Search Results (14,517)

Cybersecurity operations in IoT and edge environments require fast, evidence-grounded decisions under strict resource and trust constraints. While large language models can support triage and incident analysis, their parametric knowledge may be outdated and prone to hallucination. Retrieval-augmented generation (RAG) improves grounding by conditioning responses on retrieved evidence, but also introduces new risks such as knowledge-base poisoning, indirect prompt injection, and embedding leakage. Federated learning enables collaborative adaptation without centralizing sensitive data, motivating federated RAG (FedRAG) architectures for distributed cybersecurity deployments. This study presents a deployment-oriented scoping review of FedRAG for cybersecurity. The review follows PRISMA-ScR reporting guidance and synthesizes 82 studies published between 2020 and 2026, identified through keyword search and citation snowballing over OpenAlex, arXiv, and Crossref. We develop a taxonomy that clarifies the components of federated systems, deployment locations, trust boundaries, and protected assets. We further map the combined RAG+FL attack surface, summarize practical defenses and system patterns, and distill actionable guidance for secure, privacy-preserving, and efficient FedRAG deployment in real-world IoT and edge scenarios. Our synthesis highlights recurring trade-offs among robustness, privacy, latency, communication overhead, and maintainability, and identifies open research priorities in benchmark design, governance mechanisms, and cross-silo evaluation protocols for practical deployment. Full article

Rising urban temperatures have become a critical constraint to urban ecosystem resilience and livability due to rapid urbanization. This study proposes a novel intra-city zoning scheme, named component morphological blocks (CMBs), which classifies built-up areas into six types characterized by multidimensional urban canopy morphologies. The XGBoost-SHAP model, optimized via Bayesian tuning, was employed to examine the relative contributions of 16 potential driving variables to block-scale land surface temperature (LST). The results show that: (1) LST gradually increases with increasing building density in the warm seasons. The average building height (BH) exhibits a positive correlation with shaded area, thereby reducing LST on the block scale; (2) hotspots are mainly concentrated in function-oriented blocks with hotspot distribution indices of 1.85, 1.96, 1.24, and 1.14, respectively. Coldspots are largely observed in blue–green space in the warm seasons; (3) BH dominates the LST across seasons, while the building-related factors make a prominent impact on LST in warm seasons. The contribution of vegetation canopy density is followed by BH during autumn and winter (12.2%, 10.9%); (4) a distinct transition occurs between summer normalized difference built-up index (NDBI) and fractional vegetation cover around an NDBI of 0.1. In winter, the interaction between 2D and 3D vegetation factors indicates a shift in their relative contributions from negative to positive as they increase. This study demonstrates that CMBs serve as an effective choice for characterizing LST patterns at the block scale, providing insights for sustainable urban development aimed at mitigating the urban heat island effect. Full article

The rapid growth of Internet of Things (IoT) and Industrial IoT applications has intensified the demand for low-latency and reliable computation support for deadline-constrained periodic real-time tasks. While unmanned aerial vehicles (UAVs) enabling mobile edge computing (MEC) can reduce latency by bringing compute closer to data sources, terrestrial MEC deployments often suffer from limited coverage and poor adaptability to spatially heterogeneous demand. In this paper, we study a multiple-UAV-assisted MEC system serving cluster-based IoT networks, where cluster heads generate deadline-constrained periodic tasks for offloading under strict deadlines. To ensure practical feasibility in dense urban environments, we benchmark UAV mobility using a realistic 3D collision avoidance navigation graph with shortest-path execution, rather than assuming unconstrained continuous UAV motion in free space. On top of this benchmark, we systematically compare three multi-agent reinforcement learning (MARL) paradigms for joint navigation and periodic task offloading: (i) continuous 3D control MARL that outputs motion commands directly; (ii) discrete graph-based MARL that selects collision-free shortest paths; and (iii) asynchronous macro-action MARL. Using a high-fidelity 3D digital twin of San Francisco, we evaluate these paradigms under a unified protocol in terms of offloading success, end-to-end latency, and energy consumption. The results reveal clear performance trade-offs induced by realistic 3D collision avoidance constraints and provide actionable insights for designing UAV-assisted MEC systems supporting periodic real-time task offloading. Full article

Despite recent advances in artificial intelligence for agriculture, reliable crop recommendation remains constrained by limited access to soil diagnostics, insufficient integration of environmental context, and the absence of transparent, quantitative evaluation frameworks. This study addresses the research question: How can we integrate multiple indicators to generate accurate, explainable, and context-sensitive crop recommendations? To this end, we propose a multimodal decision-support framework that combines image-based soil texture classification with geospatial, and climatic information. A convolutional neural network was trained on a curated dataset of 3250 soil images aggregated from four publicly available sources, covering four primary soil texture classes, alongside tabular soil and nutrient data. The model was evaluated using 5-fold stratified cross-validation, achieving an average classification accuracy of 99.30% (standard deviation ≈ 0.66), and was further validated on an independent hold-out test set to assess generalization performance. To enhance practical applicability, the framework incorporates elevation, rainfall, temperature, and major soil nutrients, and employs a large language model to generate user-oriented, interpretable justifications for each recommendation. Crop recommendations were quantitatively evaluated using a novel Agronomic Suitability Score (ASS), which measures alignment across soil compatibility, climatic suitability, seasonal alignment, and elevation tolerance. Across six geographically diverse case studies, the framework achieved mean ASS values ranging from 3.76 to 4.96, with five regions exceeding 4.45, demonstrating strong agronomic validity, robustness, and scalability. A Streamlit-based application further illustrates the system’s ability to deliver accessible, location-aware, and explainable agronomic guidance. The results indicate that the proposed approach constitutes a scalable decision-support tool with significant potential for sustainable agriculture and food security initiatives. Full article

Access control systems rely increasingly on multimodal biometric and behavioral signals to enhance security and robustness against sophisticated attacks. However, when heterogeneous modalities provide conflicting evidence, such as valid biometric credentials accompanied by abnormal behavioral or acoustic patterns, traditional fusion strategies based on static thresholds or majority voting often fail, leading to false alarms or insecure authorization decisions. This paper addresses this critical limitation by proposing a contextual decision-making fusion framework designed to resolve conflicting multimodal evidence at the decision-making level. The proposed approach models access control as a decision-making problem in a context of uncertainty, where independent agents generate modality-specific evidence from authentication channels based on face, voice, and fingerprints. A centralized fusion mechanism integrates heterogeneous results using adaptive reliability weighting and contextual reasoning to resolve conflicts before operational decisions are made. Rather than treating each modality independently, the framework explicitly considers inconsistencies, uncertainties, and situational context when aggregating evidence. The framework is evaluated using public benchmarks, including VGGFace2, VoxCeleb2, and FVC2004, combined with controlled multimodal scenarios that induce conflicting evidence. Experimental results obtained under controlled contradiction scenarios show that the proposed fusion strategy reduces false alarms and improves decision consistency by approximately 18%. These results are interpreted within the scope of controlled multimodal simulations. Full article

The increasing demand for autonomous wireless sensors in Internet of Things (IoT) applications has intensified research on vibration energy harvesting, particularly in the low-frequency range where ambient vibrations are most prevalent. However, most vibration energy harvesters operate efficiently only at a single resonance mode, resulting in a narrow operational bandwidth and pronounced performance degradation under frequency detuning. To address this limitation, this paper proposes a multimodal hybrid piezoelectric–electromagnetic vibration energy harvester that exploits both the first and second resonance modes of a cantilever-based structure to achieve broadband low-frequency operation. The design is guided by the complementary utilization of strain-dominated and velocity-dominated regions associated with different vibration modes. Numerical modeling and finite element simulations are employed to investigate the influence of mass distribution, deformation characteristics, and relative velocity on energy conversion performance. A secondary cantilever carrying the electromagnetic coil is introduced to enhance the relative motion between the coil and the magnetic field, thereby extending the effective operational bandwidth. The experimental results demonstrate increased harvested power, improved energy conversion efficiency, and a significantly broadened effective frequency range compared to conventional single-mode piezoelectric and electromagnetic energy harvesters. Full article

Multi-UAV collaborative mapping in communication-constrained indoor environments is often hampered by a trade-off between overall map refinement and the timely completion of safety-relevant shared regions. In high-density or unmapped areas, network congestion can delay the updates that matter most for close-proximity coordination, because standard bandwidth allocation does not distinguish between general map refinement and hotspot-related spatial data. To address this issue, we propose a resilient scheduling framework that prioritizes globally useful map updates while improving safety-relevant hotspot completeness under unreliable links. At its core is a Safety Reserve allocation strategy for “hotspot” submaps—areas where UAV trajectories overlap or approach unknown frontiers. By enforcing this reserve, the system directs a limited uplink budget to hotspot-related updates earlier during congestion. To remain useful under packet loss, we implement a prefix-decodable hierarchical data structure over a lightweight stateless protocol, allowing immediate fusion of valid partial updates. The framework identifies hotspots using feedback from a Lambda-Field risk model and a truncated least squares solver with graduated non-convexity (TLS–GNC) pose-graph optimizer. Experiments on S3DIS and ScanNet under partition-based two-agent emulation show that the proposed method improves hotspot-band completeness and progressive mapping quality over the tested baselines, especially under packet loss. Full article

With the advancement of 5G and the Internet of Things (IoT), traditional upper-layer authentication mechanisms are vulnerable to attacks, while quantum computing threatens cryptographic security. Radio frequency fingerprint identification (RFFI) offers a physical-layer solution by exploiting inherent hardware imperfections. However, in complex electromagnetic environments, narrowband and especially agile interference (characterized by low power and narrow bandwidth) can severely distort fingerprint features, rendering conventional detection algorithms ineffective. To address this challenge, this paper proposes a novel interference detection framework tailored for Orthogonal Frequency Division Multiplexing (OFDM) systems. First, a signal transmission model incorporating non-ideal hardware characteristics (e.g., DC offset, I/Q imbalance) is established. Based on this model, we design an agile interference detection algorithm comprising two key components: (1) a time-series anomaly detection method that fuses multi-domain expert features (fractal, complexity, and high-order statistics) with machine learning, demonstrating superior performance over the traditional CME algorithm under narrowband interference, and (2) a progressive search segmental detection algorithm that, combined with reconstruction error features extracted by an autoencoder, effectively identifies low-power agile interference by appropriately trading-off computation time for detection sensitivity. Finally, an OFDM simulation platform is developed to validate the proposed methods. The results show that the segmental detection algorithm achieves reliable detection at a jammer-to-signal ratio (JSR) as low as −10 dB, significantly outperforming existing approaches and enhancing the robustness of RFFI in challenging interference environments. Full article

Complex products and production processes are intertwined and demand expressive, lifecycle-wide digital representations. The Asset Administration Shell emerged as a standard for Digital Twins (DTs), structuring heterogeneous data across cloud-based Industrial Internet of Things (IIoT) infrastructures. However, today’s deployments predominantly realize passive or reactive DTs, while intelligent behavior remains underexploited. This paper addresses this gap, proposing an end-to-end architecture operationalizing the DT Reference Model through the integration of machine-interpretable granulated industrial skills, which are semantically accumulated into a knowledge graph enabling discovery and reasoning, while a multi-agent system provides autonomous, utility-based negotiation via machine-to-machine interactions within a federated marketplace. The approach is applied in a real smart manufacturing demonstrator, combining order processes, production orchestration, and lifecycle documentation into a unified execution pipeline spanning IIoT-connected shopfloor assets and cloud-based services. Quantitative experiments evaluating negotiation latency, renegotiation robustness, and utility variation demonstrate stable, predictable behavior even under concurrent demand and failure scenarios. The architecture lays a foundation for interoperable, sovereign collaboration across value chains to realize shared production. The results underline the effectiveness of the tightly coupled enabler technologies realizing proactive, reconfigurable, and semantically enriched intelligent DTs. Full article

Telemedicine, driven by the Internet of Things (IoT) and wireless connectivity, is essential for managing cardiovascular diseases, where hypertension remains the primary risk factor. In preclinical research, rabbits are superior biological models compared to rodents due to their human-like lipid metabolism. However, continuous blood pressure monitoring in this species remains challenging. The gold-standard technique (direct carotid catheterization) requires terminal procedures, and indirect methods (Doppler, oscillometric) show limited agreement with direct measurements. Furthermore, commercially available implantable telemetry platforms, while enabling real-time monitoring in freely moving animals, require costly surgical implantation, specialized proprietary hardware, and post-operative recovery periods that may confound early hemodynamic data. To address these limitations, this study presents a low-cost, customizable, and minimally invasive monitoring system utilizing a pressure transducer in the central auricular artery. The device integrates an ESP32 microcontroller with IoT technology for digital signal processing and seamless wireless data transmission to the ThingSpeak cloud platform. Unlike implantable telemetry, the proposed approach avoids surgical implantation and its associated costs and recovery time, while still enabling continuous, real-time hemodynamic tracking throughout the experimental period. A pilot evaluation against the BIOPAC MP100 reference (carotid artery) demonstrated relative errors of 1.60% for mean arterial pressure, 8.58% for systolic blood pressure, and 2.43% for diastolic blood pressure. By reducing invasiveness and enhancing remote data accessibility, this system provides a promising framework for the preclinical evaluation of antihypertensive agents and cardiovascular mechanisms, bridging the gap between edge computing and remote clinical diagnostics. Full article

Building height is a key indicator of vertical urbanization and urban morphological complexity, yet accurately mapping building height at fine spatial resolution and large spatial scales remains challenging. This study proposes an attention-based deep learning framework (ABHNet) for building height estimation at a 10 m spatial resolution by integrating multi-source remote sensing data and socioeconomic information. The model jointly exploits Sentinel-1 synthetic aperture radar data, Sentinel-2 multispectral imagery, and point of interest (POI) data. The proposed framework is evaluated in Shanghai, a megacity with dense and vertically complex urban structures, using Baidu Maps-derived building height data as reference information. The results demonstrate that the proposed method achieves accurate building height estimation, with a root mean squared error (RMSE) of 3.81 m and a mean absolute error (MAE) of 0.96 m for 2023, and an RMSE of 3.30 m and an MAE of 0.78 m for 2019, indicating robust performance across different time periods. Also, this model is applied in two other cities (Changzhou and Guiyang) and the results indicate good performance. In addition, the expandability of the framework is examined by incorporating higher-resolution ZY-3 imagery, for which the spatial resolution was increased to 2.5 m, highlighting the potential extension of the model to heterogeneous data sources. Overall, this study demonstrates the effectiveness of attention-based deep learning and multimodal data fusion for large-scale and fine-resolution building height estimation using open-source data. Full article

Unmanned Aerial Vehicle (UAV)-assisted hierarchical federated learning (HFL) has emerged as a promising architecture for Internet of Things (IoT)-based smart agriculture, which enables scalable model training over large and sparse farmlands. In this setting, UAVs act as mobile edge servers, aggregating local updates from distributed agricultural IoT devices and relaying them to the cloud server. While HFL improves scalability and reduces communication overhead, it still faces critical security threats due to its reliance on public wireless channels and the vulnerability of model aggregation to malicious updates. In this paper, we propose a secure authentication scheme that integrates anomaly detection with elliptic curve cryptography (ECC)-based mutual authentication to protect both the communication and training phases. In the proposed scheme, UAVs authenticate participating clients before receiving their local models, then perform anomaly detection to identify and exclude malicious participants. If a client is found to be malicious, its identity credentials are revoked and broadcast by the cloud server to prevent future participation. The security of the proposed scheme is formally verified using Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, along with informal security analysis. The performance evaluation includes comparisons of security features, computation cost, and communication cost with other related schemes, and an experimental assessment of anomaly detection performance. The results demonstrate that our scheme provides strong security guarantees, low overhead, and effective malicious client detection, making it well suited for UAV-assisted HFL in smart agriculture. Full article

Fifth-generation (5G) networks are facing critical security challenges in device authentication for massive Internet of Things deployments while preserving privacy. Traditional federated learning approaches depend on the computationally expensive homomorphic encryption to protect model gradients, resulting in substantial latency and communication overhead, leading to impractical energy consumption for resource-constrained 5G devices. This paper proposes Zero-Knowledge Federated Learning (ZK-FL), eliminating homomorphic encryption by enabling devices to prove model correctness without revealing gradients. Our approach integrates zero-knowledge proofs with FL updates, where each device generates a proof ${\mathrm{Proof}}_i=\mathrm{ZK}({\mathrm{Gradient}}_i,{\mathrm{Hash}}_i)$, demonstrating computational integrity. The experimental results from 10,000 authentication attempts demonstrate ZK-FL achieves 78.4 ms average authentication latency versus 342.5 ms for homomorphic encryption-based FL (77% reduction), proof sizes of 0.128 kB versus 512 kB (99.97% reduction), and energy consumption of 284.5 mJ versus 6525 mJ (95% reduction), while maintaining 99.3% authentication success rate with formal privacy guarantees. These results demonstrate ZK-FL enables practical privacy-preserving authentication for massive-scale 5G deployment. Full article

As mobile networks increasingly support sustainable and green Internet of Things (IoT) applications, energy-efficient solutions that address coverage constraints have become paramount. Although backscatter communication (BackCom) offers a low-power option for IoT devices, particularly battery-less IoT nodes, it can suffer from limited coverage. To overcome this, we exploit aerial platforms (UAVs) integrated with non-orthogonal multiple access (NOMA) to enhance both coverage and spectral efficiency. In this paper, we propose a UAV-supported NOMA-enabled BackCom system to serve massive backscatter node (BN) networks. We aim to maximize system throughput by jointly optimizing the power allocation and reflection coefficients of the BNs, along with the trajectory and data collection locations of the UAV. We derive closed-form solutions for the reflection coefficients and the optimal collection locations of the UAV and achieve global optimality in power allocation by utilizing the Karush–Kuhn–Tucker (KKT) optimality conditions in conjunction with the golden-section search (GSS). In addition, we formulate the UAV trajectory optimization problem as a Traveling Salesman Problem (TSP) and propose an efficient low-complexity genetic algorithm (GA)-based solution. The numerical results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of sum-throughput rate and achieves an overall performance enhancement of $8.983$ dB, underscoring the potential of our approach for large-scale battery-less IoT deployments. Full article

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges. Resource-constrained devices face sophisticated threats but lack the computational capacity for advanced security analysis. This study investigates optimal security task allocation in Cognitive IoT (CIoT) networks. It specifically examines when IoT devices should process security tasks locally or offload them to Mobile Edge Computing (MEC) servers. The problem is formulated as a Continuous-Time Markov Decision Process (CTMDP). The study demonstrates that the optimal offloading policy has a threshold structure. Security tasks are offloaded to MEC servers when the offloading queue length is below a critical threshold, $k^{\ast }$. Otherwise, tasks are processed locally. This structural property is robust to changes in MEC server configurations and threat arrival patterns. It ensures an optimal and easily implementable security policy under the exponential model. Theoretical analysis establishes upper bounds on the performance of AI-based security controllers using the same models. The results also show that standard model-free Q-learning algorithms can recover optimal thresholds without any prior knowledge of the system parameters. Simulations across multiple reinforcement learning architectures, including Q-learning, State–Action–Reward–State–Action (SARSA), and Deep Q-networks (DQN), confirm that all methods converge to the predicted threshold. This empirically validates the analytical findings. The threshold structure remains effective under practical imperfections such as imperfect sensing and parameter estimation errors. Systems maintain 85% to 93% of their optimal performance. This work extends threshold Markov Decision Process (MDP) analysis from classical queuing theory to the context of CIoT security offloading. It provides optimal and practical policies and model-free algorithms for use by resource-constrained devices. Full article