Search Results (1,753)

In indoor environments, fusion localization methods that combine Wi-Fi fingerprinting and Pedestrian Dead Reckoning (PDR) are constrained by the high sensitivity of traditional filters, such as the Extended Kalman Filter (EKF), to initial states and by their susceptibility to nonlinear drift. This study presents a Wi-Fi/PDR fusion localization approach based on global geometric alignment optimized via a Genetic Algorithm (GA). The proposed method models the PDR trajectory as an integrated geometric entity and performs a global search for the optimal two-dimensional similarity transformation that aligns it with discrete Wi-Fi observations, thereby eliminating dependence on precise initial conditions and mitigating multipath noise. Experiments conducted in a real office environment (14 × 9 m, eight dual-band APs) with a double-L trajectory demonstrate that the proposed GA fusion achieves the lowest mean error of 0.878 m (compared to 2.890 m, 1.277 m, and 1.193 m for Wi-Fi, PDR, and EKF fusion, respectively) and an RMSE of 0.978 m. It also attains the best trajectory fidelity (DTW = 0.390 m, improving by 71.0%, 14.7%, and 27.8%) and the smallest maximum deviation (Hausdorff = 1.904 m, 52.4% lower than Wi-Fi). The cumulative error distribution shows that 90% of GA fusion errors are within 1.5 m, outperforming EKF and PDR. Additional experiments that compare the proposed GA optimizer with Levenberg–Marquardt (LM), particle swarm optimization (PSO), and Procrustes alignment, as well as tests with 30% artificial Wi-Fi outliers, further confirm the robustness of the Huber-based cost and the effectiveness of the global optimization framework. These results indicate that the proposed GA-based fusion method achieves high robustness and accuracy in the tested office-scale scenario and demonstrate its potential as a practical multi-sensor fusion approach for indoor localization. Full article

Indoor positioning using commodity WiFi has gained significant attention; however, achieving sub-meter accuracy across diverse layouts remains challenging due to multipath fading and Non-Line-Of-Sight (NLOS) effects. In this work, we propose a hybrid Graph–Temporal Convolutional Network (GTCN) model that incorporates Access Point (AP) geometry through graph convolutions while capturing temporal signal dynamics via dilated temporal convolutional networks. The proposed model adaptively learns per-AP importance using a lightweight gating mechanism and jointly exploits WiFi Received Signal Strength (RSS) and Round-Trip Time (RTT) features for enhanced robustness. The model is evaluated across four experimental areas such as lecture theatre, office, corridor, and building floor covering areas from 15 m × 14.5 m to 92 m × 15 m. We further analyze the sensitivity of the model to AP density under both LOS and NLOS conditions, demonstrating that positioning accuracy systematically improves with denser AP deployment, especially in large-scale mixed environments. Despite its high accuracy, the proposed GTCN remains computationally lightweight, requiring fewer than ${10}^5$ trainable parameters and only tens of MFLOPs per inference, enabling real-time operation on embedded and edge devices. Full article

This paper presents an advanced signal processing framework for multi-channel forward-scatter radar (MC-FSR) systems based on the Multiple Signal Classification (MUSIC) algorithm. The proposed framework addresses the inherent limitations of FFT-based space-domain processing, such as limited angular resolution and the poor detectability of weak or closely spaced targets, which become particularly severe in low-cost FSR systems, which are typically operated with small antenna arrays. The MUSIC algorithm is adapted to operate on real-valued data obtained from the non-coherent, amplitude-based MC-FSR approach by reformulating the steering vectors and adjusting the degrees of freedom (DoFs). While compatible with arbitrary transmitting waveforms, particular emphasis is placed on Orthogonal Frequency Division Multiplexing (OFDM) signals, which are widely used in modern communication systems such as Wi-Fi and cellular networks. An analysis of the OFDM waveform’s autocorrelation properties is provided to assess their impact on target detection, including strategies to mitigate rapid target signature decay using a sub-band approach and to manage signal correlation through spatial smoothing. Simulation results, including multi-target scenarios under constrained array configurations, demonstrate that the proposed MUSIC-based approach significantly enhances angular resolution and enables reliable discrimination of closely spaced targets even with a limited number of receiving channels. Experimental validation using an S-band MC-FSR prototype implemented with software-defined radios (SDRs) and commercial Wi-Fi antennas, involving cooperative targets like people and drones, further confirms the effectiveness and practicality of the proposed method for real-world applications. Overall, the proposed MUSIC-based MC-FSR framework exhibits strong potential for implementation in low-cost, hardware-constrained environments and is particularly suited for emerging Integrated Sensing and Communication (ISAC) systems. Full article

The Global Positioning System (GPS) and Received Signal Strength Indicator (RSSI) usage for location provenance often fails in obstructed, noisy, or densely populated urban environments. This study proposes a passive location provenance method that uses the location’s acoustics and the device’s acoustic side channel to address these limitations. With the smartphone’s internal microphone, we can effectively capture the subtle vibrations produced by the capacitors within the voltage-regulating circuit during wireless transmissions. Subsequently, we extract key features from the resulting audio signals. Meanwhile, we record the RSSI values of the WiFi access points received by the smartphone in the exact location of the audio recordings. Our analysis reveals a strong correlation between acoustic features and RSSI values, indicating that passive acoustic emissions can effectively represent the strength of WiFi signals. Hence, the audio recordings can serve as proxies for Radio-Frequency (RF)-based location signals. We propose a location-provenance framework that utilizes sound features alone, particularly the Mel-Frequency Cepstral Coefficients (MFCCs), achieving coarse localization within approximately four kilometers. This method requires no specialized hardware, works in signal-degraded environments, and introduces a previously overlooked privacy concern: that internal device sounds can unintentionally leak spatial information. Our findings highlight a novel passive side-channel with implications for both privacy and security in mobile systems. Full article

This paper presents the development and validation of an IoT-enabled temperature-sensing device for real-time monitoring of the thermal insulation properties of glass windows. The system integrates contact and non-contact temperature sensors into a compact PCB platform equipped with WiFi connectivity, enabling seamless integration into smart home and building management frameworks. By continuously assessing window insulation performance, the device addresses the challenge of energy loss in buildings, where glazing efficiency often degrades over time. The collected data can be transmitted to cloud-based services or local IoT infrastructures, allowing for advanced analytics, remote access, and adaptive control of heating, ventilation, and air-conditioning (HVAC) systems. Experimental results demonstrate the accuracy and reliability of the proposed system, confirming its potential to contribute to energy conservation and sustainable living practices. Beyond energy efficiency, the device provides a scalable approach to environmental monitoring within the broader future internet ecosystem, supporting the evolution of intelligent, connected, and human-centered living environments. Full article

This paper describes a compact low-cost telemetry system featuring ready-made sensors and an acquisition unit based on the ESP32, which makes use of the LoRa/Wi-Fi wireless standard for communication, and autonomous fallback logging to guarantee data recovery during communication loss. Ensuring safe atmospheric re-entry requires reliable onboard monitoring of capsule conditions during descent. The system is intended for sub-orbital, low-cost educational capsules and experimental atmospheric descent missions rather than full orbital re-entry at hypersonic speeds, where the environmental loads and communication constraints differ significantly. The novelty of this work is the development of a fully self-contained telemetry system that ensures continuous monitoring and fallback logging without external infrastructure, bridging the gap in compact solutions for CubeSat-scale capsules. In contrast to existing approaches built around UAVs or radar, the proposed design is entirely self-contained, lightweight, and tailored to CubeSat-class and academic missions, where costs and infrastructure are limited. Ground test validation consisted of vertical drop tests, wind tunnel runs, and hardware-in-the-loop simulations. In addition, high-temperature thermal cycling tests were performed to assess system reliability under rapid temperature transitions between −20 °C and +110 °C, confirming stable operation and data integrity under thermal stress. Results showed over 95% real-time packet success with full data recovery in blackout events, while acceleration profiling confirmed resilience to peak decelerations of ~9 g. To complement telemetry, the TeleCapsNet dataset was introduced, facilitating a CNN recognition of descent states via 87% mean Average Precision, and an F1-score of 0.82, which attests to feasibility under constrained computational power. The novelty of this work is twofold: having reliable dual-path telemetry in real-time with full post-mission recovery and producing a scalable platform that explicitly addresses the lack of compact, infrastructure-independent proposals found in the existing literature. Results show an independent and cost-effective system for small re-entry capsule experimenters with reliable data integrity (without external infrastructure). Future work will explore AI systems deployment as a means to prolong the onboard autonomy, as well as to broaden the applicability of the presented approach into academic and low-resource re- entry investigations. Full article

Accurate indoor floor classification is essential for wireless positioning systems. However, the performance of conventional received signal strength indictor (RSSI)-based fingerprinting approaches is often limited by signal fluctuations and insufficient feature representation. To address these challenges, this paper introduces a heterogeneous graph neural network (GNN) framework that models WiFi signals using two types of nodes: reference points and Media Access Control (MAC) address. The edges between reference points and MAC addresses are weighted by normalized RSSI values, allowing the model to capture signal strength interactions and perform relation-aware message passing. Through this graph-based representation, the model can learn spatial and signal dependencies more effectively than conventional vector-based approaches. The proposed model was extensively evaluated under both benchmark and practical settings. On small-scale datasets, it achieved performance comparable to that of a conventional convolutional neural network trained on large-scale datasets, confirming its effectiveness with limited samples. In addition, the proposed model consistently outperformed other models under noisy conditions, achieving 93.88% accuracy on the widely used UJIIndoorLoc dataset and 97.3% accuracy in real-time experiments conducted at a test site. These values are significantly higher than those achieved using conventional machine learning (ML) baselines, highlighting the ability of the proposed model to handle real-world signal variations. These findings highlight that the heterogeneous GNN effectively captures spatial and signal-level dependencies, offering a robust and scalable solution for accurate indoor floor classification. Overall, this work presents a promising pathway for improving the performance and reliability of future wireless positioning systems. Full article

With the increasing demand for indoor location-based services, magnetic-fingerprint-based positioning has emerged as a promising complementary solution in scenarios lacking WiFi coverage. However, the dynamic nature of indoor environments, architectural complexity, and variations in pedestrian walking speeds can lead to stretching, compression, and distortion of magnetic fingerprint sequences, making it challenging for traditional sequence-matching algorithms to maintain stable positioning performance. To address these challenges, this paper proposes a magnetic-fingerprint-based positioning model that integrates residual networks (ResNet), transformer, and LSTM, referred to as Res-T-LSTM. Within the overall architecture, the ResNet module extracts deep local spatial features of magnetic fingerprints, and its residual connections effectively mitigate gradient attenuation during deep network training. The transformer module leverages self-attention mechanisms to model long-range dependencies and global contextual information, adaptively emphasizing key magnetic variations to enhance the discriminability of the feature representations. The LSTM module further captures the dynamic temporal evolution of magnetic sequences, improving robustness to variations in walking speed and sequence stretching or compression. Experimental results show that the proposed model achieves excellent performance across four smartphone-carrying postures, yielding an average positioning error of 0.21 m. Full article

This article presents a miniaturized dual-band frequency selective surface (FSS) based on capacitance-enhancing technique for RF shielding applications. The FSS incorporates two independent corner-modified square loop (CMSL) elements realized on a lossy dielectric, effectively suppressing the WiFi 2.45 GHz and WLAN 5.5 GHz bands simultaneously. The capacitance of FSS element is enhanced through corner truncation without using additional lumped elements. The symmetric geometry enables the FSS shield to manifest angularly stable and polarization-insensitive spectral responses under various oblique incident angles. Moreover, an equivalent circuit model (ECM) of the FSS structure is designed. A finite FSS prototype is fabricated and tested to verify the EM simulations. The measured results are in good agreement with the simulated responses. More importantly, the proposed design is scalable to other frequencies and is capable of selectively mitigating electromagnetic interference or confine the EM fields. Full article

Over-the-air (OTA) firmware updating has become a fundamental requirement in modern Internet of Things (IoT) deployments, where thousands of heterogeneous embedded devices operate in remote and distributed environments. Manual firmware maintenance in such systems is impractical, costly, and prone to security risks, making automated update mechanisms essential for long-term reliability and lifecycle management. This paper presents a unified OTA update architecture for ESP32-based IoT devices that integrates centralized version control and multi-protocol communication support (Wi-Fi, BLE, Zigbee, LoRa, and GSM), enabling consistent firmware distribution across heterogeneous networks. The system incorporates version-compatibility checks, rollback capability, and a server-driven release routing mechanism for development and production branches. An analytical model of timing, reliability, and energy consumption is provided, and experimental validation on a fleet of ESP32 devices demonstrates reduced update latency compared to native vendor OTA solutions, together with reliable operation under simultaneous device loads. Overall, the proposed solution provides a scalable and resilient foundation for secure OTA lifecycle management in smart-industry, remote sensing, and autonomous infrastructure applications. Full article

The increasing exposure to non-ionizing electromagnetic (EM) radiation driven by urbanization and digitalization has encouraged the development of building materials with EM shielding properties. This study investigates the potential of enhancing the electromagnetic shielding properties of cement mortars by incorporating multi-walled carbon nanotubes (MWCNT) in various dosages (1%, 3%, 6%, 9% and 10% by binder mass). The microstructural and mechanical effects of MWCNT addition, as well as their efficiency in reducing EM transmission in the frequency range of 1.5–10 GHz (covering LTE, 5G, WiFi, and radar systems), were analyzed. S₂₁ measurements were performed using a modified coaxial transmission line method with a vector network analyzer. Results show that increasing the MWCNT content enhances EM shielding effectiveness but simultaneously affects the mortar’s microstructure and mechanical properties. Higher MWCNT levels achieved the best EM shielding, with an improvement of up to 27.66 dB compared to ordinary mortar in the navigation radar frequency range. These findings confirm the potential of MWCNT-modified mortars for protecting buildings and sensitive infrastructure—such a hospitals, communication hubs, data centers and military facilities—from EM radiation. Full article

Android devices employ several methods to calculate their position. This paper’s focus is the Network Location Provider (NLP), which leverages Wi-Fi and cell tower signals via the fingerprinting/database approach. Unlike GNSS-based positioning, the NLP should be able to compute positions even when the device is indoors or experiencing GNSS radio frequency interference (RFI), making it an enticing candidate for ensuring robust PNT solutions. However, the inner workings of NLP are largely undisclosed, remaining as a ‘black-box’ system. Using the Samsung S24 and Xiaomi Redmi K80 Ultra, we explored the NLP’s response to GNSS spoofing and offline operation (no network connection), as well as attempting NLP spoofing. The GNSS spoofing test confirmed that when satellite signals are spoofed, the NLP solution is maintained at the truth location. This reinforces the robustness of the NLP in RFI environments. In offline mode, NLP continued to operate without a Google server connection, indicating that it can compute positions locally using internally stored cache data. This behavior deviates from the conventional understanding of NLP and offers valuable insights into the latest NLP mechanism. These findings build upon previous work to uncover the inner workings of the NLP and ultimately contribute to robust smartphone PNT. Full article

This article presents experimental research focused on the implementation and evaluation of new wireless communication technologies to enhance the quality and stability of motion control in autonomous mobile robots. The reason for this research arises from the fact that autonomous mobile robots are increasingly used in industrial applications, where reliable real-time operation is required, which depends on low latency and the stability of wireless communication. The theoretical part deals with the possibilities of using local 5G technology in industrial mobile robotics and with the parameters of this technology. The experimental part includes measurements in which the robot was simultaneously connected to both the private 5G SA network and the Wi-Fi 6E network during its operation, where a custom Python script was used to record connection quality parameters (latency, jitter, packet loss, RSSI) together with the robot’s position in space and a timestamp. The collected data were processed and visualized in the form of graphs and heatmaps, enabling an analysis of network dynamics in relation to the robot’s movement in space. The results show that Wi-Fi 6E is more sensitive to environmental conditions, and during dynamic robot movement it often exhibits unstable latency, increased jitter, and packet loss. In contrast, the private 5G SA network demonstrates higher stability and resilience to outages, making it more suitable for industrial environments. This research highlights the importance of implementing new communication technologies to improve the quality and stability of autonomous mobile robot control. Full article

The compact and wideband patch antennas applied to WiFi 7 multiple-input multiple-output (MIMO) antenna systems are presented. The MIMO antenna structure consists of four multi-branch radiating patches fed by coupled microstrip lines, which occupies a size of $32\times 32\times 1 {\mathrm{m}\mathrm{m}}^3$. By exploiting multiple resonant modes, an impedance bandwidth of 37% (5.07–7.37 GHz) achieves a reflection coefficient of less than −10 dB and fully encompasses both WiFi 7 high-frequency ranges. To alleviate mutual coupling, two decoupling structures, named complementary split-ring resonators (CSRRs), are employed between the MIMO elements to interact with the undesirable surface current; furthermore, the proposed orthogonal placement of four elements further minimizes radiation coupling. Consequently, the proposed array achieves measured isolations greater than 14.5 dB and 11 dB at 5 GHz and 6 GHz bands, respectively. The prototype of the proposed MIMO antenna has been manufactured. It has also been measured and the results show similarity with the simulations. The measured radiation pattern and the diversity performance, including the envelope correlation coefficient (ECC), diversity gain (DG), and multiplexing efficiency, are calculated, and they verify the outstanding diversity characteristics of the proposed MIMO antenna. This makes it a promising solution for emerging WiFi 7 wideband applications. Full article

Although research on occupancy detection has been extensive, most studies have focused on improving detection accuracy, while the application of occupancy models to device control remains limited. This paper presented an occupant-centric control (OCC) of split air conditioners, personal desktop fans, and lights based on the Wi-Fi probe technology, Internet of Things (IoT), and time-of-use (TOU) electricity rates. Firstly, a machine-learning model based on Wi-Fi signals for occupancy detection was developed. The occupancy detection model integrated convolutional neural networks, gradient boosting classifier, and random forest (CNN-GBC-RF) models to identify four types of occupant statuses: arrival, leave, stay, and outside. Subsequently, the occupancy status and TOU rates information were integrated with IoT-enabled devices to dynamically control the space split air conditioners, personal desktop fans, and personal lights. This study implemented two experimental scenarios: a baseline scenario with fixed device operations and an OCC scenario. The OCC scenario dynamically adjusted device operation based on real-time occupancy status, TOU electricity rates, desktop illuminances, and dry-bulb air temperatures. Finally, a 16-day experiment was conducted in a multi-occupant office room to evaluate both occupancy detection model performance and the effectiveness of the proposed OCC-based scenario. Subjective questionnaires were collected to evaluate the thermal comfort under different scenarios. The results showed that the CNN-GBC-RF model had an overall detection accuracy of 97.0%. Additionally, the OCC-based scenario achieved a relative reduction of 39.9%, a relative reduction of 41.6% compared to the baseline scenario. The thermal comfort of the occupants under the OCC scenario was close to the baseline scenario. The results indicated that the proposed OCC-based control scenario contributed to improving energy efficiency while maintaining occupant thermal comfort. Full article