Search Results (821)

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving edge intelligence because it enables geographically distributed devices to collaboratively train a shared model without transferring raw data to a central cloud. This capability is particularly valuable for 5G and emerging 6G networks, where edge-native services are required to satisfy stringent latency, bandwidth, and privacy constraints while operating on highly heterogeneous devices and time-varying wireless channels. In practice, however, synchronous FL is often constrained by straggling clients with limited computation capability or unfavorable communication conditions, which increases round latency and reduces overall resource efficiency. To address this challenge, this study develops a rigorously structured framework for dynamic client selection and radio resource allocation in heterogeneous wireless edge environments. Each FL round is formulated as a latency-aware scheduling problem that jointly captures local computation time, uplink transmission time, minimum participation constraints, and resource block assignment. On this basis, we propose a Dynamic Client Selection and Resource Allocation (DCS-RA) method that integrates computation-aware, channel-aware, and fairness-aware scoring with greedy resource block allocation guided by marginal completion time reduction. The study further provides a clear methodological structure, workflow visualization, literature-grounded justification, dataset documentation, and uncertainty-aware result reporting. Under the reported simulation setting with 100 clients and 20 resource blocks, DCS-RA reduces the average round completion time from 1.92 s to 1.55 s on MNIST and from 2.02 s to 1.57 s on CIFAR-10, corresponding to improvements of 19.39% and 22.47%, respectively. Standard deviation reductions of 70.59% and 80.77% further indicate improved round-to-round stability and more reliable training behavior. These results support the central conclusion that lightweight joint scheduling can materially improve wall-clock FL efficiency in heterogeneous 5G/6G edge networks. Full article

Fine-grained, authenticated, traceable, and efficient encrypted access control is indispensable for secure data sharing in mobile edge cloud networks, especially for resource-constrained data requesters. Despite the prevalence of outsourced ciphertext-policy attribute-based encryption (CP-ABE) solutions, existing schemes still suffer from critical practical limitations. First, requester-side transformation keys are typically unverified prior to computationally expensive outsourced decryption operations. Second, commitment-based verification mechanisms fail to validate the identity of data publishers. Third, the online computational overhead scales linearly with either the requester attribute set or the policy-matching set, severely degrading practical efficiency. To address these issues, this paper proposes ePoFSC, a novel policy-oriented functional signcryption scheme for authenticated encrypted data sharing in mobile edge cloud scenarios. The proposed ePoFSC scheme integrates pre-auditing and caching mechanisms for requester trapdoors before online access requests, enabling constant-time operations for request generation, request verification, and request header construction independent of requester attribute scale. In the outsourced decryption phase, ePoFSC offloads all costly pairing and exponentiation operations with constant computational complexity, leaving only lightweight policy-dependent group multiplications for terminal requesters. Furthermore, ePoFSC tightly couples decryption verification with publisher authentication and requester traceability to realize comprehensive access accountability. Rigorous security analysis formally validates the confidentiality, publisher-side unforgeability, and requester traceability of the proposed scheme. Extensive experimental evaluations on the BLS12-381 curve verify that ePoFSC achieves prominent performance superiority over existing state-of-the-art schemes in both the encryption and data recovery phases. Full article

The outsourcing of machine learning tasks, such as Support Vector Machine (SVM) training, to cloud platforms poses significant security challenges, primarily concerning the confidentiality of sensitive training data and the integrity of computation results returned by potentially malicious servers. To address these challenges, this paper proposes a lightweight, privacy-preserving, and verifiable SVM training scheme designed for resource-constrained clients. Our scheme leverages a replicated secret sharing protocol to securely distribute training data and model parameters across multiple non-colluding servers, executing the entire collaborative training process in the share domain without leaking plaintext information. Furthermore, to guarantee computational correctness, we introduce a novel interval-based index point storage strategy combined with a bilinear mapping-based parameter label consistency check. This verifiable mechanism enables clients to perform sampled, lightweight audits of the cloud’s intermediate training states and final outputs. Experimental evaluations on multiple typical datasets demonstrate that the proposed scheme maintains stable classification performance while achieving an order-of-magnitude decrease in training runtime compared with existing ciphertext-based methods, offering a highly configurable trade-off among verification coverage, computational overhead, and storage cost. Full article

Healthcare and urban infrastructure are increasingly supported by Internet of Things-based sensing systems, in which heterogeneous physiological, environmental, and transmission-level data require reliable, low-latency processing. Existing works typically treat medical IoT sensing, smart-city anomaly detection, or edge-cloud offloading as isolated problems, thereby failing to support integrated sensing scenarios in shared smart environments. This paper introduces a Hybrid Edge–Cloud Intelligence Framework (HECIF) for reliable sensing and data fusion in smart healthcare and urban IoT environments. HECIF introduces modality-specific feature extraction, adaptive offloading to the edge cloud, an attention mechanism for multimodal fusion, and a reliability-weighted decision layer that incorporates sensor quality and transmission delay. The framework was tested on three publicly available datasets: the Multi-Sensor Medical IoT dataset for physiological signal classification, the UrbanIoT Anomaly dataset for urban anomaly detection, and the IoT Sensor Cloud Data Transmission dataset for offloading decision modeling, all from Kaggle. It achieved a 92.1% accuracy, 91.3% F1-score, 93.8% AUC, and 0.821 Matthews correlation coefficient in a simulated edge cloud environment, outperforming the baselines (logistic regression, random forest, XGBoost, MLP, CNN/LSTM). The framework also reduced the mean inference time to 29 ms, down from 142 ms in the cloud-only configuration, while achieving a throughput of 1150 samples per second. The results show that reliability-aware edge cloud fusion is feasible for cross-domain IoT sensing with a simulated edge cloud. However, physical device validation and real-world IoT network validation are still required before practical deployment. Full article

Modern indoor spaces increasingly contain curved walls, slanted surfaces, nested rooms and other non-Manhattan structures, making room-level subdivision from 3D point clouds challenging. Existing projection-based, primitive-based and semantic methods often rely on Manhattan assumptions, explicit structural labels or local geometric heuristics, which may lead to fragmented spatial units and unstable boundaries in complex scenes. Here, we propose a topology-driven voxel partitioning framework that reformulates indoor space subdivision as controlled connectivity disconnection within a topological closure. The central idea is to first construct a closed voxelated space domain and then selectively disconnect it at near functional openings and topological bottlenecks, rather than partitioning space only from local geometric cues. Within this framework, classical operations are reorganized under topological constraints as follows: adaptive region growing with aperture-sensitive spherical kernels generates initial spatial units, boundary-anisotropic watershed completion restores unlabeled boundary regions within interior domain, and label reassignment regularizes shared interfaces by minimizing discrete contact areas. Experiments on real-world and synthetic datasets show that the method produces stable room-level subdivisions across most Manhattan and non-Manhattan scenes. Cases with narrow bottlenecks or abrupt geometric narrowing still reduce detection-level precision and recall, indicating remaining limitations in highly constrained spatial configurations. Overall, the proposed framework offers a useful topological modeling perspective for indoor space subdivision in complex non-Manhattan environments. Full article

The calculation of costs for a complex edge–cloud platform operated by a telecommunications provider, which both manages networks and delivers communication and cloud services, is of fundamental importance. This work systematically addresses the evaluation of CapEx and OpEx, and thus the total cost of ownership, from a multi-period perspective for fully on-premises or hybrid edge–cloud platforms, including multi-cloud options. It also considers the computation of hourly costs of elementary resource units (e.g., vCPU), enabling cost allocation to applications and supporting service pricing. The proposed model and methods are general and adaptable to different contexts, requiring only the population of the data model and, as a next step, the platform instantiation for specific cases. The approach enables comparative analyses of multi-period deployment alternatives, including different degrees of decentralization, varying shares of public cloud resources, and sensitivity analyses on key parameters, particularly infrastructure costs. This work is developed within the IPCEI-CIS TIM Edge–Cloud Continuum project and currently is in the research phase, without application in TIM’s operational or commercial domains. Full article

The Internet of Vehicles (IoV) is reshaping intelligent transportation through pervasive connectivity, real-time data exchange, cooperative perception, and vehicle–edge–cloud services, while also expanding cybersecurity and privacy risks across heterogeneous cyber–physical environments. This paper presents a PRISMA 2020-informed systematic review of IoV security and privacy protection research. A cross-layer and lifecycle-oriented analytical framework is developed by integrating a four-layer IoV architecture—sensing layer, network access layer, coordinative computing layer, and application layer—with a five-stage data lifecycle covering data collection, transmission, storage, usage, and disposal. Based on this framework, the paper examines representative threat surfaces, vehicle-to-everything (V2X) communication security, public key infrastructure (PKI) based authentication, trust management, privacy-preserving data sharing, intrusion detection, active defense, and AI-assisted security analytics. Privacy-preserving mechanisms, including differential privacy, federated learning, blockchain, homomorphic encryption, and secure multi-party computation, are further compared in terms of deployment layer, lifecycle stage, real-time suitability, and representative performance evidence. In addition, the review discusses the engineering relevance of UNECE WP.29 R155/R156, ISO/SAE 21434, and related national standards, with emphasis on compliance evidence, over-the-air (OTA) governance, supply-chain coordination, and lifecycle cybersecurity management. The review shows that no single protection mechanism can simultaneously satisfy the requirements of real-time performance, scalability, privacy preservation, trustworthiness, and regulatory compliance in dynamic IoV environments. Future research should emphasize lightweight and adaptive protection, cross-layer trust coordination, privacy–utility co-optimization, trustworthy AI-assisted security operations, and evidence-based lifecycle governance. This review provides a structured reference for researchers and a practical basis for secure and privacy-aware IoV system design. Full article

As the Kingdom of Saudi Arabia (KSA) accelerates its transition toward smart mobility under Vision 2030, establishing a robust digital infrastructure is paramount for the safe deployment of autonomous vehicles (AVs). High-definition (HD) maps serve as a critical foundation for this infrastructure, yet their deployment is severely bottlenecked by extreme operational costs, massive data processing payloads, and rapid environmental variations across vast highway networks. To address these challenges, this paper proposes a comprehensive, localized national strategy structured around three key tasks. First, it establishes a unified national HD map standard to guarantee seamless interoperability and data sharing among competing AV manufacturers and government transport authorities. Second, it implements an AI-powered baseline workflow using Mobile Mapping Systems (MMS) for high-fidelity static map construction, anchored and validated within designated pilot zones, including the King Abdulaziz University campus and key sectors in the Kingdom. Third, it deploys a decentralized, vision-based crowdsourcing system that leverages active public and commercial vehicle fleets for real-time map maintenance. By integrating a sovereign edge-cloud AI infrastructure that respects local Personal Data Protection Law (PDPL), this framework bridges the gap between high-accuracy baseline mapping and long-term economic sustainability, offering an actionable technical roadmap for scaling a resilient digital transport layer across the Kingdom. Full article

Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balance detection accuracy, privacy, and resource overhead during continuous operation, and often have limited situational awareness and inflexible management. We propose EdgeElderCare, a resource-aware, scene-adaptive edge-cloud collaborative system for continuous elderly safety and health monitoring. Its contributions are threefold: (1) a scene-adaptive multi-sensor task-sharing architecture that deploys vision-based fall detection in public areas and privacy-aware millimeter-wave radar in private spaces. Combined with edge-side task scheduling, it provides spatially complementary coverage of public and private areas, mitigates the accuracy–privacy conflict, and reduces computing and bandwidth consumption relative to data-level fusion; (2) a lightweight myocardial infarction detection module deployed on an edge platform, enabling local ECG analysis with low resource overhead; (3) a 3D digital-twin edge-cloud management platform that maps multi-source sensing data to a virtual scene in real time and supports hierarchical visual alerting. Experiments in a real nursing home environment show that the system operated stably on resource-constrained edge hardware: UWB positioning achieved centimeter-level RMSE, visual fall detection reached a recall of 0.90, millimeter-wave radar fall detection achieved accuracy, and F1 above 0.90, and myocardial infarction detection exceeded 0.99 accuracy on the public PTB/PTB-XL benchmark. These results indicate an engineering-feasible approach to intelligent elderly care. Larger-scale and longer-term validation remains the focus of future work. Full article

Industry 4.0 is speeding up the move to connected, data-driven, and automated production, where the Internet of Things (IoT) enables sensing, communication, and real-time support for decisions. At the same time, rapid growth in industrial IoT studies has led to scattered technologies, architectures, and results. This paper fills this gap through a systematic literature review on IoT for Industry 4.0. It also helps readers compare methods and choose suitable building blocks for real deployments today. We focus on key technologies, integration architectures, application areas, challenges, trends, and reported benefits. Using PRISMA 2020, we searched five major databases (Scopus, MDPI, IEEE Xplore, ScienceDirect, and Web of Science) for 2020–2025 and found 584 records. After removing duplicates and screening, we kept 96 peer-reviewed studies for detailed analysis. Results show that most studies use a layered stack that combines sensing/actuation, industrial networking, data collection pipelines, and analytics across edge, fog, and cloud resources. MQTT, OPC UA, CoAP, LPWAN, and 5G connectivity are often used for communication, while RAMI 4.0, IIRA, and similar layered models guide system design. Many architectures follow an edge–cloud pattern, with growing focus on digital twin/CPS links and security-by-design. Applications are mainly smart manufacturing, predictive maintenance, and logistics, with added work in energy management, Construction 4.0, and agri-food monitoring. The key barriers remain interoperability, data quality and evaluation gaps, cybersecurity risks, legacy integration, and deployment limits. The review points to future work on edge AI/TinyML, deterministic connectivity, scalable digital twins, trusted data sharing, and sustainable industrial IoT. Full article

As global markets navigate the dual transition of digitalization and sustainability, the risk of “digital greenwashing” has emerged as a critical corporate governance challenge. Utilizing a comprehensive dataset of Chinese A-share listed firms from 2018 to 2024—an ideal laboratory characterized by rapid regulatory shifts and unique state-market dynamics that provide highly generalizable insights for other emerging economies—this study empirically investigates whether corporate digital transformation acts as a genuine driver for Environmental, Social, and Governance (ESG) enhancement or merely serves as a symbolic disclosure tool. Fortified by rigorous identification strategies, including Propensity Score Matching and Lewbel heteroskedasticity-based instrumental variable estimations, the results confirm that digitalization serves as an incremental yet statistically significant driver for corporate sustainability. Crucially, mechanism analyses reveal a “full moderation” effect: the positive impact of digitalization on ESG performance is completely activated only in the presence of premium external assurance (e.g., Big 4 audits). Without high-quality IT auditing to act as a credibility enforcer and verify the substance of digital signals, technological adoption alone fails to yield significant ESG improvements. Furthermore, a nuanced structural asymmetry is identified: foundational data infrastructures (Cloud Computing and Big Data) directly enhance quantifiable Environmental and Governance metrics, whereas premium audits are strictly required to activate the “soft,” qualitative Social dimension. Finally, the synergy exhibits distinct boundary conditions. It is heavily concentrated within high-pollution industries where digital transition acts as a regulatory survival imperative rather than mere market expansion, and its reliance on external assurance is fundamentally driven by the market-signaling needs of non-State-Owned Enterprises (non-SOEs) rather than the policy-distorted mandates of SOEs. These findings offer critical theoretical extensions and policy implications for standardizing digital-audit infrastructures globally. Full article

This article explores the depiction of galloping horses in ancient art, focusing on a Greco-Bactrian gilt bronze finial known as the “Heavenly Horse.” By systematically classifying artistic gallop motifs—rearing gallop, flying gallop, landing gallop, and the anatomically accurate V-gallop—the study traces the evolution of equine imagery across Eurasia. Detailed analysis of the Heavenly Horse finial shows that its pose, musculature, and anatomical features closely correspond to the true suspension phase of a horse’s gallop, a finding supported by modern motion studies. High-resolution 3D scanning, surface diagnostics, and computational modeling indicate that the sculpture’s mass distribution was intentionally designed for dynamic balance and realism. The artifact’s stylistic features, such as the frontal forehead protuberance and S-shaped neck, align with descriptions of elite horse breeds in ancient Chinese and Central Asian sources. Symbolic motifs, including the vine-and-cloud scroll, further connect the object to broader Eurasian traditions of imperial and celestial symbolism. Comparative analysis with Saka and Chinese artifacts highlights both shared conventions and unique technical achievements. The study demonstrates how the Heavenly Horse finial integrates empirical observation, artistic tradition, and symbolic meaning, reflecting the exchange of ideas and technologies along the Silk Road. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. About 98 percent of large businesses will use cloud computing services in 2025 to enable remote working. The highly distributed structures of cloud computing are prone to attacks starting from weakened access control to data breaches. The sources making cloud systems vulnerable to attacks are public accessibility, auto scaling, and shared form of network architecture. Distributed Denial of Service (DDoS) is one of the most serious forms of attacks where multiple botnets get created simultaneously and flood massive requests for the cloud services. If the DDoS attack is not identified early it leads to the unavailability of cloud services, increased cost of migration, exhaustion of resources, and frequent violations of Service Level Agreements (SLAs). Hence, there is a need to detect DDoS at an early stage. Traditional machine learning models demand high computational power and larger memory capacity which make it unsuitable for a real-time cloud environment. This limitation is overcome by presenting a novel Quantum-Enhanced Knowledge Distillation framework (QKD) to detect DDoS attacks in cloud systems. QKD is a highly potential form of architecture which uses quantum computing to enhance the knowledge transfer between teacher and student models. The knowledge is extracted from the teacher model and quantum encoding of knowledge is performed. The complex correlation between the features of the traffic is extracted by applying the entanglement gates. The student model is trained considering the distillation loss and optimized until convergence. The simulation of the QKD is performed using DynamicCloudSim 3.0.3 simulator considering benchmark dataset CIC-DDoS2019and the performance is further validated using expected value analysis methodology. The performance of QKD is found to be promising toward performance metrics such as packet loss rate, attack detection time, attack recovery ratio, bandwidth utilization, and response time. Full article

Unmanned aerial vehicles (UAVs) are widely used for documentation, surveying, and 3D modeling in the built environment, yet their outputs often remain difficult to reuse for immersive comparison of alternative construction scenarios. This study presents a low-cost UAV-to-3DGS-to-VR workflow for constructing scenario-comparable immersive environments for built-environment review. The workflow combines multi-angle UAV imagery, point-cloud-based geometric anchoring, 3D Gaussian Splatting (3DGS), and Unity-based virtual reality (VR) to transform drone-captured reality into a reusable scene for controlled scenario comparison. The workflow is demonstrated in Middenbeemster, the central town of the Beemster polder World Heritage property. One present-condition scene (M0) and three alternative construction scenarios (M1 to M3) were created within a shared spatial reference. Reconstruction quality was assessed using PSNR and SSIM, and the VR scenes were further evaluated through eye-tracking, head-motion recording, and subjective ranking. The results indicate that the workflow can generate visually reliable and directly comparable immersive scenes from UAV data in this case study. Behavioral and subjective findings showed a consistent pattern, with M1 appearing more compatible than M2 and M3 in this pilot evaluation. The study contributes a pilot UAV-based workflow that links reality capture, immersive scenario comparison, and supplementary behavioral evidence within one process. Full article

Bi-temporal change detection (CD) in remote sensing (RS) aims to map image pairs at different times into a shared feature space to discriminate variant regions effectively. However, factors such as cloud interference may disrupt the feature distribution of RS images and cause pseudo-change problems. Existing public change detection datasets also pay less attention to such pseudo-change phenomena. To address the pseudo-change problems of CD applications, we propose a Domain Adversarial Anti-Interference Change Detection Network (DAAINet), which uses ResNet to extract multi-scale features from the original input images. Semantic features are then obtained and fed into a subsequent graph convolution module after soft clustering, by introducing a domain adversarial structure to align the feature space in RS images. In the graph convolution module, the association of node context is utilized to predict the adjacency relationship between objects. We collected data and constructed a real-world dataset called “Cloud Interference Change Detection” (CICD), which focuses on real bi-temporal remote sensing image data containing cloud interference and includes pseudo-changes caused by factors such as the presence of temporary objects and illumination changes. Experimental results demonstrate that our method is more robust and efficient compared to other state-of-the-art methods on two public CD datasets, and achieves state-of-the-art performance on the noise-corrupted CICD dataset, surpassing prior methods by up to 5.67%p in IoU and 1.42%p in recall. Full article