Search Results (361)

Power wireless sensor networks (PWSNs) provide essential field-level sensing and communication support for smart grids, where topology authenticity directly affects communication reliability and network operation. However, wormhole attacks can forge false adjacency relationships through low-latency tunnels, thereby disrupting topology consistency and misleading routing decisions. In practical power environments, metallic obstruction, multipath reflection, and non-line-of-sight (NLOS) propagation may further cause normal-ranging anomalies to resemble attack-induced topology distortion, making reliable wormhole attack detection challenging. To address this issue, this paper proposes a topology-stress-based wormhole attack defense method with ultra-wideband (UWB) physical-layer awareness. The first-path power ratio and root-mean-square delay spread extracted from UWB channel impulse responses are used to evaluate link-ranging reliability and construct adaptive stiffness coefficients. Local backbone links are modeled as virtual springs, and a topology stress indicator is derived from the residual deformation after potential-energy minimization to quantify the geometric inconsistency caused by forged adjacency relationships. Furthermore, a Beta-based temporal evidence fusion mechanism is introduced to support graded node access decisions and improve decision stability. Simulation and hardware validation results demonstrate that the proposed method effectively suppresses NLOS-induced false alarms while maintaining high sensitivity to wormhole attacks. Compared with representative baseline methods, it achieves more stable detection performance under increasing ranging errors and different attack intensities. Hardware experiments further show that topology stress can clearly distinguish normal links, NLOS-affected links, and forged wormhole links, confirming its effectiveness for topology-authenticity verification in power wireless sensor networks. Full article

This paper presents a simulation-based 16-quadrature amplitude modulation (16-QAM) direct current-biased optical orthogonal frequency-division multiplexing (DCO-OFDM) visible-light optical wireless system using a 520 nm InGaN directly modulated laser (DML) and direct detection over 500 m. A 1024-point transform with 511 data subcarriers provides approximately 15 Gb/s gross and 14.82 Gb/s payload rates without external optical modulators or amplifiers. Under the adopted static line-of-sight model, the simulated bit-error rate (BER) falls below ${10}^{-3}$ at a receiver-side equivalent optical signal-to-noise ratio (OSNR) of about 17 dB and remains below this threshold for beam divergence up to 9 mrad. Gamma–Gamma simulations show that a 5 cm aperture maintains $\mathrm{BER}<{10}^{-3}$ at 20 dB OSNR up to $C_n^2\approx 5\times {10}^{-14}{\mathrm{m}}^{-2/3}$. Pointing-error analysis gives per-axis angular-jitter standard deviations of 0.425, 0.515, and 0.564 mrad at 1% outage for 5, 10, and 15 cm apertures. The clear-air margin is exhausted at $V_{2\%}\approx 0.66\mathrm{km}$, corresponding to $V_{5\%}\approx 0.50\mathrm{km}$, or near 107 mm/h rain. For a 1.5 GHz bandwidth-limited DML, adaptive bit loading reaches 16.5 Gb/s at 28 dB OSNR. The results support a low-complexity medium-range architecture but remain numerical estimates requiring experimental validation under practical device, alignment, and weather conditions. Full article

Remote supervision of agricultural robots depends on continuous interpretation of robot status and wireless link quality. In smart greenhouses, crop canopies, metallic frames, cultivation rows, and non-line-of-sight propagation can cause intermittent packet loss and RSSI attenuation. Treating such transient degradation as immediate communication failure can interrupt robot operation unnecessarily, whereas delayed recognition of persistent loss can compromise safety. This study proposes a probabilistic communication-state inference method for remotely supervised agricultural robots. The robot-to-gateway wireless link is represented by three states: normal, degraded, and failure. The degraded state acts as an uncertainty buffer that preserves recoverable degradation before failure escalation. Packet reception ratio, received signal strength, and trajectory-derived context are used to update state probabilities through a bounded transition mechanism. Field experiments with a mobile agricultural robot in a smart greenhouse showed an accuracy of $0.915\pm 0.007$ and a macro F1-score of $0.907\pm 0.008$, while reducing the premature failure rate to $18.0\pm 1.4\%$. Comparisons with threshold-based, moving-average, and adapted WSN fault-detection baselines, including a FedLSTM-inspired baseline, showed that binary fault-detection logic cannot explicitly preserve recoverable degraded communication intervals. The results indicate that probabilistic degradation modeling supports communication-aware remote supervision by distinguishing transient degradation from failure-level communication loss. Full article

With the widespread adoption of smartphones, location-based services have become increasingly important. Consequently, accurate and reliable global satellite navigation system positioning on smartphones has become essential. However, achieving accurate positioning in urban areas remains challenging because of the inherent limitations of smartphones and severe multipath effects. To address this issue, this study proposes two methods to improve positioning accuracy using contextual information. First, an environmental context indicator was used to refine the C/N₀-based observation covariance model. Second, normalized C/N₀ and code-pseudorange residuals were used to detect non-line-of-sight satellites and adjust the observation covariance. Experiments were conducted in both open and urban areas, and performance was evaluated using circular error probable (CEP) and distance root mean square (DRMS). The experimental results showed that, in open areas, the proposed method achieved submeter to decimeter-level horizontal accuracy and precision. In semi-urban areas, CEP95, CEP50, and DRMS decreased by approximately 8, 2, and 4 m, respectively. In urban canyons, CEP95, CEP50, and DRMS decreased by approximately 15, 2, and 5 m, respectively. Full article

The work environment in automated laboratories and industrial sites exposes workers to the risks associated with chemical gas and vapor leaks caused by unforeseen incidents. Such leaks may result in severe health hazards as well as damage to equipment or infrastructure at the leak site. Therefore, the development of systems capable of early detection and highly accurate localization of chemical leaks is of high importance for occupational safety. In this work, a low-cost, portable sensor node based on the Internet of Things (IoT) is proposed for the detection and localization of gas and chemical leaks in indoor environments. The sensor node features a modular design that enables flexible integration and replacement of gas and environmental sensors depending on the target application. In addition, the system includes an ultra-wideband (UWB)-based positioning and tracking unit, allowing operation across multiple indoor zones. The main contribution of this work lies in the combined integration of (i) multi-sensor-based environmental event detection and prediction and (ii) high-precision location within a dynamic multi-zone tracking architecture. The system automatically selects the most relevant anchors in each zone and applies trilateration and least-squares estimation, enhanced by Kalman filtering techniques. In particular, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are employed, with sensor fusion incorporating inertial measurement unit (IMU) data to mitigate the effects of on-line-of-sight (NLoS) conditions and signal degradation caused by obstacles. Experimental results demonstrate that both the EKF and UKF significantly reduce positioning errors and improve tracking stability compared to baseline methods under challenging indoor conditions. The UKF shows superior performance in highly nonlinear scenarios. A quantitative evaluation using manually surveyed reference points showed that the UKF achieved the best overall performance, with a mean error of 39.72 cm and an RMSE of 43.03 cm. These findings confirm the effectiveness of Kalman filter-based sensor fusion for reliable indoor positioning and highlight the suitability of the proposed system for real-time safety monitoring applications. Full article

Non-Line-Of-Sight (NLOS) Terahertz (THz) radar 3D imaging leverages electromagnetic wave propagation characteristics such as reflection, diffraction, scattering, and penetration to detect, locate, and image hidden targets in occluded environments. It holds significant potential for applications in autonomous driving, disaster rescue, and urban warfare. However, uncertainties introduced by reflecting surfaces and occluding objects in practical NLOS scenarios, such as phase errors, aperture shadowing, and multipath effects, lead to issues like blurred imaging and increased artifacts in radar imaging. To address these challenges, this study proposes a 3D learning imaging method for NLOS THz radar based on a holographic imaging operator, leveraging the adaptive optimization properties of deep unfolding networks and prior environmental perception. First, a 3D imaging model for NLOS THz radar in the Looking Around Corner (LAC) scenario is established. A holographic imaging operator is introduced to enhance imaging efficiency and reduce computational complexity. Second, a high-precision NLOS 3D imaging network is constructed based on the Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) framework. Utilizing features specific to NLOS scenes and designing algorithm parameters as functions of network weights, the method achieves high-precision and high-efficiency in the 3D reconstruction of NLOS targets. Finally, a near-field NLOS radar imaging platform operating at 121 GHz (within the sub-THz regime) is developed. Experimental validations in the LAC scenario are performed on targets, including metal letters “E”, a metal resolution chart, and a pair of scissors. The results demonstrate that the proposed method significantly improves 3D imaging precision, achieving a two-orders-of-magnitude increase in computational speed over traditional imaging algorithms. Full article

Relative satellite navigation is critical for formation flying, rendezvous, and docking. This study augments a vision-based relative navigation framework with a robust multiplicative extended Kalman filter (RMEKF) that adaptively scales the measurement covariance using innovation-based covariance matching and a chi-square fault-detection test. A two-spacecraft scenario is simulated in which a deputy monocular camera observes six active beacons on a chief spacecraft. To evaluate fault tolerance, constant line-of-sight (LOS) errors are injected on two beacon measurements during a fixed interval. Over the fault-centered evaluation window, the RMEKF reduces attitude root mean square error (RMSE) by approximately 71–73% compared to the conventional multiplicative extended Kalman filter (MEKF), while also improving relative/orbital state accuracy by 19–93%. These results indicate improved robustness to LOS measurement faults without degrading overall estimation stability. Full article

To address the issues of information leakage risks faced by the base station (BS) when communicating with multiple users in an integrated sensing and communication (ISAC) system, as well as the blockage of the direct link between the BS and the target to be detected, which limits sensing functionality, this paper introduces the active intelligent reflecting surface (IRS) into the ISAC system. By creating a virtual line-of-sight (LoS) path, signal blockage is effectively mitigated, while the active IRS enhances the incident signal strength and adjusts the reflection phase shifts, thereby improving the reliability and security of communication. This paper proposes a joint optimization scheme for the active IRS-assisted ISAC system, which jointly designs the BS beamforming and the IRS reflection coefficient matrix. A non-convex optimization problem is formulated with the objective of maximizing the radar output signal-to-noise ratio (SNR) subject to communication performance constraints. To solve this problem, this paper employs an iterative algorithm based on alternating optimization (AO), fractional programming (FP), and semidefinite relaxation (SDR). Simulation results demonstrate that the proposed scheme significantly outperforms the benchmark schemes without IRS assistance and with passive IRS assistance in terms of enhancing the sensing performance of the ISAC system. Full article

As the number of elderly individuals living alone increases, the risk of fall-related accidents correspondingly rises, underscoring the need for rapid fall detection systems. Because falls are difficult to predict in terms of location, detection systems must be deployed in a distributed manner, which in turn requires compact and low-power implementations. Unlike camera sensors, radar sensors do not raise privacy concerns and are not limited by line-of-sight constraints. Moreover, compared with wearable sensors, radar enables continuous monitoring without user intervention. However, prior radar-based approaches incur high computational complexity, leading to increased power consumption and larger hardware area, thereby necessitating efficient hardware design. This paper proposes a lightweight fall detection system based on continuous-wave (CW) radar and a binarized convolutional neural network (BCNN). Radar signals are preprocessed using short-time Fourier transform (STFT) to generate binary spectrograms, which are then fed into a BCNN-based classification network. The proposed system performs binary classification of five fall activities and seven non-fall activities with an accuracy of 96.1%. The preprocessing module and classification network were implemented as hardware accelerators and integrated with a microprocessor in a system-on-chip (SoC) architecture on a field-programmable gate array (FPGA). Compared with the software implementation, the proposed hardware achieved speedups of 387.5× and 86.7× for the preprocessing and classification modules, respectively. Furthermore, the overall system processing time was 2.58 ms, corresponding to an 89.5× speedup over the software baseline. Full article

Many Internet of Things (IoT) applications that use LoRaWAN require node localization, often relying on signal strength or message timestamps to estimate distance. However, traditional techniques typically require prior knowledge of signal propagation models or clock synchronization between multiple nodes. Therefore, this paper proposes a one-way ranging method based on LoRa to estimate link distances using the received signal from a single node, with no additional infrastructure or synchronization requirements. The approach uses the inherent properties of the LoRa chirp-based waveform to extract time delay information and estimate distance. The proposed method consists of a transmitter and a receiver capable of detecting the link delay using demodulation of the preamble. Then, the method estimates the distance using the link delay without requiring additional hardware or information. The method was validated through MATLAB R2025a simulations, including five nodes distributed over an 18 km² area. The proposed method achieves distance estimation with mean errors of 25 m under semi-urban, non-line-of-sight conditions, outperforming existing methods. Additionally, the study identifies two practical system configurations for LoRa, at 8 Msps and 2 Msps, which reduce the ranging error while considering hardware feasibility. These findings are especially relevant for researchers developing Global Positioning System (GPS) free localization techniques in resource-constrained IoT environments. Full article

High-precision and reliable self-localization is essential for autonomous navigation systems. However, in urban canyons (urban environments with clusters of high-rise buildings), Global Navigation Satellite Systems (GNSS) suffer from severe multipath and Non-Line-of-Sight (NLOS) signal reception. This causes a theoretically unbounded positive bias in pseudorange measurements, significantly degrading positioning integrity. To address this challenge, this study proposes a novel GNSS/Inertial Measurement Unit (IMU) tightly coupled integrated navigation system using factor graph optimization (FGO) integrated with machine learning-based NLOS detection. To train the NLOS detection model, we utilized a dual-polarized antenna to label signals based on the strength difference between RHCP and LHCP components, achieving a detection accuracy of 0.89. A random forest classifier identifies NLOS signals, and based on its classification labels, the variance of the corresponding GNSS pseudorange factors within the FGO framework is dynamically inflated. This effectively mitigates the impact of outliers while preserving the graph topology. Experimental evaluations in dense urban environments demonstrated that the proposed method improves horizontal positioning accuracy by 84.8% compared to conventional standalone GNSS positioning. The dynamic integration of machine learning-based signal classification and tightly coupled FGO provides an extremely robust positioning solution, proven to meet the stringent reliability requirements demanded of autonomous systems even under severe signal obscuration. Full article

Detecting non-line-of-sight (NLOS) signals is essential for improving the accuracy and reliability of smartphone Global Navigation Satellite System (GNSS) positioning in dense urban areas. This paper presents a practical method for NLOS detection based on skymasks derived from smartphone observations. The observable rates of satellite observation series are first computed using precise ephemeris, and the observations are then classified into blocked and unblocked groups. A smoothing spline is then applied to fit the building boundary from the categorized series. Based on the fitted boundary, a skymask is constructed and used for NLOS detection. Datasets collected at three locations using three different smartphones are used for validation. The results show that both the number and proportion of NLOS signals decrease significantly after applying the proposed method. As the degree of obscuration increases, the detection accuracy remains stable across different smartphones. In some cases, single-point positioning accuracy is improved after excluding NLOS signals. In addition, the derived skymask can be used to estimate sky visibility and support the selection of positioning strategies. Overall, the proposed method can be combined with the consistency checking method for NLOS detection, as it does not require additional information. Full article

Active non-line-of-sight (NLOS) human detection aims to infer the presence of hidden individuals by analyzing indirectly reflected photons between a relay surface and occluded targets. In this study, a single-photon avalanche diode (SPAD) and time-correlated single-photon counting (TCSPC)-based acquisition system were used to measure time–photon waveforms in controlled NLOS environments designed to represent post-disaster rubble scenarios. Although the effective temporal resolution of the system is limited by the detector timing jitter and laser pulse width, the recorded transient signals retain distinguishable intensity and temporal delay patterns associated with the primary and secondary reflections. To construct a representative dataset, measurements were collected under varying subject poses, orientations, and surrounding object configurations. The recorded signals were processed using a unified preprocessing pipeline that included normalization, histogram shaping, and signal windowing. Three machine learning models, namely, Convolutional Neural Network, Gated Recurrent Unit, and Random Forest, were trained and evaluated for human presence classification. All models achieved full sensitivity in detecting human presence; however, notable differences emerged in the classification of human-absent scenarios. Among the tested approaches, random forest achieved the highest overall accuracy and specificity, demonstrating stronger robustness to statistical variations in time–photon histograms under limited photon conditions. These results suggest that tree-based classifiers capture amplitude distribution patterns and temporal dispersion characteristics more effectively than deep neural architectures under the present acquisition constraints. Overall, the findings indicate that low-cost SPAD-based NLOS sensing systems can provide reliable human detection in indirect-observation scenarios. Full article

The rapid integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace has introduced complex security challenges, particularly regarding the protection of critical infrastructure and personal privacy. While conventional detection mechanisms such as radar and optical sensors are widely deployed, they are frequently limited by line-of-sight obstructions and the small radar cross-section of modern commercial drones. Acoustic analysis presents a viable passive alternative; however, accurate three-dimensional localization remains a computationally demanding task, further complicated by the use of directional sensors with non-uniform sensitivity patterns. In this paper, a deep learning framework is proposed to address these ambiguities. The method involves the fusion of raw acoustic data with explicit sensor geometry metadata within a neural network architecture. To enhance localization precision, a composite loss function is introduced, which independently optimizes planar and altitude coordinates while penalizing outlier predictions. Experimental validation was conducted using a custom dataset of real-world drone flights, utilizing a distributed array of directional microphones. The results demonstrate that the proposed system effectively mitigates the spatial irregularities of ad hoc sensor deployment, achieving robust localization performance in complex acoustic environments. Full article

The Mw 6.4 Petrinja earthquake on 29 December 2020 ruptured the Petrinja-Pokupsko fault system in central Croatia, producing widespread coseismic deformation and subsequent postseismic processes. This study examines ground displacements in the Petrinja area from 2019 to 2022 using Sentinel-1 SAR data processed with SBAS time series analysis. Interferometric phase residuals were filtered using temporal coherence masking and RMS cut-off criteria to ensure high-quality displacement estimates. Line-of-sight (LOS) velocity fields were derived separately for ascending and descending tracks, combined into horizontal and vertical components, and rotated into a fault-parallel direction. Fault-parallel velocities were also extracted with pixel-wise coseismic offsets removed to isolate postseismic transients. Pre-event displacements are generally small and often within measurement uncertainties. However, because the 2019–2022 observation window includes the mainshock and concentrated early postseismic motion, robust estimation of long-term interseismic rates (millimeters per year) is not possible from this dataset. Such rates from independent regional GNSS measurements are therefore included solely for tectonic context and visual illustration. A clear surface displacement jump exceeding 20 cm was detected, with opposite signs in ascending and descending geometries, reflecting predominant right-lateral strike-slip motion. Following the removal of the coseismic jump, weighted profile analysis identifies residual transients of up to ±1.5 cm/yr near the fault, consistent with dominant shallow afterslip. Possible contributions from viscoelastic relaxation are noted, as such processes produce broader, longer-timescale deformation patterns that cannot be excluded without extended observations or forward modeling. These geodetic observations quantify the immediate postseismic deformation and provide constraints on near-fault slip patterns following the mainshock. Full article