Search Results (602)

In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks. Full article

Developing stretchable strain sensors that combine both high sensitivity and a wide linear range is a critical requirement for health electronics, yet it remains challenging to meet the practical demands of daily health monitoring. This study proposes a novel heterogeneous surface strategy by in situ silver deposition on modified PDMS followed by MXene spray coating, constructing a multilevel microcrack strain sensor (MAP) using silver nanoparticles and MXene. This innovative multilevel heterogeneous microcrack structure forms a dual conductive network, which demonstrates excellent detection performance within GF_max = 487.3 and response time ≈65 ms across various deformation variables. And the seamless integration of the sensor arrays was designed and employed for the detection of human activities without sacrificing biocompatibility and comfort. Furthermore, by adopting advanced deep learning technology, these sensor arrays could identify different joint movements with an accuracy of up to 95%. These results provide a promising example for designing high-performance stretchable strain sensors and intelligent recognition systems. Full article

With the increasing scale of industrial devices and the growing complexity of multi-source heterogeneous sensor data, traditional methods struggle to address challenges in fault detection, data security, and trustworthiness. Ensuring tamper-proof data storage and improving prediction accuracy for imbalanced anomaly detection for potential deployment in the Industrial Internet of Things (IIoT) remain critical issues. This study proposes a blockchain-powered Long Short-Term Memory Network (LSTM)–Attention hybrid model: an LSTM-based Encoder–Attention–Decoder (LEAD) for industrial device anomaly detection. The model utilizes an encoder–attention–decoder architecture for processing multivariate time series data generated by industrial sensors and smart contracts for automated on-chain data verification and tampering alerts. Experiments on real-world datasets demonstrate that the LEAD achieves an $F_{0.1}$ score of 0.96, outperforming baseline models (Recurrent Neural Network (RNN): 0.90; LSTM: 0.94; and Bi-directional LSTM (Bi-LSTM, 0.94)). We simulate the system using a private FISCO-BCOS network with a multi-node setup to demonstrate contract execution, anomaly data upload, and tamper alert triggering. The blockchain system successfully detects unauthorized access and data tampering, offering a scalable solution for device monitoring. Full article

Background: Tremor is a common but diagnostically challenging movement disorder due to its clinical heterogeneity and overlapping aetiologies. The 2018 consensus introduced a two-axis classification system that redefined tremor syndromes by distinguishing between clinical phenomenology and underlying causes, and introduced new diagnostic categories, such as essential tremor plus. Methods: This review synthesises recent advances in the epidemiology, classification, pathophysiology, and treatment of tremor syndromes, aiming to provide an integrated and clinically relevant framework that aligns with emerging diagnostic and therapeutic paradigms. Results: We discuss how electrophysiology, neuroimaging, wearable sensors, and artificial intelligence are reshaping diagnostic precision. Syndromes such as essential tremor, parkinsonian tremor, dystonic tremor, task-specific tremor, orthostatic tremor, and functional tremor are examined through syndromic, aetiological, and mechanistic lenses. The limitations of current rating scales and the promise of emerging biomarkers are critically assessed. Conclusions: As therapeutic approaches evolve toward neuromodulation and precision medicine, the need for pathophysiologically grounded diagnostic criteria becomes more urgent. Integrating network-based frameworks, digital diagnostics, and individualised treatment holds promise for advancing tremor care. Full article

The rapid integration of complex sensors and electronic control units (ECUs) in autonomous vehicles significantly increases cybersecurity risks in vehicular networks. Although the Controller Area Network (CAN) is efficient, it lacks inherent security mechanisms and is vulnerable to various network attacks. The traditional Intrusion Detection System (IDS) makes it difficult to effectively deal with the dynamics and complexity of emerging threats. To solve these problems, a lightweight vehicular network intrusion detection framework based on Dynamic Feature Fusion Federated Learning (DFF-FL) is proposed. The proposed framework employs a two-stream architecture, including a transformer-augmented autoencoder for abstract feature extraction and a lightweight CNN-LSTM–Attention model for preserving temporal and local patterns. Compared with the traditional theoretical framework of the federated learning, DFF-FL first dynamically fuses the deep feature representation of each node through the transformer attention module to realize the fine-grained cross-node feature interaction in a heterogeneous data environment, thereby eliminating the performance degradation caused by the difference in feature distribution. Secondly, based on the final loss $L_{AE}\left(X,\widehat{X}\right)$ index of each node, an adaptive weight adjustment mechanism is used to make the nodes with excellent performance dominate the global model update, which significantly improves robustness against complex attacks. Experimental evaluation on the CAN-Hacking dataset shows that the proposed intrusion detection system achieves more than 99% F1 score with only 1.11 MB of memory and 81,863 trainable parameters, while maintaining low computational overheads and ensuring data privacy, which is very suitable for edge device deployment. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a target area by analyzing multi-temporal images. To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images. The MHGAN employs a bidirectional adversarial convolutional autoencoder network to reconstruct and perform style transformation of heterogeneous images. Unlike existing unidirectional translation frameworks (e.g., CycleGAN), our approach simultaneously aligns features in both domains through multi-head graph attention and dynamic kernel width estimation, effectively reducing false changes caused by sensor heterogeneity. The network training is constrained by four loss functions: reconstruction loss, code correlation loss, graph attention loss, and adversarial loss, which together guide the alignment of heterogeneous images into a unified data domain. The code correlation loss enforces consistency in feature representations at the encoding layer, while a density-based kernel width estimation method enhances the capture of both local and global changes. The graph attention loss models the relationships between features and images, improving the representation of consistent regions across bitemporal images. Additionally, adversarial loss promotes style consistency within the shared domain. Our bidirectional adversarial convolutional autoencoder simultaneously aligns features across both domains. This bilateral structure mitigates the information loss associated with one-way mappings, enabling more accurate style transformation and reducing false change detections caused by sensor heterogeneity, which represents a key advantage over existing unidirectional methods. Compared with state-of-the-art methods for heterogeneous change detection, the MHGAN demonstrates superior performance in both qualitative and quantitative evaluations across four benchmark heterogeneous remote sensing datasets. Full article

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

Cross-view image matching between UAV and satellite platforms is critical for geographic localization but remains challenging due to domain gaps caused by disparities in imaging sensors, viewpoints, and illumination conditions. To address these challenges, this paper proposes an Adaptive Threshold-guided Ring Partitioning Framework (ATRPF) for UAV–satellite cross-view image matching. Unlike conventional ring-based methods with fixed partitioning rules, ATRPF innovatively incorporates heatmap-guided adaptive thresholds and learnable hyperparameters to dynamically adjust ring-wise feature extraction regions, significantly enhancing cross-domain representation learning through context-aware adaptability. The framework synergizes three core components: brightness-aligned preprocessing to reduce illumination-induced domain shifts, hybrid loss functions to improve feature discriminability across domains, and keypoint-aware re-ranking to refine retrieval results by compensating for neural networks’ localization uncertainty. Comprehensive evaluations on the University-1652 benchmark demonstrate the framework’s superiority; it achieves 82.50% Recall@1 and 84.28% AP for UAV→Satellite geo-localization, along with 90.87% Recall@1 and 80.25% AP for Satellite→UAV navigation. These results validate the framework’s capability to bridge UAV–satellite domain gaps while maintaining robust matching precision under heterogeneous imaging conditions, providing a viable solution for practical applications such as UAV navigation in GNSS-denied environments. Full article

Minimizing energy consumption remains a critical challenge in wireless sensor networks (WSNs) because of their reliance on nonrechargeable batteries. Clustering-based hierarchical communication has been widely adopted to address this issue by improving residual energy and balancing the network load. In this architecture, cluster heads (CHs) are responsible for data collection, aggregation, and forwarding, making their optimal selection essential for prolonging network lifetime. The effectiveness of CH selection is highly dependent on the choice of metaheuristic optimization method and the design of the fitness function. Although numerous studies have applied metaheuristic algorithms with suitably designed fitness functions to tackle the CH selection problem, many existing approaches fail to fully capture both the spatial distribution of nodes and dynamic energy conditions. To address these limitations, we propose the binary secretary bird optimization clustering (BSBOC) method. BSBOC introduces a binary variant of the secretary bird optimization algorithm (SBOA) to handle the discrete nature of CH selection. Additionally, it defines a novel multiobjective fitness function that, for the first time, considers the Voronoi diagram of CHs as an optimization objective, besides other well-known objectives. BSBOC was thoroughly assessed via comprehensive simulation experiments, benchmarked against two advanced methods (MOBGWO and WAOA), under both homogeneous and heterogeneous network models across two deployment scenarios. Findings from these simulations demonstrated that BSBOC notably decreased energy usage and prolonged network lifetime, highlighting its effectiveness as a reliable method for energy-aware clustering in WSNs. Full article

The rapid increase in the deployment of Internet of Things (IoT) sensor networks has led to an exponential growth in data generation and an unprecedented demand for efficient resource management infrastructure. Ensuring end-to-end communication across multiple heterogeneous network domains is crucial to maintaining Quality of Service (QoS) requirements, such as low latency and high computational capacity, for IoT applications. However, limited computing resources at multi-access edge computing (MEC), coupled with increasing IoT network requests during task offloading, often lead to network congestion, service latency, and inefficient resource utilization, degrading overall system performance. This paper proposes an intelligent task offloading and resource orchestration framework to address these challenges, thereby optimizing energy consumption, computational cost, network congestion, and service latency in dynamic IoT-MEC environments. The framework introduces task offloading and a dynamic resource orchestration strategy, where task offloading to the MEC server ensures an efficient distribution of computation workloads. The dynamic resource orchestration process, Service Function Chaining (SFC) for Virtual Network Functions (VNFs) placement, and routing path determination optimize service execution across the network. To achieve adaptive and intelligent decision-making, the proposed approach leverages Deep Reinforcement Learning (DRL) to dynamically allocate resources and offload task execution, thereby improving overall system efficiency and addressing the optimal policy in edge computing. Deep Q-network (DQN), which is leveraged to learn an optimal network resource adjustment policy and task offloading, ensures flexible adaptation in SFC deployment evaluations. The simulation result demonstrates that the DRL-based scheme significantly outperforms the reference scheme in terms of cumulative reward, reduced service latency, lowered energy consumption, and improved delivery and throughput. Full article

Accurate calibration and validation of remote sensing soil moisture products critically depend on high-quality in situ measurements. However, effectively capturing representative soil moisture patterns across heterogeneous catchments using ground-based sensors remains a significant challenge. To address this, we propose a machine-learning-based framework for optimizing soil moisture sensor network deployment at the catchment scale. The framework was validated using Sentinel-1 SAR-derived soil moisture data within a humid catchment in southern China. Results show that a network of nine optimally placed sensors minimized prediction errors (RMSE: 7.20%), outperforming both sparser and denser configurations. The optimized sensor network achieved a 52.45% reduction in RMSE compared to random placement. Moreover, the optimal number of sensors varied with seasonal dynamics: the wet season required 11 sensors due to increased precipitation-induced spatial variability, whereas the dry season could be adequately monitored with only six sensors. The proposed optimization approach offers a cost-effective strategy for collecting reliable in situ data, which is essential for improving the accuracy and applicability of remote sensing products in catchment-scale soil moisture monitoring. Full article

The progressive proliferation of sensor deployment in distribution networks (DNs), propelled by the dual drivers of power automation and ubiquitous IoT infrastructure development, has precipitated exponential growth in real-time data generated by multisource heterogeneous (MSH) sensors within multilayer grid architectures. This phenomenon presents dual implications: large-scale datasets offer an enhanced foundation for reliability assessment and dispatch planning in DNs; the dramatic escalation in data volume imposes demands on the computational precision and response speed of traditional evaluation approaches. The identification of critical influencing factors under extreme operating conditions, coupled with dynamic assessment and prediction of DN reliability through MSH data approaches, has emerged as a pressing challenge to address. Through a brief analysis of existing technologies and algorithms, this article reviews the technological development of MSH data analysis in DNs. By integrating the stability advantages of conventional approaches in practice with the computational adaptability of artificial intelligence, this article focuses on discussing key approaches for MSH data processing and assessment. Based on the characteristics of DN data, e.g., diverse sources, heterogeneous structures, and complex correlations, this article proposes several practical future directions. It is expected to provide insights for practitioners in power systems and sensor data processing that offer technical inspirations for intelligent, reliable, and stable next-generation DN construction. Full article

Manufacturing enterprises implementing Industry 4.0 technologies face significant challenges in integrating heterogeneous maintenance data sources and deploying AI solutions effectively. While various AI methods exist for predictive maintenance, the fundamental challenge lies in creating a cohesive architecture that enables seamless data flow and AI deployment. This paper presents a standardized architecture framework with initial implementation steps at Toyota Motor Manufacturing France. The proposed architecture introduces a four-layer approach: (1) a unified data acquisition layer integrating IoT sensors, CMMS, and legacy systems through standardized interfaces (OPC UA/MQTT), (2) a data quality and standardization layer ensuring consistent formats and automated validation, (3) a modular AI deployment layer supporting anomaly detection (Wavelet-based analysis and Deep Learning) and remaining useful life prediction (LSTM networks), and (4) a maintenance workflow integration layer with bi-directional feedback. Key innovations include a unified maintenance data model, configurable data quality pipelines, and human-in-the-loop decision support. A conceptual validation suggests this architecture can improve integration efficiency and reduce equipment downtime. This research contributes to smart maintenance by providing a scalable architecture that balances interoperability, data quality, and practical deployment in brownfield environments. Full article

Anomaly detection in multivariate time series is a critical task across a range of real-world domains, such as industrial automation and the internet of things. These environments are generally monitored by various types of sensors that produce complex, high-dimensional time-series data with intricate cross-sensor dependencies. While existing methods often utilize sequence modeling or graph neural networks to capture global sensor relationships, they typically treat all sensors uniformly—potentially overlooking the benefit of grouping sensors with similar temporal patterns. To this end, we propose a novel framework called Multi-task Learning Anomaly Detection (MLAD), which leverages clustering techniques to group sensors based on their temporal characteristics, and employs a multi-task learning paradigm to jointly capture both shared patterns across all sensors and specialized patterns within each cluster. MLAD consists of four key modules: (1) sensor clustering based on sensors’ time series, (2) representation learning with a cluster-constrained graph neural network, (3) multi-task forecasting with shared and cluster-specific learning layers, and (4) anomaly scoring. Extensive experiments on three public datasets demonstrate that MLAD achieves superior detection performance over state-of-the-art baselines. Ablation studies further validate the effectiveness of the modules of our MLAD. This study highlights the value of incorporating sensor heterogeneity into model design, which contributes to more accurate and robust anomaly detection in sensor-based monitoring systems. Full article