Search Results (251)

As the demand for context-aware services in smart environments continues to rise, Indoor Positioning Systems (IPSs) have evolved from auxiliary technologies into indispensable components of mission-critical infrastructure. This paper presents a comprehensive, multidimensional evaluation of IPSs through the lens of critical infrastructure, addressing both their technical capabilities and operational limitations across dynamic indoor environments. A structured taxonomy of IPS technologies is developed based on sensing modalities, signal processing techniques, and system architectures. Through an in-depth trade-off analysis, the study highlights the inherent tensions between accuracy, energy efficiency, scalability, and deployment cost—revealing that no single technology meets all performance criteria across application domains. A novel evaluation framework is introduced that integrates traditional performance metrics with emerging requirements such as system resilience, interoperability, and ethical considerations. Empirical results from long-term Wi-Fi fingerprinting experiments demonstrate the impact of temporal signal fluctuations, heterogeneity features, and environmental dynamics on localization accuracy. The proposed adaptive algorithm consistently outperforms baseline models in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), confirming its robustness under evolving conditions. Furthermore, the paper explores the role of collaborative and infrastructure-free positioning systems as a pathway to achieving scalable and resilient localization in healthcare, logistics, and emergency services. Key challenges including privacy, standardization, and real-world adaptability are identified, and future research directions are proposed to guide the development of context-aware, interoperable, and secure IPS architectures. By reframing IPSs as foundational infrastructure, this work provides a critical roadmap for designing next-generation indoor localization systems that are technically robust, operationally viable, and ethically grounded. Full article

Wi-Fi fingerprinting is a widely adopted technique for indoor localization in location-based services (LBS) due to its cost-effectiveness and ease of deployment using existing infrastructure. However, the performance of these systems often suffers due to missing received signal strength indicator (RSSI) measurements, which can arise from complex indoor structures, device limitations, or user mobility, leading to incomplete and unreliable fingerprint data. To address this critical issue, we propose Feature-level Augmentation for Localization (FALoc), a novel framework that enhances Wi-Fi fingerprinting-based localization through targeted feature-level data augmentation. FALoc uniquely models the observation probabilities of RSSI signals by constructing a bipartite graph between reference points and access points, which is then processed by a variational graph auto-encoder (VGAE). Based on these learned probabilities, FALoc intelligently imputes likely missing RSSI values or removes unreliable ones, effectively enriching the training data. We evaluated FALoc using an MLP (Multi-Layer Perceptron)-based localization model on the UJIIndoorLoc and UTSIndoorLoc datasets. The experimental results demonstrate that FALoc significantly improves localization accuracy, achieving mean localization errors of 7.137 m on UJIIndoorLoc and 7.138 m on UTSIndoorLoc, which represent improvements of approximately 12.9% and 8.6% over the respective MLP baselines (8.191 m and 7.808 m), highlighting the efficacy of our approach in handling missing data. Full article

Specific emitter identification (SEI) is a core technology for wireless device security that plays a crucial role in protecting wireless communication systems from various security threats. However, current deep learning-based SEI methods heavily rely on large amounts of labeled data for supervised training, facing challenges in non-cooperative communication scenarios. To address these issues, this paper proposes a novel contrastive asymmetric masked learning-based SEI (CAML-SEI) method, effectively solving the problem of SEI under scarce labeled samples. The proposed method constructs an asymmetric auto-encoder architecture, comprising an encoder network based on channel squeeze-and-excitation residual blocks to capture radio frequency fingerprint (RFF) features embedded in signals, while employing a lightweight single-layer convolutional decoder for masked signal reconstruction. This design promotes the learning of fine-grained local feature representations. To further enhance feature discriminability, a learnable non-linear mapping is introduced to compress high-dimensional encoded features into a compact low-dimensional space, accompanied by a contrastive loss function that simultaneously achieves feature aggregation of positive samples and feature separation of negative samples. Finally, the network is jointly optimized by combining signal reconstruction and feature contrast tasks. Experiments conducted on real-world ADS-B and Wi-Fi datasets demonstrate that the proposed method effectively learns generalized RFF features, and the results show superior performance compared with other SEI methods. Full article

Indoor localization techniques operate independently of Global Navigation Satellite Systems (GNSSs), which are primarily designed for outdoor environments. However, integrating indoor and outdoor positioning often leads to inconsistent and delayed location estimates, especially at transition zones such as building entrances. This paper develops a probabilistic transition detection algorithm to identify indoor, outdoor, and transition zones, aiming to enhance the continuity and accuracy of positioning. The algorithm leverages multi-source sensor data, including WiFi Received Signal Strength Indicator (RSSI), Bluetooth Low-Energy (BLE) RSSI, and GNSS metrics such as carrier-to-noise ratio. During transitions, the system incorporates Inertial Measurement Unit (IMU)-based tracking to ensure smooth switching between positioning engines. The outdoor engine utilizes a Kalman Filter (KF) to fuse IMU and GNSS data, while the indoor engine employs fingerprinting techniques using WiFi and BLE. This paper presents experimental results using three distinct devices across three separate buildings, demonstrating superior performance compared to both Google’s Fused Location Provider (FLP) algorithm and a GPS. Full article

Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and important method for human activity recognition. However, most WiFi-based activity recognition methods have limitations, such as using WiFi fingerprints to identify human activities. They either require extensive sample collection and training, are constrained by a fixed environmental layout, or rely on the precise positioning of transmitters (TXs) and receivers (RXs) within the space. If the positions are uncertain, or change, the sensing performance becomes unstable. To address the dependency of current WiFi indoor human activity trajectory reconstruction on the TX-RX position, we propose WiPIHT, a stable system for tracking indoor human activity trajectories using a small number of commercial WiFi devices. This system does not require additional hardware to be carried or locators to be attached, enabling passive, real-time, and accurate tracking and trajectory reconstruction of indoor human activities. WiPIHT is based on an innovative CSI channel analysis method, analyzing its autocorrelation function to extract location-independent real-time movement speed features of the human body. It also incorporates Fresnel zone and motion velocity direction decomposition to extract movement direction change patterns independent of the relative position between the TX-RX and the human body. By combining real-time speed and direction curve features, the system derives the shape of the human movement trajectory. Experiments demonstrate that, compared to existing methods, our system can accurately reconstruct activity trajectory shapes even without knowing the initial positions of the TX or the human body. Additionally, our system shows significant advantages in tracking accuracy, real-time performance, equipment, and cost. Full article

Despite the widespread adoption of indoor positioning technology, the existing solutions still face significant challenges. On one hand, Wi-Fi-based positioning struggles to balance accuracy and efficiency in complex indoor environments and architectural layouts formed by pre-existing access points (APs). On the other hand, vision-based methods, while offering high-precision potential, are hindered by prohibitive costs associated with binocular camera systems required for depth image acquisition, limiting their large-scale deployment. Additionally, channel state information (CSI), containing multi-subcarrier data, maintains amplitude symmetry in ideal free-space conditions but becomes susceptible to periodic positioning errors in real environments due to multipath interference. Meanwhile, image-based positioning often suffers from spatial ambiguity in texture-repeated areas. To address these challenges, we propose a novel hybrid indoor positioning method that integrates multi-granularity and multi-modal features. By fusing CSI data with visual information, the system leverages spatial consistency constraints from images to mitigate CSI error fluctuations while utilizing CSI’s global stability to correct local ambiguities in image-based positioning. In the initial coarse-grained positioning phase, a neural network model is trained using image data to roughly localize indoor scenes. This model adeptly captures the geometric relationships within images, providing a foundation for more precise localization in subsequent stages. In the fine-grained positioning stage, CSI features from Wi-Fi signals and Scale-Invariant Feature Transform (SIFT) features from image data are fused, creating a rich feature fusion fingerprint library that enables high-precision positioning. The experimental results show that our proposed method synergistically combines the strengths of Wi-Fi fingerprints and visual positioning, resulting in a substantial enhancement in positioning accuracy. Specifically, our approach achieves an accuracy of 0.4 m for 45% of positioning points and 0.8 m for 67% of points. Overall, this approach charts a promising path forward for advancing indoor positioning technology. Full article

Radio Frequency Fingerprint Identification (RFFI) is a promising device authentication technique that utilizes inherent hardware flaws in transmitters to achieve device identification, thus effectively maintaining the security of the Internet of Things (IoT). However, time-varying channels degrade accuracy due to factors like device aging and environmental changes. To address this, we propose an RFFI method integrating Zero-Forcing (ZF) equalization and KAN-ResNet. Firstly, the Wi-Fi preamble signals under the IEEE 802.11 standard are Zero-Forcing equalized, so as to effectively reduce the interference of time-varying channels on RFFI. We then design a novel residual network, KAN-ResNet, which adds a KAN module on top of the traditional fully connected layer. The module combines the B-spline basis function and the traditional activation function Sigmoid Linear Unit (SiLU) to realize the nonlinear mapping of the complex function, which enhance the classification ability of the network for RFF features. In addition, to improve the generalization of the model, the grid of B-splines is dynamically updated and L1 regularization is introduced. Experiments show that on datasets collected 20 days apart, our method achieves 99.4% accuracy, reducing the error rate from 6.3% to 0.6%, outperforming existing models. Full article

Indoor positioning systems have become increasingly important due to the limitations of GPS in indoor environments, such as non-line-of-sight conditions and weak signal strength. Among the various indoor positioning techniques, fingerprinting-based approaches utilizing WiFi signals are highly regarded for their accessibility and convenience. However, existing convolutional neural network (CNN) models for fingerprinting often struggle to maintain consistent performance under diverse environmental conditions. To address these challenges, this study proposes a local batch normalization (LBN)-aided CNN model for received signal strength indicator (RSSI)-based indoor positioning. The LBN technique is designed to overcome the limitations of traditional batch normalization (BN) and layer normalization (LN) in managing location-dependent RSSI variations, thereby improving positioning accuracy. The proposed approach consists of two phases: an offline phase, where RSSI data are collected at reference points to train the model, and an online phase, where real-time RSSI data are used to estimate the device’s location. Experimental results demonstrate that the proposed LBN-aided CNN model achieves an accuracy of 92.9%, outperforming existing CNN-based methods. These findings confirm the effectiveness of LBN in enhancing CNN performance for indoor positioning, particularly in challenging environments with significant signal variability. Full article

With the rapid advancement of network technologies and the widespread adoption of smart devices, the demand for efficient indoor localization and navigation systems has surged. Addressing the navigation challenge without requiring additional hardware is critical for the broad adoption of such technologies. Among various fingerprint-based systems—such as Bluetooth, ZigBee, or FM radio—Wi-Fi-based indoor positioning stands out as a practical solution, due to the pervasiveness of Wi-Fi infrastructure in public indoor spaces. This study introduces an ESP32-based data-collection tool designed to minimize offline training time for Wi-Fi fingerprinting, and it presents a novel dataset incorporating room-to-room transition time, which represents the time taken to move between rooms, alongside Wi-Fi signal strength data. The proposed approach focuses on room-level localization, leveraging Machine Learning (ML) models to predict the most likely room rather than precise (x, y) coordinates. To assess the effectiveness of this feature, three datasets were collected from different residential environments by three different individuals, enabling a comprehensive evaluation across multiple spatial layouts and movement patterns. The experimental results demonstrate that incorporating room-to-room transition time consistently enhanced localization performance across all the datasets, with accuracy improvements ranging from 1.17% to 12.47%, depending on the model and dataset. Notably, the Wide Neural Network model exhibited the highest improvement, achieving an accuracy increase from 82.37% to 94.77%, while the Ensemble-based methods such as Ensemble Bagged Trees also benefited significantly, reaching up to 93.17% accuracy. Despite varying gains across the datasets, the results confirm that integrating room-to-room transition time improves Wi-Fi-based indoor positioning by leveraging temporal movement patterns to enhance classification. Full article

The Enhanced 911 (E911) mandate of the Federal Communications Commission (FCC) drives the evolution of indoor three-dimensional (3D) location/positioning services for emergency calls. Many indoor localization systems exploit location-dependent wireless signaling signatures, often called fingerprints, and machine learning techniques for position estimation. In particular, received signal strength indicators (RSSIs) and Channel State Information (CSI) in Wireless Local Area Networks (WLANs or Wi-Fi) have gained popularity and have been addressed in the literature. While RSSI signatures are easy to collect, the fluctuation of wireless signals resulting from environmental uncertainties leads to considerable variations in RSSIs, which poses a challenge to accurate localization on a single floor, not to mention multi-floor or even three-dimensional (3D) indoor localization. Considering recent E911 mandate attention to vertical location accuracy, this study aimed to investigate CSI from Wi-Fi signals to produce baseline Z-axis location data, which has not been thoroughly addressed. To that end, we utilized CSI measurements and two representative machine learning methods, an artificial neural network (ANN) and convolutional neural network (CNN), to estimate both 3D and vertical-axis positioning feasibility to achieve E911 accuracy compliance. Full article

The increasing dependence on Wi-Fi for device-to-device communication in industrial environments has introduced significant security and privacy challenges. In such wireless networks, rogue access point (RAP) attacks have become more common, exploiting the openness of wireless communication to intercept sensitive operational data, compromise privacy, and disrupt industrial processes. Existing mitigation schemes often rely on dedicated hardware and cryptographic methods for authentication, which are computationally expensive and impractical for the diverse and resource-limited devices commonly found in industrial networks. To address these challenges, this paper introduces a robust and lightweight Wi-Fi device identification scheme, named the P-A scheme, specifically designed for industrial settings. By extracting hardware fingerprints from the phase and amplitude characteristics of channel state information (CSI), the P-A scheme offers an efficient and scalable solution for identifying devices and detecting rogue access points. A lightweight neural network ensures fast and accurate identification, making the scheme suitable for real-time industrial applications. Extensive experiments in real-world scenarios demonstrate the effectiveness of the scheme, achieving 95% identification accuracy within 0.5 s. The P-A scheme offers a practical pathway to safeguard data integrity and privacy in complex industrial networks against evolving cyber threats. Full article

This research article proposes a new method for an enhanced Flexible Manufacturing System (FMS) using a combination of smart methods. These methods use a set of three technologies of Industry 4.0, namely Artificial Intelligence (AI), Digital Twin (DT), and Wi-Fi-based indoor localization. The combination tackles the problem of asset tracking through Wi-Fi localization using machine-learning algorithms. The methodology utilizes the extensive “UJIIndoorLoc” dataset which consists of data from multiple floors and over 520 Wi-Fi access points. To achieve ultimate efficiency, the current study experimented with a range of machine-learning algorithms. The algorithms include Support Vector Machines (SVM), Random Forests (RF), Decision Trees, K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN). To further optimize, we also used three optimizers: ADAM, SDG, and RMSPROP. Among the lot, the KNN model showed superior performance in localization accuracy. It achieved a mean coordinate error (MCE) between 1.2 and 2.8 m and a 100% building rate. Furthermore, the CNN combined with the ADAM optimizer produced the best results, with a mean squared error of 0.83. The framework also utilized a deep reinforcement learning algorithm. This enables an Automated Guided Vehicle (AGV) to successfully navigate and avoid both static and mobile obstacles in a controlled laboratory setting. A cost-efficient, adaptive, and resilient solution for real-time tracking of assets is achieved through the proposed framework. The combination of Wi-Fi fingerprinting, deep learning for localization, and Digital Twin technology allows for remote monitoring, management, and optimization of manufacturing operations. Full article

Indoor positioning systems have become increasingly important due to the rapid expansion of Internet of Things (IoT) technologies, especially for providing precise location-based services in complex environments such as multi-floor campus buildings. This paper presents a WiFi fingerprint indoor localization system based on AdaBoost, combined with a new access point (AP) filtering technique. The system comprises offline and online phases. During the offline phase, a fingerprint database is created using received signal strength (RSS) values for two four-floor campus buildings. In the online phase, the AdaBoost classifier is used to accurately estimate locations. To improve localization accuracy, APs that always appear in the measurement data are selected for applying the AdaBoost algorithm, aiming to eliminate noise from the fingerprint database. The performance of the proposed method is compared with other well-known machine learning-based positioning algorithms in terms of positioning accuracy and error distances. The results indicate that the average positioning accuracy of the proposed scheme reaches 95.55%, which represents an improvement of 5.55% to 16.21% over the other methods. Additionally, the two-dimensional positioning error can be reduced to 0.25 m. Full article

Specific emitter identification (SEI) is a promising physical-layer authentication technique that serves as a crucial complement to upper-layer authentication mechanisms. SEI capitalizes on the inherent radio frequency fingerprints stemming from circuit discrepancies, which are intrinsic hardware properties and challenging to counterfeit. Recently, various deep learning (DL)-based SEI methods have been proposed, achieving outstanding performance. However, collecting and annotating substantial data for novel or unknown radiation sources is not only time-consuming but also cost-intensive. To address this issue, this paper proposes a few-shot (FS) metric learning-based time-frequency fusion network. To enhance the discriminative capability for radiation source signals, the model employs a convolutional block attention module (CBAM) and feature transformation to effectively fuse the raw signal’s time domain and time-frequency domain representations. Furthermore, to improve the extraction of discriminative features under FS scenarios, the proxy-anchor loss and center loss are introduced to reinforce intra-class compactness and inter-class separability. Experiments on the ADS-B and Wi-Fi datasets demonstrate that the proposed TFAF-Net consistently outperforms existing models in FS-SEI tasks. On the ADS-B dataset, TFAF-Net achieves a 9.59% higher accuracy in 30-way 1-shot classification compared to the second-best model, and reaches an accuracy of 85.02% in 10-way classification. On the Wi-Fi dataset, TFAF-Net attains 90.39% accuracy in 5-way 1-shot classification, outperforming the next best model by 6.28%, and shows a 13.18% improvement in 6-way classification. Full article

High-precision indoor positioning is essential for various applications, such as the Internet of Things, robotics, and smart manufacturing, requiring accuracy better than 1 m. Conventional indoor positioning methods, like Wi-Fi or Bluetooth fingerprinting, typically provide low accuracy within a range of several meters, while techniques such as laser or visual odometry often require fusion with absolute positioning methods. Ultra-wideband (UWB) and Wi-Fi Round-Trip Time (RTT) are emerging radio positioning technologies supported by industry leaders like Apple and Google, respectively, both capable of achieving high-precision indoor positioning. This paper offers a comprehensive survey of UWB and Wi-Fi positioning, beginning with an overview of UWB and Wi-Fi RTT ranging, followed by an explanation of the fundamental principles of UWB and Wi-Fi RTT-based geometric positioning. Additionally, it compares the strengths and limitations of UWB and Wi-Fi RTT technologies and reviews advanced studies that address practical challenges in UWB and Wi-Fi RTT positioning, such as accuracy, reliability, continuity, and base station coordinate calibration issues. These challenges are primarily addressed through a multi-sensor fusion approach that integrates relative and absolute positioning. Finally, this paper highlights future directions for the development of UWB- and Wi-Fi RTT-based indoor positioning technologies. Full article