Search Results (449)

Indoor localization in GNSS-denied environments remains a significant challenge due to the low sampling frequency and high variability of wireless signal measurements. This~paper presents a wireless communication-based indoor localization method that integrates Wi-Fi received signal strength indication (RSSI) measurements with optical initialization and inertial sensor fusion. The proposed approach eliminates the need for labor-intensive fingerprinting and specialized infrastructure by leveraging existing Wi-Fi networks. Optical pose estimation using ArUco markers provides accurate initial position and orientation, enabling alignment between sensor coordinate systems and reducing inertial drift. During tracking, inertial measurements compensate for motion between sparse Wi-Fi observations by virtually translating historical RSSI samples, allowing statistically consistent averaging and improved distance estimation. A simplified factor graph framework is employed to fuse heterogeneous measurements while maintaining computational efficiency suitable for real-time operation on mobile devices. Experimental validation using a robot-based ground-truth reference system demonstrates sub-meter localization accuracy with an average positioning error of approximately 0.40~m. The proposed method provides a low-cost and scalable solution for indoor positioning and navigation applications such as access-controlled environments, exhibitions, and large public venues. Full article

The rapid proliferation of the Internet of Things (IoT) leaves terminal devices vulnerable to considerable security challenges, notably the absence of robust yet efficient identity authentication mechanisms. Traditional certificate-based approaches incur substantial management overhead and storage expenditure, whereas Identity-Based Cryptography poses inherent key escrow risks. To tackle these challenges, this paper proposes a PUF and SM2-based certificateless identity authentication mechanism that integrates SM2 Certificateless Public Key Cryptography (a Chinese national cryptographic standard) with Physical Unclonable Functions (PUFs). Initially, the proposed solution utilizes PUF technology to derive a unique hardware-generated “fingerprint” from an IoT device, which functions as a root key to generate a partial user private key. This approach essentially binds the terminal’s identity to its physical hardware, thereby effectively mitigating physical cloning attacks against nodes. Moreover, through the adoption of a Certificateless Public Key Cryptography (CLPKC) framework, the complete user private key is jointly generated by a semi-trusted Key Generation Centre (KGC) and the terminal device itself. The comprehensive security analysis proves that the proposed scheme is provably secure under the random oracle model, capable of resisting various common attacks such as physical cloning, man-in-the-middle, and replay attacks. Performance evaluation confirms that the implemented PUF + SM2 certificateless mechanism significantly reduces the size of user public key identifiers to within 64 bytes, offering a substantial advantage over the 1–2 KB certificates typically required in conventional PKI/CA systems, thereby enhancing efficiency in storage and communication. Full article

Radio frequency fingerprint identification (RFFI) based on physical-layer characteristics provides a reliable solution for secure authentication of Internet of Things (IoT) devices. Deep neural networks have demonstrated strong capability in improving RFFI performance; however, their high computational complexity and large parameter size pose significant challenges for deployment on resource-constrained edge devices. In RFFI tasks, existing pruning methods often lack effective performance recovery strategies, which leads to noticeable degradation in identification accuracy after pruning. To address this issue, this paper proposes a pruning method based on adversarial learning and polarization regularization. Polarization regularization is applied to learnable soft masks to effectively distinguish channels to be pruned from those to be retained. In addition, an adversarial learning-based performance recovery strategy is introduced to align the output feature distributions between the baseline network and the pruning network, thereby improving identification accuracy after pruning. Experimental results on multiple RFFI datasets demonstrate that the proposed method can effectively prune ResNet18 and VGG16, achieving substantial reductions in model complexity with only minor losses in identification performance. Full article

The increasing diffusion of plant-based milk alternatives poses new challenges at the intersection of food safety and consumer experience, particularly regarding allergen cross-contamination and beverage performance during preparation. Traditional quality control strategies are typically confined to upstream production stages and are unable to address individualized risks and sensory variability at the point of consumption. In this study, we propose an embedded volatilomic sensing approach that combines metal oxide semiconductor (MOX) sensor arrays with lightweight artificial intelligence algorithms to enable real-time, on-device decision-making. The volatilome of four commercially available plant-based milk beverages (oat, almond, soy, and coconut) was characterized using GC–MS/SPME as a reference method, while a MOX-based electronic nose provided rapid, non-destructive sensing of volatile fingerprints. Linear Discriminant Analysis demonstrated clear discrimination among beverage types based on their volatile signatures, supporting the use of MOX sensor arrays as functional descriptors of compositional identity and process-related variability. Beyond beverage classification, the proposed framework is designed to support future implementation of (i) screening for anomalous volatilomic patterns potentially compatible with accidental cow’s milk carryover in shared preparation settings and (ii) adaptive tuning of preparation parameters (e.g., foaming-related settings) in smart beverage systems. The results highlight the role of embedded volatilomic intelligence as a unifying layer between personalized risk-aware screening and sensory-oriented process control, paving the way for intelligent food-processing appliances capable of autonomous, real-time adaptation at the point of consumption. Full article

Localization and navigation techniques have become fundamental for modern lives, while achieving accurate results indoors still remains a significant challenge. The widespread adoption of smart devices, and especially smartphones, has increased the need for accurate and robust pedestrian dead reckoning systems that operate in infrastructure-less environments. Pedestrian dead reckoning’s primary challenge is maintaining accuracy despite varying smartphone placements (attitudes) and the noisy, low-cost inertial measurements units. In this work, a comprehensive pedestrian dead reckoning framework is presented that integrates advanced step counting and heading estimation techniques. For step detection and counting, we propose a robust step counting algorithm that utilizes the optimum fusion of the raw IMU readings, i.e., accelerometer, linear accelerometer, gyroscope, and magnetometer readings, each broken down into three degrees of freedom for different body placements and walking speeds. Furthermore, to address the critical issue of heading estimation, we propose the heading estimation axis mapping (HEAT-MAP) algorithm, which dynamically adjusts the sensor axes in response to the smartphone’s orientation, ensuring a consistent coordinate frame and reducing heading drift. Moreover, to eliminate cumulative pedestrian dead reckoning errors, the system incorporates an adaptive weighted fusion mechanism with Wi-Fi fingerprinting. Experimental results demonstrate that this integrated system significantly improves the overall trajectory accuracy, providing a high-precision, attitude-unconstrained solution for real-time indoor pedestrian navigation. Full article

Radio Frequency Fingerprinting (RFF) would be a promising Physical Layer Security (PLS) solution for the Internet of Things (IoT) that requires robust, low-overhead security techniques. However, practical implementation of RFF may pose challenges, in particular, performance instability and ethical-regulatory conflicts. Based on authors’ previous research, this paper elaborates these challenges in potential deployment of a resilient and compliant RFF system. First, we analytically show how hardware-induced feature drift, primarily driven by device aging and temperature variations, degrades RFF performance. We then critically survey existing temperature variation and aging models, one of which is being studied by one of the authors’ research team. We look into this from a purely hardware-design perspective, and then compensation methods for an RFF perspective. This reveals a significant gap: current techniques are insufficient to maintain the long-term, high-accuracy RFF for real-world IoT security requirements. Finally, we introduce inherent privacy risks by enabling device tracking. This property conflicts with General Data Protection Regulation (GDPR) mandates, raising significant regulatory challenges and privacy risks. Overall, this work highlights the key technical and legal challenges that must be addressed for RFF to evolve into a robust, privacy-compliant and deployable security primitive for IoT and future wireless systems. Full article

Radio frequency fingerprints (RFFs), arising from inherent hardware imperfections, serve as distinctive features for device identification. The location- and time-dependent nature of the wireless channel directly affects RFF-based device identification, making it challenging under different channel conditions. This is primarily because the training and test datasets containing RFFs may not overlap within the same feature-space domain. In this work, the mentioned issue is addressed as a domain adaptation problem. For this objective, we propose the use of a triplet-learning-based domain-adversarial neural network within a hybrid framework named TripletDANN. We leverage the triplet loss, enabling the network to focus exclusively on device-specific latent representations under different channel conditions, while employing an adversarial loss to prevent the network from exploiting channel-specific characteristics. With this aim, data aggregation is performed together with channel labeling. The generalization capability of TripletDANN is evaluated on previously unseen test data collected across different locations under two distinct scenarios. Raw I/Q signals of 15 Wi-Fi devices are used as a case study. The proposed TripletDANN model achieves up to $88.52\%$ average device classification accuracy across the different data collection locations. On average, TripletDANN attains up to a $5\%$ performance improvement over its counterpart model. Moreover, data augmentation is employed to improve the overall performance, and a highest accuracy of $96.71\%$ is achieved on experimentally collected test data from an unseen location. Full article

Source camera identification on smartphones constitutes a fundamental task in multimedia forensics, providing essential support for applications such as image copyright protection, illegal content tracking, and digital evidence verification. Numerous techniques have been developed for this task over the past decades. Among existing approaches, Photo-Response Non-Uniformity (PRNU) has been widely recognized as a reliable device-specific fingerprint and has demonstrated remarkable performance in real-world applications. Nevertheless, the rapid advancement of computational photography technologies has introduced significant challenges: modern devices often exhibit anomalous behaviors under PRNU-based analysis. For instance, images captured by different devices may exhibit unexpected correlations, while images captured by the same device can vary substantially in their PRNU patterns. Current approaches are incapable of automatically exploring the underlying causes of these anomalous behaviors. To address this limitation, we propose a simple yet effective forensic analysis framework leveraging Exchangeable Image File Format (EXIF) metadata. Specifically, we represent EXIF metadata as type-aware word embeddings to preserve contextual information across tags. This design enables visual interpretation of the model’s decision-making process and provides complementary insights for identifying the anomalous behaviors observed in modern devices. Extensive experiments conducted on three public benchmark datasets demonstrate that the proposed method not only achieves state-of-the-art performance for source camera identification but also provides valuable insights into anomalous device behaviors. Full article

The integration of wearable inertial measurement units (IMU) in animal welfare Internet of Things (IoT) systems has become crucial for monitoring animal behaviors and enhancing welfare management. However, the vulnerability of IoT devices to network and hardware attacks poses significant risks, potentially compromising data integrity and misleading caregivers, negatively impacting animal welfare. Additionally, current animal monitoring solutions often rely on intrusive tagging methods, such as Radio Frequency Identification (RFID) or ear tagging, which may cause unnecessary stress and discomfort to animals. In this study, we propose a lightweight integrity and provenance-oriented security stack that complements standard transport security, specifically tailored to IMU-based animal motion IoT systems. Our system utilizes a 1D-convolutional neural network (CNN) model, achieving 88% accuracy for precise motion recognition, alongside a lightweight behavioral fingerprinting CNN model attaining 83% accuracy, serving as an auxiliary consistency signal to support collar–animal association and reduce mis-attribution risks. We introduce a dynamically generated pre-shared key (PSK) mechanism based on SHA-256 hashes derived from motion features and timestamps, further securing communication channels via application-layer Hash-based Message Authentication Code (HMAC) combined with Message Queuing Telemetry Transport (MQTT)/Transport Layer Security (TLS) protocols. In our design, MQTT/TLS provides primary device authentication and channel protection, while behavioral fingerprinting and per-window dynamic–HMAC provide auxiliary provenance cues and tamper-evident integrity at the application layer. Experimental validation is conducted primarily via offline, dataset-driven experiments on a public canine IMU dataset; system-level overhead and sensor-to-edge latency are measured on a Raspberry Pi-based testbed by replaying windows through the MQTT/TLS pipeline. Overall, this work integrates motion recognition, behavioral fingerprinting, and dynamic key management into a cohesive, lightweight telemetry integrity/provenance stack and provides a foundation for future extensions to multi-species adaptive scenarios and federated learning applications. Full article

The composition of sunflower oil, rich in fatty acids, largely depends on the seed variety. Commercial sunflower oils are classified as low (SFO), medium (MOSFO), and high (HOSFO) oleic, distinguished by their oleic and linoleic acid content. Higher oleic acid levels enhance health benefits and oxidative stability. Due to their differing market values, ensuring the correct quality and authenticity of these oils is essential. Unsupervised chemometric methods have been applied to visualise the natural behaviour of sunflower oils, while supervised models have been used for authentication based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR) fingerprints obtained from a benchtop spectrometer. Authentication of MOSFO is particularly challenging because of its wider oleic acid range (43.1–74.9%) and production via genetic modification or blending SFO/HOSFO. To address this, two multivariable PLS-R regression models were developed using ATR FT-IR and Fibre Optic Reflectance Spectroscopy (FORS) fingerprints, the latter obtained with a portable, cost-effective device. The results indicate that FORS could be used as a rapid quality control tool for on-site quantification. In contrast, ATR FT-IR is a more accurate tool for confirmation and quantification, achieving excellent results (Residual Predictive Deviation, RPD = 7.09 and Range Error Ratio, RER = 17.82). Full article

The proliferation of wired Gigabit Ethernet has greatly increased communication bandwidth, while introducing new challenges for device identification and security. Conventional physical-layer fingerprinting techniques are constrained by the Nyquist sampling theorem, limiting their suitability for large-scale deployment. To overcome this limitation, we propose a lightweight fingerprint extraction scheme based on sub-Nyquist sampling. The scheme introduces two types of fingerprints: the signal rearrangement distribution fingerprint ($f_{sort}$) and the amplitude–frequency distribution fingerprint ($f_{hist}$). The $f_{sort}$ adopts a low-complexity unsupervised classification framework based on amplitude rearrangement, principal component analysis (PCA), and a support vector machine (SVM), making it suitable for resource-constrained scenarios. The $f_{hist}$ establishes a high-accuracy supervised classification framework using amplitude–frequency statistical representations, linear discriminant analysis (LDA), and a deep learning classifier. Multi-instance and cross-type scenarios are used to evaluate classification accuracy and generalization capability. Experimental results show that the $f_{hist}$ method, employing the LDA-DL framework, achieves an accuracy of 97.4% in identifying 18 different devices at a sampling rate of 5 $\mathrm{M}\mathrm{s}\mathrm{p}\mathrm{s}$. This approach reduces dependence on the sampling rate and data volume while maintaining high identification accuracy. It therefore provides a robust and cost-effective physical-layer authentication solution for Gigabit Ethernet. Full article

Accurate fingerprinting of Internet of Things (IoT) devices is essential for network security, management, and anomaly detection. Existing machine-learning-based approaches can be broadly classified into two categories. The first are time-domain-based approaches that infer device identity from aggregated traffic statistics, while effective in dense communication environments, they perform poorly for devices that generate sparse, low-volume, or irregular traffic, which restricts behavioral visibility. The second, radio frequency fingerprinting (RFF), extracts hardware-specific traits from radio frequency signals but is limited in wired or mixed-connectivity IoT networks and lacks behavioral or functional insights. To overcome these limitations, this paper proposes a hybrid fingerprinting framework that integrates network traffic analysis with frequency-domain representations using wavelet transform techniques. This approach captures both temporal and spectral characteristics, combining behavioral and structural perspectives to enable robust and accurate IoT device identification. The proposed system is evaluated on three real-world datasets under multiple experimental scenarios, including (1) device identification, (2) device type classification, (3) scalability with dataset size and complexity, and (4) performance under Distributed Denial-of-Service (DDoS) attack conditions. Experimental results show that wavelet-based features consistently outperform conventional time-domain features across all evaluation metrics, achieving higher accuracy, resilience, and generalization. Full article

Wrist-worn devices enable new paradigms of implicit and continuous user authentication; however, identifying biometric modalities that combine reliability with practical integrability remains challenging. Inner-wrist skin texture represents a relatively unexplored biometric characteristic that may be acquired unobtrusively using commodity hardware. This study evaluates biometric verification based on inner-wrist skin texture using an off-the-shelf capacitive fingerprint sensor and an unmodified, manufacturer-provided fingerprint verification algorithm. Two experiments were conducted. Experiment 1 assessed baseline verification performance under controlled acquisition conditions in a cohort of 33 participants (21 male, 12 female; mean age 30.0 ± 16.9 years, range 10–71 years), yielding 1768 genuine authentication trials. Experiment 2 examined the effect of wrist posture variation under controlled flexion in a separate cohort of 15 participants (11 male, 4 female; mean age 30.9 years, range 18–49 years), with 3900 authentication trials recorded. Across 86,897 impostor comparisons in Experiment 1, no false acceptances were observed, corresponding to a conservative upper bound on the false acceptance rate of 6.7 × 10⁻⁵ at the 99.7% confidence level, while the false rejection rate was approximately 2.93%. In Experiment 2, the overall false rejection rate increased to 3.52%, with no clear monotonic relationship between wrist angle and verification performance within the tested range. The results demonstrate that inner-wrist skin texture can be captured and matched using fingerprint-oriented sensing and matching technology under controlled conditions, providing an experimental baseline for this biometric modality. At the same time, the use of a closed matching algorithm and a sensor designed for fingerprints limits interpretability and generalization. These findings motivate further investigation using dedicated recognition methods, larger sensing areas, and extended evaluation protocols tailored specifically to wrist skin print biometrics. Full article

This study examines how the seven pillars of ZTA are implemented in a CDS environment that demands high security reliability, similar to the defense and finance sectors, and identifies the technological advancements and integration patterns that emerge during this process. With the introduction of user- and device-centric authentication methods like distributed identity and RF fingerprinting in the Identity and Device areas, there is a growing trend towards strengthening trust even in domains where distrust is prevalent. In the Network and Application domains, the focus is on using micro-segmentation and SDN to segment and control internal traffic flows, while dynamically enforcing the principle of least privilege. In the Data, Visibility, and Orchestration domains, AI analysis is being applied in real-time, leveraging log and visibility data, and orchestration is automating policy execution and response. In conclusion, it is clear that each pillar of ZTA operates in tandem with the others, rather than as isolated components within the CDS environment. This fusion structure demonstrates its ability to function as a unified security strategy that balances trust with comprehensive coverage of diverse domains. Full article

In this paper, we propose a source camera identification method based on disentangled feature modeling, aiming to achieve robust extraction of camera fingerprint features under complex imaging and post-processing conditions. To address the severe coupling between image content and camera fingerprint features in existing methods, which makes content interference difficult to suppress, we develop a dual-branch deep learning framework guided by imaging physics. By introducing physical consistency constraints, the proposed framework explicitly separates image content representations from device-related fingerprint features in the feature space, thereby enhancing the stability and robustness of source camera identification. The proposed method adopts two parallel branches: a content modeling branch and a fingerprint feature extraction branch. The content branch is built upon an improved U-Net architecture to reconstruct scene and color information, and further incorporates texture refinement and multi-scale feature fusion to reduce residual content interference in fingerprint modeling. The fingerprint branch employs ResNet-50 as the backbone network to learn discriminative global features associated with the camera imaging pipeline. Based on these branches, fingerprint information dominated by sensor noise is explicitly extracted by computing the residual between the input image and the reconstructed content, and is further encoded through noise analysis and feature fusion for joint camera model classification. Experimental results on multiple public-source camera forensics datasets demonstrate that the proposed method achieves stable and competitive identification performance in same-brand camera discrimination, complex imaging conditions, and post-processing scenarios, validating the effectiveness of the proposed disentangled modeling and physical consistency constraint strategy for source camera identification. Full article