Received Signal Strength Research Articles

Device‐Free Detection of Pedestrian and Movement Direction Using Bluetooth Direction Finding Function

From Bluetooth 5.1, a direction finding function has been added that allows the receiver to measure the angle of arrival (AoA) of the wireless signal from the sender. In this letter, we propose a method for detecting pedestrians and their movement direction passing between Bluetooth devices equipped with direction finding functions. As a result of the evaluation, pedestrians could be detected with higher accuracy (F‐score 0.929) by combining received signal strength indicator and AoA than when using each alone. In addition, the accuracy rate for estimating the movement direction using AoA was 0.704. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Wireless UV collaborative RSSI and the AOA hybrid localization method for UAV swarms

Wireless ultraviolet (UV) light can guarantee the inter-copter positioning accuracy of cluster-flying unmanned aerial vehicles (UAVs) in an electromagnetic confrontation environment. By combining wireless UV received signal strength indication (RSSI) localization and the angle of arrival (AOA) localization algorithm, a UV-hybrid localization method is proposed for UV communication collaboration between UAV swarms. The method collects the UV signal strength between the anchor UAV node and the unknown UAV node to obtain the inter-aircraft distance information, establishes a Gaussian hybrid noise model based on semi-definite relaxation, and uses the UV angle of the arrival estimation to solve the angle between the UAVs to achieve the maximum likelihood estimation of node positions in UAV swarms. A simulation comparison of the UV-hybrid localization algorithm, weighted least squares, and the Gaussian hybrid semi-definite planning localization algorithm is carried out. The results show that the performance of the UV-hybrid localization algorithm is close to the Cramer–Rao lower bound, and the average localization error is reduced by 32.9% and 15.6% compared to weighted least squares and Gaussian hybrid semi-definite planning algorithms; the algorithm of this paper achieves the node localization with fewer iterations, and it has a higher accuracy and efficiency of localization than the other algorithms.

In-Home Positioning for Remote Home Health Monitoring in Older Adults: Systematic Review

Background With the growing proportion of Canadians aged >65 years, smart home and health monitoring technologies may help older adults manage chronic disease and support aging in place. Localization technologies have been used to support the management of frailty and dementia by detecting activities in the home. Objective This systematic review aims to summarize the clinical evidence for in-home localization technologies, review the acceptability of monitoring, and summarize the range of technologies being used for in-home localization. Methods The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was followed. MEDLINE, Embase, CINAHL, and Scopus were searched with 2 reviewers performing screening, extractions, and quality assessments. Results A total of 1935 articles were found, with 36 technology-focused articles and 10 articles that reported on patient outcomes being included. From moderate- to high-quality studies, 2 studies reported mixed results on identifying mild cognitive dementia or frailty, while 4 studies reported mixed results on the acceptability of localization technology. Technologies included ambient sensors; Bluetooth- or Wi-Fi–received signal strength; localizer tags using radio frequency identification, ultra-wideband, Zigbee, or GPS; and inertial measurement units with localizer tags. Conclusions The clinical utility of localization remains mixed, with in-home sensors not being able to differentiate between older adults with healthy cognition and older adults with mild cognitive impairment. However, frailty was detectable using in-home sensors. Acceptability is moderately positive, particularly with ambient sensors. Localization technologies can achieve room detection accuracies up to 92% and linear accuracies of up to 5-20 cm that may be promising for future clinical applications. Trial Registration PROSPERO CRD42022339845; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=339845

Machine Learning Based Localization of LoRa Mobile Wireless Nodes Using a Novel Sectorization Method

Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors.

A 2.4 GHz IEEE 802.15.4 Multi-Hop Network for Mountainous Forest and Watercourse Environments: Sensor Node Deployment and Performance Evaluation

In this paper, we demonstrate the realistic test of a 2.4 GHz multi-hop wireless network for mountainous forest and watercourse environments. A multi-hop network using IEEE 802.15.4 XBee3 micro-modules and a communication protocol among nodes were developed. A wireless node deployment solution was introduced for practical testing. The proposed system’s communication reliability was tested in two different scenarios: a mountainous forest with sloping areas and trees and a watercourse, which referred to environmental and flooding monitoring applications. Wireless network performances were evaluated through the received signal strength indicator (RSSI) level of each wireless link, a packet delivery ratio (PDR), as the successful rate of packet transmission, and the end-to-end delay (ETED) of all data packets from the transmitter to the receiver. The experimental results demonstrate the success of the multi-hop WSN deployment and communication in both scenarios, where the RSSI of each link was kept at the accepted level and the PDR achieved the highest result. Furthermore, as a real-time response, the data from the source could be sent to the sink with a small ETED.

Indoor Positioning Method by CNN-LSTM of Continuous Received Signal Strength Indicator

This paper proposes an indoor positioning method based on Bluetooth Low Energy signals by Convolution Neural Network-Long Short-Term Memory (CNN-LSTM). The proposed method determines a receiver location based on distances from adjacent transmitters. The CNN-LSTM model estimates the distance from each transmitter using continuous signal strengths. To train and validate the model, the signal strengths are collected in several locations within various indoor environments. The positioning technique is adaptively selected based on the highest signal strength to avoid the interfering problem due to an excessively strong signal. If the signal strength exceeds a certain threshold, the location is determined using the proximity technique, which utilizes only the strongest signal instead of triangulation. In the experimental results, the proposed method demonstrated an average error of about 2.90 m, which is 34.2% better than a triangulation-based positioning method that does not utilize neural networks.

Exploring Ground Reflection Effects on Received Signal Strength Indicator and Path Loss in Far-Field Air-to-Air for Unmanned Aerial Vehicle-Enabled Wireless Communication

Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection.

A Precise and Scalable Indoor Positioning System Using Cross-Modal Knowledge Distillation.

User location has emerged as a pivotal factor in human-centered environments, driving applications like tracking, navigation, healthcare, and emergency response that align with Sustainable Development Goals (SDGs). However, accurate indoor localization remains challenging due to the limitations of GPS in indoor settings, where signal interference and reflections disrupt satellite connections. While Received Signal Strength Indicator (RSSI) methods are commonly employed, they are affected by environmental noise, multipath fading, and signal interference. Round-Trip Time (RTT)-based localization techniques provide a more resilient alternative but are not universally supported across access points due to infrastructure limitations. To address these challenges, we introduce DistilLoc: a cross-knowledge distillation framework that transfers knowledge from an RTT-based teacher model to an RSSI-based student model. By applying a teacher-student architecture, where the RTT model (teacher) trains the RSSI model (student), DistilLoc enhances RSSI-based localization with the accuracy and robustness of RTT without requiring RTT data during deployment. At the core of DistilLoc, the FNet architecture is employed for its computational efficiency and capacity to capture complex relationships among RSSI signals from multiple access points. This enables the student model to learn a robust mapping from RSSI measurements to precise location estimates, reducing computational demands while improving scalability. Evaluation in two cluttered indoor environments of varying sizes using Android devices and Google WiFi access points, DistilLoc achieved sub-meter localization accuracy, with median errors of 0.42 m and 0.32 m, respectively, demonstrating improvements of 267% over conventional RSSI methods and 496% over multilateration-based approaches. These results validate DistilLoc as a scalable, accurate solution for indoor localization, enabling intelligent, resource-efficient urban environments that contribute to SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities).

Optimizing Communication for a Humanoid Subs-Talker Robot Using RSSI Analysis on ROS System

This study aims to optimize communication for the humanoid "Subs-Talker" robot using Receive Signal Strength Indicator (RSSI) analysis within the Robot Operating System (ROS). The background of this research highlights the importance of stable and strong inter-robot communication in the Indonesian Humanoid Robot Soccer Contest (KRSBI-H), where robots need to function in synergy. The method involves measuring signal strength at specific distances, both with and without the use of an extender device, utilizing the InSSIDer application for RSSI data recording and analysis. The results show that using an extender improves communication stability, especially at longer distances (7.2 and 9.0 meters), although the impact is minimal at closer distances. The discussion suggests that an extender can be an effective solution for enhancing inter-robot communication quality in competitions. In conclusion, the extender significantly improves the communication range and signal quality among robots, supporting optimal performance in collaborative robot interactions within the competition arena.

WSN Localization in Smart Cities Using Hybrid (TDOA & RSSI) Localization Techniques with Extended Kalman Filter

In the context of smart cities, localization technologies are essential for tracking objects in both indoor and outdoor environments. The characteristics of urban settings such as building density, signal interference, and multipath propagation significantly impact the accuracy of localization algorithms. This paper reviews existing localization techniques, categorizing them into range-based and range-free methods, and discusses key algorithms including trilateration, multilateration, and triangulation. We propose a hybrid localization framework that combines Time Difference of Arrival (TDOA) and Received Signal Strength Indicator (RSSI) techniques, enhanced by an Extended Kalman Filter (EKF) for improved accuracy and robustness. Additionally, we present a design architecture for a transmitter and receiver system utilizing Long Range (LoRa) technology, facilitating the development of low-cost, low-power tracking devices that operate independently of Global Positioning System (GPS). Our approach aims to enhance the efficacy of localization in smart city applications, ultimately contributing to improved urban management and safety.

Construction of a Wi-Fi System with a Tethered Balloon in a Mountainous Region for the Teleoperation of Vehicular Forestry Machines

In this study, a Wi-Fi system with a tethered balloon is proposed for the teleoperation of vehicular forestry machines. This system was developed to establish a Wi-Fi communication for stable teleoperation in a timber harvesting site. This system consisted of a helium balloon, Wi-Fi nodes, a measurement system, a global navigation satellite system (GNSS) antenna, and a wind speed sensor. The measurement system included a GNSS module, an inertial measurement unit (IMU), a data logger, and an altitude sensor. While the helium balloon with the Wi-Fi system was 60 m in the air, the received signal strength indicator (RSSI) was measured by moving a Wi-Fi receiver on the ground. Another GNSS set was also utilized to collect the latitude and longitude data from the Wi-Fi receiver as it traveled. The developed Wi-Fi system with a tethered balloon can create a Wi-Fi zone of up to 1.9 ha within an average wind speed range of 2.2 m/s. It is also capable of performing the teleoperation of vehicular forestry machines with a maximum latency of 185.7 ms.

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results.

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.

Detecting and Localizing Wireless Spoofing Attacks on the Internet of Medical Things

This paper proposes a hybrid approach using design science research to identify rogue RF transmitters and locate their targets. We engineered a framework to identify masquerading attacks indicating the presence of multiple adversaries posing as a single node. We propose a methodology based on spatial correlation calculated from received signal strength (RSS). To detect and mitigate wireless spoofing attacks in IoMT environments effectively, the hybrid approach combines spatial correlation analysis, Deep CNN classification, Elliptic Curve Cryptography (ECC) encryption, and DSRM-powered attack detection enhanced (DADE) detection and localization (DAL) frameworks. A deep neural network (Deep CNN) was used to classify trusted transmitters based on Python Spyder3 V5 and ECC encrypted Hack RF Quadrature Signals (IQ). For localizing targets, this paper also presents DADE and DAL frameworks implemented on Eclipse Java platforms. The hybrid approach relies on spatial correlation based on signal strength. Using the training methods of Deep CNN1, Deep CNN2, and Long Short-Term Memory (LSTM), it was possible to achieve accuracies of 98.88%, 95.05%, and 96.60% respectively.

Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks

We present DeepIA, a deep neural network (DNN) framework for fast and reliable initial access (IA) for artificial intelligence (AI)-driven 6G millimeter wave (mmWave) networks. DeepIA reduces the beam sweep time compared to a conventional exhaustive search-based IA process by utilizing only a subset of the available beams. DeepIA maps received signal strengths (RSSs) obtained from a subset of beams to the beam that is best oriented to the receiver. In both line of sight (LoS) and non-line of sight (NLoS) conditions, DeepIA reduces the IA time and outperforms the conventional IA&#x0027;s beam prediction accuracy. We show that DeepIA&#x0027;s accuracy saturates with the number of beams used, and also depends on the particular choice of the beams used. The choice of beams that are selected is consequential and improves accuracy by upto upto <inline-formula><tex-math notation="LaTeX">$35\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$70\%$</tex-math></inline-formula> in NLoS and LoS channels. We find that, averaging multiple RSS snapshots further reduces the number of beams needed and achieves more than <inline-formula><tex-math notation="LaTeX">$95\%$</tex-math></inline-formula> accuracy in both LoS and NLoS conditions. We introduce interference into our models to understand impact on performance. Finally, we evaluate the beam prediction time of DeepIA through embedded hardware implementation and show the improvement over the baseline approach.

Last-seen Time is Critical: Revisiting RSSI-based WiFi Indoor Localization

Received Signal Strength Indicator (RSSI)-based WiFi indoor localization has been widely applied in various applications. However, through an empirical study, we have identified several issues in existing methodologies that degrade system performance and localization accuracy due to the misinterpretation of local and last-seen timestamps. To address these issues, we revisit existing RSSI-based approaches and introduce a novel processing pipeline. This pipeline efficiently filters out-of-date RSSI data while maintaining data freshness, which is crucial for enhancing localization accuracy. We further propose an Access Point (AP) filling approach to handle scenarios where selected APs may be unavailable during the online phase. Additionally, we design a new AP selection strategy based on the Wasserstein distance to optimize system performance. Experimental evaluations using self-collected and open-source datasets demonstrate a significant reduction in Mean Positioning Error (MPE) and non-matching rates compared to existing approaches.

Optical parameter design model of hemispherical laser retroreflector arrays for satellite laser ranging.

A laser retroreflector array (LRA) with multiple retroreflectors is used to directionally reflect an incident laser beam and aims to enhance the received signal strength into laser ranging ground stations. A comprehensive mathematical equation of the received photons is established by modelling the LRA far field diffraction intensity (FFDI) and the satellite velocity aberration position. Based on this received photon model, a novel method on designing the dihedral angle offset and the aperture of retroreflectors is proposed, which maximizes the received photon under the minimal elevation angle of the ground station. Taking the hemispherical structure parameters of Chinese HY-2 satellite LRA and the specification parameters of the Shanghai Astronomical Observatory, the optimal aperture and the dihedral angle offset of the HY-2 laser retroreflectors are 17 mm and 1.6″, respectively. These two optimal optical parameters make the received photon under the elevation angle of 0° reach ∼0.9 count per laser shot. Until now, ∼851,400 successful observations have been successfully obtained by global ground stations since the first launch of HY-2 satellite series in 2011, which guarantees the high-precise orbit determination of HY-2 satellite series.

Integration of an IoT sensor with angle-of-arrival-based angle measurement in AGV navigation: A reliability study

Automated guided vehicle (AGV), which was initially designed for indoor operations in industry, has been increasingly applied in outdoor heavy-duty logistics tasks. In typical navigation tasks, such as the autonomous tracking of a designated object or a person, relative angle and relative distance between AGV and the target is required. To obtain the necessary information, various on-board sensors are extensively integrated. In this paper, the reliability of measuring the relative angle with the Angle-of-Arrival (AoA) method with two different Internet of Things (IoT) sensor sets from Texas Instrument (TI) and u-blox, according to Bluetooth 5.1 was investigated. The performance of IoT sensors was validated with angle accuracy parameters and received signal strength indicator (RSSI). The better IoT sensor was then integrated into the AGV navigation system, and the information gathered from IoT sensor enabled the AGV to turn toward the direction of the target. The process of AGV turning to the targeted direction based on IoT sensor information was respectively tested in the simulation and actual environment and evaluated by the disparity between the real relative angle and the rotation angle of the AGV. The results showed that this disparity was within ±5° in both simulated and actual environments, and methods for higher accuracy were proposed. In this way, the reliability and performance of Angle of Arrival (AoA) sensors in measuring the relative angle, which remains unexplored by other researchers, was systematically assessed contributing to extending the usability of AoA sensors in complex, real-world applications.

Research on Rate Adaptation of Underwater Optical Communication with Joint Control of Photoelectric Domain

As the communication distance changes, the received signal strength of an underwater optical communication system will change, and the range of its variation may not only exceed the dynamic range of the photoelectric detection device but also cause the reliability of communication to change due to the change in the received signal-to-noise ratio. In order to maintain better communication over a longer distance, this paper proposes a rate-adaptive method for underwater optical communication with joint control in the photoelectric domain. In the optical domain, the incident light’s power is adaptively adjusted by controlling the transmittance of the liquid crystal light valve to reduce saturation distortion. In the electrical domain, the constellation distribution is optimized according to the desired probability mass function, and the modulation order is adjusted in real time by estimating the received signal-to-noise ratio of the link. The simulation results show that under the forward error correction (FEC) threshold, the proposed method increases the dynamic range of the photomultiplier tube (PMT) by about 10 dB and expands the dynamic range of the system’s communication distance.

A fingerprint dictionary processing approach in indoor localization system based on wi-fi

Indoor localization using Wi-Fi fingerprinting based on Received Signal Strength (RSS) has gained widespread attention due to its immunity to external factors and ability to penetrate obstacles. The localization process involves an offline phase for building a radio map and an online phase for matching location queries. Existing matching algorithms often prioritize enhancing online phase accuracy, overlooking the importance of offline data preprocessing, which can negatively impact overall performance. This study introduces a novel approach called Fingerprint Dictionary Preprocessing (FDP) that employs Convolutional Dictionary Learning (CDL) to process radio map data. CDL learns a set of kernels capturing site characteristics, representing RSS values from Access Points (APs) in a sparse manner. The proposed FDP system compresses data through feature learning, reducing storage requirements for data transmission. In the online phase, CDL is utilized for assisting matching fingerprints against the learned dictionary, accurately locating users. The contributions of the FDP system presenting a cost-effective and practical solution for indoor localization, addressing the challenges associated with large data collection and multi-dimensional data requirements, making it a promising approach for real-world applications. We conducted experiments in two real indoor environments, and the results indicated that the proposed FDP system, whether applied to the original radio map or the preprocessed fingerprint database, led to improved localization accuracy and reduced localization time.

A fast and stable calibration method for VLP system without any geometric measurement

Abstract. Visible light positioning (VLP) is one of the most promising technologies for providing high-precision, low-cost indoor positioning and navigation services. However, the traditional calibration methods of VLP are complex and unfriendly to users, which hinders the large-scale commercial deployment of VLP systems. In this letter, a fast and stable calibration method for Lambert model in the received signal strength (RSS)-based VLP system is proposed, which dispenses with geometric measurement and greatly simplifies the calibration procedures. The proposed method calibrates the Lambert model through two steps, firstly using a ratio method to calibrate the Lambert order, and then estimating the constant term. The actual processes only require moving a robot equipped with a photo-detector (PD) along a rectangular trajectory once, and then the program will automatically estimate the required parameters by analyzing the RSS during this period. Experimental results show a good stability of the calibrated parameters, as well as a excellent distance measuring accuracy within 12 cm. And the entire processes, including signal collection and processing, only take a total of no more than four minutes.