Search Results (607)

In widely deployed Internet of Things (IoT) scenarios, physical-layer key generation (PLKG) serves as a useful complement to conventional cryptographic methods, yet it often suffers from a fundamentally low key generation rate, which becomes particularly severe in quasi-static environments. This low rate is mainly attributed to three key issues: (1) slow channel variations, which provide insufficient randomness and thus limit the key generation rate; (2) correlation between the legitimate channel and the eavesdropping channel, which reduces the uniqueness of the extracted key and further degrades the achievable rate; and (3) insufficient degrees of freedom in the key source, which constrain the key space. To address these challenges, this paper introduces the Dynamic Agile Reconfigurable Intelligent Surface Antenna into physical-layer key generation. By deploying metasurface antennas at both ends and independently applying random phase modulation, the scheme injects two-sided randomness, thereby mitigating the adverse effects of quasi-static channels and legitimate eavesdropper channel correlation. Moreover, by leveraging the dynamic, agile, and reconfigurable characteristics of the metasurface antennas in the key generation process, the proposed approach can further enhance the key generation rate while simultaneously resolving all three issues above. The proposed scheme is developed under a general setting where correlation exists between the legitimate and eavesdropping channels. A closed-form expression for the key capacity is rigorously derived, accompanied by detailed theoretical analysis and simulations. The results demonstrate the superiority of the proposed approach when applied to physical-layer key generation. Full article

The efficient and timely management of human evacuation during emergency events is an important area of research where the Internet of Things (IoT) can be of great value. Significant areas of application for optimum evacuation strategies include buildings, sports arenas, cultural venues, such as museums and concert halls, and ships that carry passengers, such as cruise ships. In many cases, the evacuation process is complicated by constraints on space and movement, such as corridors, staircases, and passageways, that can cause congestion and slow the evacuation process. In such circumstances, the Internet of Things (IoT) can be used to sense the presence of evacuees in different locations, to sense hazards and congestion, to assist in making decisions based on sensing to guide the evacuees dynamically in the most effective direction to limit or eliminate congestion and maximize safety, and notify to the passengers the directions they should take or whether they should stop and wait, through signaling with active IoT devices that can include voice and visual indications and signposts. This paper uses an analytical queueing network approach to analyze an emergency evacuation system, and suggests the use of the Pull Policy, which employs the IoT to direct evacuees in a manner that reduces downstream congestion by signalling them to move forward when the preceding evacuees exit the system. The IoT-based Pull Policy is analyzed using a realistic representation of evacuation from an existing commercial cruise ship, with a queueing network model that also allows for a computationally very efficient comparison of different routing rules with wide-ranging variations in speed parameters of each of the individual evacuees.Numerical examples are used to demonstrate its value for the timely evacuation of passengers within the confined space of a cruise ship. Full article

Uncertainty-aware decision-making increasingly relies on multimodal sensing pipelines that must fuse correlated measurements, propagate uncertainty, and trigger reliable control actions. This study develops a unified mathematical framework for multimodal data fusion and Bayesian decision-making under uncertainty. The approach integrates adaptive Covariance Intersection (aCI) for correlation-robust sensor fusion, a Gaussian state–space backbone with Kalman filtering, heteroskedastic Bayesian regression with full posterior sampling via an affine-invariant MCMC sampler, and a Bayesian likelihood-ratio test (LRT) coupled to a risk-sensitive proportional–derivative (PD) control law. Theoretical guarantees are provided by bounding the state covariance under stability conditions, establishing convexity of the aCI weight optimization on the simplex, and deriving a Bayes-risk-optimal decision threshold for the LRT under symmetric Gaussian likelihoods. A proof-of-concept agro-environmental decision-support application is considered, where heterogeneous data streams (IoT soil sensors, meteorological stations, and drone-derived vegetation indices) are fused to generate early-warning alarms for crop stress and to adapt irrigation and fertilization inputs. The proposed pipeline reduces predictive variance and sharpens posterior credible intervals (up to 34% narrower 95% intervals and 44% lower NLL/Brier score under heteroskedastic modeling), while a Bayesian uncertainty-aware controller achieves 14.2% lower water usage and 35.5% fewer false stress alarms compared to a rule-based strategy. The framework is mathematically grounded yet domain-independent, providing a probabilistic pipeline that propagates uncertainty from raw multimodal data to operational control actions, and can be transferred beyond agriculture to robotics, signal processing, and environmental monitoring applications. Full article

To strengthen the safety management and control of explosives in underwater blasting construction sites, this study proposes an improved YOLOv11-based network named CRS-Y, designed to enhance the detection accuracy of explosives in complex underwater environments and improve the recognition capability of multi-scale targets. To address the limitations of traditional object detection methods in handling complex backgrounds and low-resolution targets, a lightweight re-parameterized vision transformer was integrated into the C3K module, forming a novel CSP structure (C3K-RepViT) that enhances feature extraction under small receptive fields. In combination with the Efficient Multi-Scale Attention (EMSA) mechanism, the model’s spatial feature representation is further strengthened, enabling a more effective understanding of objects in complex scenes. Furthermore, to reduce the computational cost of the P2 feature layer, a new convolutional structure named SPD-DSConv (Space-to-Depth Depthwise Separable Convolution) is proposed, which integrates downsampling and channel expansion within depthwise separable convolution. This design achieves a balance between parameter reduction and multidimensional feature learning. Finally, the Inner-IoU loss function is introduced to dynamically adjust auxiliary bounding box scales, accelerating regression convergence for both high-IoU and low-IoU samples, thereby optimizing bounding box shapes and localization accuracy while improving overall detection performance and robustness. Experimental results demonstrate that the proposed CRS-Y model achieved superior performance on the VOC2012, URPC2020, and self-constructed underwater blasting datasets, effectively meeting the real-time detection requirements of underwater blasting construction scenarios while exhibiting strong generalization ability and practical value. Full article

Detecting botnets in IoT environments is difficult because most intrusion detection systems treat network events as independent observations. In practice, infections spread through device relationships and evolve through distinct temporal phases. A system that ignores either aspect will miss important patterns. This paper explores a hybrid architecture combining Graph Neural Networks with Long Short-Term Memory networks to capture both structural and temporal dynamics. The GNN component models behavioral similarity between traffic flows in feature space, while the LSTM tracks how patterns change as attacks progress. The two components are trained jointly so that relational context is preserved during temporal learning. We evaluated the approach on two datasets with different characteristics. N-BaIoT contains traffic from nine devices infected with Mirai and BASHLITE, while CICIoT2023 covers 105 devices across 33 attack types. On N-BaIoT, the model achieved 99.88% accuracy with F1 of 0.9988 and Brier score of 0.0015. Cross-validation on CICIoT2023 yielded 99.73% accuracy with Brier score of 0.0030. The low Brier scores suggest that probability outputs are reasonably well calibrated for risk-based decision making. Consistent performance across both datasets provides some evidence that the architecture generalizes beyond a single benchmark setting. Full article

In practical deployments of decentralized federated learning (FL) in Internet of Things (IoT) environments, the non-independent and identically distributed (Non-IID) nature of client-local data limits model performance. Furthermore, concept drift further exacerbates complexity and introduces temporal uncertainty that significantly degrades convergence and generalization. Existing approaches, which mainly rely on model-level similarity or static clustering, struggle to disentangle inherent data heterogeneity from dynamic distributional shifts, resulting in poor adaptability under drift scenarios. This paper proposes FedPLC, a novel FL framework that introduces two mechanism-level innovations: (i) Prototype-Anchored Representation Learning (PARL), a strategy inspired by Learning Vector Quantization (LVQ) that stabilizes the representation space against label noise and distributional shifts by aligning sample embeddings with class prototypes; and (ii) Label-wise Dynamic Community Adaptation (LDCA), a fine-grained adaptation mechanism that dynamically reorganizes classifier heads at the label level, enabling rapid personalization and drift-aware community evolution. Together, PARL and LDCA enable FedPLC to explicitly disentangle static Non-IID heterogeneity from temporal concept drift, achieving robust and fine-grained adaptation for large-scale IoT/edge client populations. Our experimental results on the Fashion-MNIST, CIFAR-10, and SVHN datasets demonstrate that FedPLC outperforms the state-of-the-art federated learning methods designed for concept drift in both abrupt drift and incremental drift scenarios. Full article

The health and operational continuity of emergency responders are fundamental pillars of sustainable and resilient disaster management systems. These personnel operate in high-risk environments, exposed to intense physical, environmental, and psychological stress. This makes it crucial to monitor their health to safeguard their well-being and performance. Traditional methods, which rely on intermittent, voice-based check-ins, are reactive and create a dangerous information gap regarding a responder’s real-time health and safety. To address this sustainability challenge, the convergence of the Internet of Things (IoT) and wearable biosensors presents a transformative opportunity to shift from reactive to proactive safety monitoring, enabling the continuous capture of high-resolution physiological and environmental data. However, realizing a field-deployable system is a complex “system-of-systems” challenge. This review contributes to the field of sustainable emergency management by analyzing the complete technological chain required to build such a solution, structured along the data workflow from acquisition to action. It examines: (1) foundational health sensing technologies for bioelectrical, biophysical, and biochemical signals; (2) powering strategies, including low-power design and self-powering systems via energy harvesting; (3) ad hoc communication networks (terrestrial, aerial, and space-based) essential for infrastructure-denied disaster zones; (4) data processing architectures, comparing edge, fog, and cloud computing for real-time analytics; and (5) visualization tools, such as augmented reality (AR) and heads-up displays (HUDs), for decision support. The review synthesizes these components by discussing their integrated application in scenarios like firefighting and urban search and rescue. It concludes that a robust system depends not on a single component but on the seamless integration of this entire technological chain, and highlights future research directions crucial for quantifying and maximizing its impact on sustainable development goals (SDGs 3, 9, and 11) related to health, sustainable cities, and resilient infrastructure. Full article

Air pollution is a growing environmental issue in densely populated urban areas worldwide. Rapid population growth and the consequent increase in energy demand, emissions from industrial activities and vehicular traffic, and the reduction in vegetation cover have in recent years led to increasing concerns about quality of life, especially due to serious health problems associated with respiratory diseases. This study focuses on air quality in the city of Turin in north-western Italy. Continuous one-year monitoring, which collected approximately two million georeferenced data points, was possible using specific devices—palm-sized, wearable, and commercially available sensors—in different parts of the city. This enabled the assessment of the geographical and seasonal distributions of the most commonly studied air pollutants, namely particulate matter (PM) of three size fractions, nitrogen dioxide (NO₂), and total volatile organic compounds (TVOCs). The results highlight that the north-western zone and the urban centre are the most polluted areas. In particular, seasonal variations suggest that space heating and cooling systems, together with industrial activities, are the main contributors, more so than vehicular traffic. In this context, handheld devices in an IoT context can provide a reliable description of the spatial and temporal distribution of common air pollutants. Full article

Intrusion detection in Internet of Things (IoT) environments is challenged by high-dimensional traffic, heterogeneous attack behaviors, and severe class imbalance. To address these issues, this paper proposes EHFOA-ID, an intrusion detection framework driven by an Enhanced HawkFish Optimization Algorithm integrated with a hybrid deep ensemble. The proposed optimizer jointly performs feature selection and hyperparameter tuning using adaptive exploration–exploitation balancing, Lévy flight-based global searching, and diversity-preserving reinitialization, enabling efficient navigation of complex IoT feature spaces. The optimized features are processed through a multi-view ensemble that captures spatial correlations, temporal dependencies, and global contextual relationships, whose outputs are fused via a meta-learner to improve decision reliability. This unified optimization–learning pipeline reduces feature redundancy, enhances generalization, and improves robustness against diverse intrusion patterns. Experimental evaluation on benchmark IoT datasets shows that EHFOA-ID achieves detection accuracies exceeding 99% on UNSW-NB15 and 98% on SECOM, with macro-F1 scores above 0.97 and false-alarm rates reduced to below 2%, consistently outperforming state-of-the-art intrusion detection approaches. Full article

There is increasing interest in improving the reliability of short-range wireless links in dense IoT deployments, where BLE is widely used due to its low power consumption and robust GFSK modulation. For this purpose, this work presents a novel Orthogonal Space-Time (OST) scheme for transmission and detection of BLE signals while preserving the BLE GFSK waveform and modulation constraints. The proposed signal processing system integrates advanced OST coding techniques with nonlinear GFSK modulation to achieve high-quality communication while maintaining phase continuity. This implies that the standard BLE GFSK modulator and demodulator blocks can be reused, with additional processing introduced only in the multi-antenna encoder and combiner. A detailed theoretical analysis demonstrates the feasibility of employing the Rayleigh fading channel model in BLE communications and establishes the BER performance bounds for various MIMO configurations. Simulations confirm the advantages of the proposed OST-GFSK signal processing scheme, maintaining a consistent performance when compared with OST linear modulation approaches under Rayleigh fading channels. As a result, the proposed IoT-enabling technology integrates the advantages of widely used OST linear modulation with nonlinear GFSK modulation required for BLE. Full article

The rapid advancement of low-Earth-orbit (LEO) satellite constellations and unmanned aerial vehicles (UAVs) has positioned space–air–ground integrated networks as a key enabler of large-scale IoT services. However, ensuring reliable end-to-end operation remains challenging due to heterogeneous IoT–UAV link conditions and rapidly varying satellite visibility. This work proposes a two-stage optimization framework that jointly minimizes UAV energy consumption during IoT data acquisition and ensures stable UAV–LEO offloading through a demand-aware satellite association strategy. The first stage combines gradient-based refinement with combinatorial path optimization, while the second stage triggers handover only when the remaining offloading demand cannot be met. Simulation results show that the framework reduces UAV energy consumption by over 20% and shortens flight distance by more than 30% in dense deployments. For satellite offloading, the demand-aware strategy requires only 2–3 handovers—versus 7–9 under greedy selection—and lowers packet loss from 0.47–0.60% to 0.13–0.20%. By improving both stages simultaneously, the framework achieves consistent end-to-end performance gains across varying IoT densities and constellation sizes, demonstrating its practicality for future SAGIN deployments. Full article

In the era of the Internet of Things (IoT), video surveillance, as a vital component of smart cities and public security systems, faces the critical challenge of efficiently detecting abnormal behaviors within massive video streams. However, existing weakly supervised video anomaly detection methods are often limited by the scarcity of abnormal samples, the similarity between normal and abnormal segments, and the insufficient modeling of temporal dependencies. To address these challenges, this paper proposes a novel approach that integrates temporal structural attention with contrastive learning. On the one hand, causal masks and temporal decay weights are incorporated into the attention mechanism to explicitly constrain temporal relations and prevent future information leakage; on the other hand, positive/negative offsets and a contrastive learning strategy are employed to enhance the discriminability of abnormal segments in the latent space. Experiments conducted on multiple public video anomaly detection datasets validate the effectiveness of the proposed method, with results showing superior performance over existing mainstream models: the AUC increases to 98.1%, ACC reaches 96.1%, and the F1-score improves to 94.5%. These findings demonstrate that the proposed method can provide more intelligent, efficient, and reliable anomaly detection for IoT-based video surveillance, holding significant implications for public safety and intelligent monitoring. Full article

The global elderly population continues to increase, and the demand for leisure programs that support active aging is growing. In particular, group-based outdoor activities for seniors are often conducted in large public areas such as parks, ecological gardens, and cultural sites. As many of these spaces are now being integrated into smart city infrastructures equipped with IoT-based sensing and location-aware services, opportunities for data-driven safety support are expanding. However, in these wide and crowded environments, a small number of social workers are responsible for supervising many elderly participants, which creates monitoring blind spots. In addition, age-related cognitive and physical decline increases the risk of wandering and sudden health deterioration, making timely detection and response difficult. To address this problem, we propose a safety monitoring system for seniors. The system is based on a cloud platform that collects location data from GPS modules and motion information from embedded sensors on mobile devices. It provides real-time tracking of each participant and periodically evaluates their safety state. When abnormal conditions are detected, alerts are delivered to both social workers and the corresponding senior. A prototype implementation, consisting of a cloud server and mobile applications for social workers and elderly users, has been developed. The system is evaluated through a field test conducted on a university campus. Full article

Smart farming relies heavily on IoT automation and data-driven decision making, but this growing connectivity also increases exposure to cyberattacks. Flow-based unsupervised intrusion detection is a privacy-preserving alternative to signature and payload inspection, yet it still faces three challenges: loss of subtle anomaly cues during Autoencoder (AE) compression, instability of fixed reconstruction-error thresholds, and performance degradation of clustering in noisy high-dimensional spaces. To address these issues, we propose a fog-aware two-stage hierarchical AE with latent-space gating, followed by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for attack categorization. A shallow AE compresses the input into a compact 21-dimensional latent space, reducing computational demand for fog-node deployment. A deep AE then computes reconstruction-error scores to isolate malicious behavior while denoising latent features. Only high-error latent vectors are forwarded to DBSCAN, which improves cluster separability, reduces noise sensitivity, and avoids predefined cluster counts or labels. The framework is evaluated on two benchmark datasets. On CIC IoT-DIAD 2024, it achieves 98.99% accuracy, 0.9897 F1-score, 0.895 Adjusted Rand Index (ARI), and 0.019 Davies–Bouldin Index (DBI). To examine generalizability beyond smart farming traffic, we also evaluate the framework on the CSE-CIC-IDS2018 benchmark, where it achieves 99.33% accuracy, 0.9928 F1-score, 0.9013 ARI, and 0.0174 DBI. These results confirm that the proposed model can reliably detect and categorize major cyberattack families across distinct IoT threat landscapes while remaining compatible with resource-constrained fog computing environments. Full article

Multi-agent path planning for Unmanned Aerial Vehicles (UAVs) in agricultural data collection tasks presents a significant challenge, requiring sophisticated coordination to ensure efficiency and avoid conflicts. Existing multi-agent reinforcement learning (MARL) algorithms often struggle with high-dimensional state spaces, continuous action domains, and complex inter-agent dependencies. To address these issues, we propose a novel algorithm, Multi-Agent Transformer-based Soft Actor–Critic (MATRS). Operating on the Centralized Training with Decentralized Execution (CTDE) paradigm, MATRS enables safe and efficient collaborative data collection and trajectory optimization. By integrating a Transformer encoder into its centralized critic network, our approach leverages the self-attention mechanism to explicitly model the intricate relationships between agents, thereby enabling a more accurate evaluation of the joint action–value function. Through comprehensive simulation experiments, we evaluated the performance of MATRS against established baseline algorithms (MADDPG, MATD3, and MASAC) in scenarios with varying data loads and problem scales. The results demonstrate that MATRS consistently achieves faster convergence and shorter task completion times. Furthermore, in scalability experiments, MATRS learned an efficient “task-space partitioning” strategy, where the UAV swarm autonomously divides the operational area for conflict-free coverage. These findings indicate that combining attention-based architectures with Soft Actor–Critic learning offers a potent and scalable solution for high-performance multi-UAV coordination in IoT data collection tasks. Full article