while maximizing network performance. Test results show that, in a single ED scenario, the average PRR achieved by the proposed ADR algorithm for power-constrained EDs in LoRa networks is 14% higher than that of the standard LoRaWAN ADR algorithm. In a multi-ED link scenario (50 end devices), the proposed method reduces the average power consumption of EDs by 10% compared to LoRaWAN ADR, achieves a network throughput of 6683 bps, and an average latency of 2.10 s, demonstrating superior performance overall. The proposed method shows unique advantages in LoRa networks with power-constrained EDs and a large number of EDs, as it not only reduces the average power consumption of the EDs but also optimizes network throughput and average latency. Full article

Leaf moisture content (LMC) directly affects the life activities of plants and becomes a key factor to evaluate the growth status of plants. To explore a low-cost, real-time, rapid, and accurate method for LMC detection, this paper employs Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) sensor technology. By reading the tag information attached to the back of leaves, the parameters of the RSSI, phase, and reading distance of the tags are collected. In this paper, we propose an enhanced Multi-Feature Fusion algorithm based on Hyperdimensional Computing (HDC) called MFFHDC. In our proposed method, the real-valued features are encoded into hypervectors and then combined with Multi-Linear Discriminant Analysis (MLDA) for the feature fusion of different features. Finally, a retraining method based on Cosine Annealing with Warm Restarts (CAWR) is proposed to improve the model and further enhance its accuracy. Tests conducted in the experimental forest show that the proposed mechanism can effectively predict the LMC. The model’s Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²) reached 0.0195, 0.0255, and 0.9131, respectively. Additionally, comparisons with other methods demonstrate that the presented system performs excellently in most aspects. As a lightweight model, this study shows great practical application value, particularly for the limited data volume and low hardware costs. Full article

Aiming at the problem that there is a big contradiction between accuracy and calculation and cost based on the RSSI positioning algorithm, an accurate and effective cooperative positioning algorithm is proposed in combination with error correction and refinement measures in each stage of positioning. At the ranging stage, the RSSI measurement value is converted to distance by wireless channel modeling and the dynamic acquisition of the power attenuation factor. Then, the ranging correction is carried out by using the known anchor node ranging error information. The Taylor series expansion least-square iterative refinement algorithm is implemented in the position optimization stage, and satisfactory positioning accuracy is obtained. The idea of cooperative positioning is introduced to upgrade the nodes that meet the requirements and are upgraded to anchor nodes and participate in the positioning of other nodes to improve the positioning coverage and positioning accuracy. The experimental results show that the localization effect of this algorithm is close to that of the Taylor series expansion algorithm based on coordinates but far higher than that of the basic least-squares localization algorithm. The positioning accuracy can be improved rapidly with the decrease in the distance measurement error. Full article