Search Results (1,281)

Cyber–Physical Systems (CPS), including smart grids and industrial control networks, must maintain secure and stable operations despite increasingly adaptive cyber threats. Existing moving target defense (MTD) approaches often rely on fixed reconfiguration strategies or flat learning architectures that fail to scale and do not explicitly ensure operational resilience under real-time constraints. This study proposes a resilience-oriented hierarchical multi-agent reinforcement learning (MARL) framework for adaptive MTD in CPS environments. The attacker–defender interaction is modeled as a partially observable stochastic game, enabling defenders to learn adaptive strategies with incomplete information. The proposed architecture consists of three layers: a strategic MARL layer that optimizes high-level defense parameters, a distributed k-winner-take-all coordination layer for low-latency defender selection, and a robust execution layer based on sliding-mode control to preserve physical system stability during reconfiguration. By decoupling strategic adaptation from real-time control, the framework improves scalability and supports resource-aware defense through selective agent activation. Extensive simulations with up to 50 defender agents demonstrate that the proposed approach achieves a defense success rate of 92.4%, reduces the response time by 15% compared with the random MTD, and lowers the energy consumption by 34% on average (up to 52% at N = 50) relative to the flat MARL. These results indicate that hierarchical MARL can significantly enhance CPS resilience by enabling adaptive, efficient, and operationally safe defenses against dynamic cyber-attacks. The proposed framework is particularly suitable for edge-enabled CPS environments with strict, real-time, and safety constraints. Full article

With the rapid development of artificial intelligence and Internet of Things technologies, smart libraries increasingly require low-latency and energy-efficient computing support for heterogeneous services such as access control, intelligent recommendation, indoor navigation, and book localization. To address the limitations of cloud-only processing, this paper investigates task-offloading optimization in a cloud-assisted mobile edge computing environment for smart library services. A three-tier cloud–edge–device collaborative architecture is first established, and the task-offloading problem is formulated as a multi-objective optimization problem that jointly minimizes task-completion delay and user-side energy consumption under latency, resource-capacity, and coverage constraints. To solve the dynamic decision-making problem, a preference-adaptive dueling double deep Q-network algorithm, termed PA-DDQN, is proposed by integrating preference conditioning, multi-head attention, a dueling architecture, and double Q-learning. Simulation results show that PA-DDQN achieves better performance than fixed offloading strategies and representative reinforcement-learning baselines. Under the heaviest task load, PA-DDQN reduces the average task-completion delay by 23.1% and 31.0% compared with D3QN and DDQN, respectively, while reducing energy consumption by 5.8% and 9.9%. It also improves the task success rate by 14.8% and 21.7%, demonstrating its effectiveness in enhancing service responsiveness, energy efficiency, and reliability in smart library MEC systems. Full article

Indoor evacuation under emergency conditions remains a challenging problem due to dynamic hazards, uncertain infrastructure availability, and variability in human behavior. Traditional evacuation systems rely heavily on centralized architectures, making them vulnerable to communication failures and delayed global decision making. To address these limitations, this paper proposes a novel framework termed Federated Edge-Semantic Learning for Decentralized Resilient Evacuation (FESL-DRE). The proposed framework distributes evacuation intelligence across edge nodes, enabling autonomous decision making without dependence on a central controller. It integrates semantic reasoning to transform raw sensor data into interpretable environmental states, federated learning to model behavioral patterns in a privacy-preserving manner, and a gossip-based coordination mechanism to propagate hazard information across neighboring nodes. An adaptive routing strategy is developed to account for hazard levels, crowd density, and human behavioral variability. The framework is evaluated using a simulation-based environment under dynamic hazard conditions and varying levels of node failure. Experimental results demonstrate that FESL-DRE achieves superior performance compared to classical and centralized adaptive methods, with improvements in evacuation success rate, reduced blocked movement attempts, and enhanced resilience under moderate infrastructure degradation. Furthermore, the proposed approach maintains low communication overhead and demonstrates promising scalability characteristics within the evaluated simulation environment. The results highlight the potential of decentralized intelligence for evacuation support and provide a foundation for future validation in realistic smart building and IoT-enabled environments. Full article

Smart-home environments require secure and reliable user authentication mechanisms to prevent unauthorized access and spoofing attacks. Traditional password- and PIN-based methods remain vulnerable to theft, replay attacks, and credential compromise. To address these challenges, this study proposes an intelligent voice-based authentication and anomaly detection framework for secure smart-home environments. The framework utilizes benchmark ASVspoof 2019 and ASVspoof 2021 datasets containing bona fide and spoofed speech samples. After preprocessing, discriminative acoustic features, including Mel-Frequency Cepstral Coefficients (MFCC) and Constant-Q Cepstral Coefficients (CQCC), are extracted and provided to a Hybrid CNN-LSTM model for speaker verification. An integrated anomaly detection module further enhances security by identifying replay, spoofing, and synthetic speech attacks. Access is granted only when the input voice is authenticated and classified as non-anomalous. Experimental results demonstrate the effectiveness of the proposed framework, achieving an overall accuracy of 97.2% and a macro-AUC of 0.972. The model also achieves low Equal Error Rates of 3.8%, 2.9%, and 2.1% across the evaluated classes, indicating robust spoof detection and anomaly generalization capabilities. These results highlight the suitability of the proposed framework for secure and intelligent smart-home access control applications. Full article

Smart manufacturing requires real-time monitoring of multidimensional forces at the interface between the tool and workpiece in computer numerical control (CNC) machining. In this study, an innovative iKIT wireless extrasensory toolholder is introduced that is capable of high-fidelity, in situ, high-frequency sensing and monitoring of the cutting force, torque, and two-way bending moments. The hardware design of the system is outlined, highlighting a high-bandwidth miniature wireless transmission method and noncontact power supply and energy storage solution suitable for rotating machining environments. To assess the system performance, comprehensive milling tests were performed on aluminum alloy materials, and the relationship between the process parameters and changes in multidimensional mechanical loads was thoroughly examined. The experimental findings demonstrate that the smart toolholder detects precisely how parameter variations affect the loads. Multidimensional mechanical signals (torque and two-way bending moments) show a strong positive correlation with the feed rate and axial depth of cut, confirming the impact of the material removal rate on the system loads. Conversely, these signals are negatively correlated with spindle speed, accurately reflecting the effects of thermal softening and a reduced friction coefficient in aluminum alloys during high-speed cutting. This study not only offers a dependable hardware framework for integrating miniaturized sensors into toolholders, but also delivers accurate data to support digital twin models and adaptive control in machining processes. Full article

Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principal–agent dynamics, and regulatory compatibility. We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value. Full article

Background/Objectives: The rapid expansion of digital evidence in modern investigations has created pressing challenges for maintaining integrity, traceability, chain of custody, and meaningful analysis across heterogeneous forensic artefacts. Conventional evidence management approaches often fall short in scalability, transparency, and the ability to correlate diverse digital evidence. This study presents BlockFECS, a blockchain-based proof-of-concept system for metadata-driven evidence correlation in digital forensics. Methods: BlockFECS uses Hyperledger Fabric to support auditable and tamper-resistant evidence management while capturing structured forensic metadata, including timestamps, locations, device IDs, user IDs, and file hashes. An off-chain weighted correlation algorithm assigns similarity scores between evidence pairs and classifies relationships as Related, Supplementary, Duplicate, or Unrelated. The system was evaluated using a simulated smart city accident scenario and tested for correctness, transaction latency, throughput, scalability trends, and concurrency behaviour across four computing environments. Results: Within the controlled proof-of-concept dataset, the correlation algorithm achieved 1.00 precision and recall for clear Related and Duplicate evidence relationships and high precision (0.90) for Supplementary relationships, although recall in this category was lower due to incomplete or noisy metadata. Performance testing showed that Create, Transfer, and Delete operations completed with sub-second latency, while correlation throughput exceeded 60 comparisons per second across all tested environments. Conclusions: The findings demonstrate the feasibility of combining blockchain-backed evidence integrity with lightweight metadata-driven forensic intelligence. BlockFECS contributes a proof-of-concept model for automating metadata-based evidence analysis while preserving provenance integrity and auditability, highlighting a promising direction for trustworthy and intelligent digital forensic investigation support. Full article

Rapid electric-vehicle adoption is reshaping urban energy and mobility systems, especially in multi-unit dwellings (MUDs), where concentrated charging in shared parking areas simultaneously stresses distribution transformers and amplifies the consequences of charger faults, battery thermal events, smoke spread, and emergency-access constraints. The central argument of this paper is that grid stress, resident-facing service quality, lifecycle cost, and fire-risk exposure in enclosed residential parking should be governed jointly rather than as four separate problems. To make that argument concrete, we develop an integrated framework that couples stochastic EV adoption, residential charging-behavior simulation, XGBoost demand forecasting, and linear-programming-based optimization for coordinated control, and we evaluate it through 1000 Monte Carlo trials on representative Turkish MUDs. Unmanaged charging triggers transformer overload at about 30% EV penetration, whereas coordinated control reduces peak demand by 44.7% (405 kW to 224 kW) and raises load factor from 0.40 to 0.68. Strict capacity protection exposes a sharp service–quality trade-off, with only 8.9% of users reaching 80% state of charge (SOC) by departure. Smart charging lowers upfront cost by about 55% ($200 vs. $439 per dwelling unit) and yields roughly $306 net present value per unit over ten years. Building on these results, we propose a five-pillar fire-risk-aware governance architecture—coordinated control, interoperability standards, time-of-use pricing, building–utility coordination, and monitoring—that turns coordinated charging into a preventive governance layer for reducing hazardous congestion in enclosed residential charging environments. Full article

Traditional Unmanned Aerial Vehicle (UAV) oblique photogrammetry for 3D real-scene modeling of historic cultural districts suffers from data gaps, insufficient texture, and poor accuracy in complex alleyway environments, hindering the widespread adoption of UAV technology. To address these challenges, this paper establishes a distortion region identification algorithm based on image grayscale variation range parameters. Then, through fusing UAV oblique photogrammetry, close-range smartphone photogrammetry, and Real-Time Kinematic (RTK) positioning technology, it ultimately constructs a 3D real-scene reconstruction technical framework. To validate the method’s effectiveness and reliability, a field experiment was conducted in the Zaoerxiang Historic Cultural District of Zhanggong District, Ganzhou City, Jiangxi Province, China. The experimental results demonstrate that the proposed algorithm can effectively identify distortions in the modeling results from UAV images. After fusing smartphone images from distorted regions and RTK measurements from ground control points (GCPs), the discrepancies in X, Y, and Z coordinates between the results and verification points mostly fall within 10 to 25 mm, while the differences from the measured lengths using a steel tape measure and a leveling rod were within the range of 10 to 20 mm. Furthermore, compared to approaches that rely solely on UAV images or on the fusion of UAV and all ground-based images for modeling, the method proposed in this paper restores building texture information in occluded areas and improves the accuracy of 3D real-scene modeling while simultaneously reducing data-processing and storage requirements and enhancing operational efficiency. It provides a referenceable technical framework for digital preservation, restoration planning, and smart cultural tourism of historic districts. Full article

This study addresses the power dispatching of a DC-coupled three-phase grid-connected photovoltaic (PV) and energy storage-integrated system by proposing a peak shaving and valley-filling control architecture based on time-of-use (TOU) pricing. This research involves achieving maximum power-point tracking (MPPT) for PVMAs using a boost converter combined with the perturb and observe (P&O) method. A lithium-iron phosphate battery pack is integrated into the DC link via a bidirectional buck-boost converter, where charging and discharging control is executed according to peak and off-peak periods to regulate and stabilize the DC link voltage. Furthermore, bidirectional power flow control for peak and off-peak electricity consumption is realized using hysteresis current control and sinusoidal pulse-width modulation (SPWM) technologies within a smart inverter. By integrating the aforementioned power control architecture, the grid system can store energy from the utility during off-peak hours and release the stored energy during peak hours to reduce the load demand on the utility side. Initially, a simulation environment was established using Matlab/Simulink (2024b version) software, followed by control verification of the proposed system on a physical platform. The simulation and experimental results confirm that the integrated control architecture can precisely control the system’s DC link voltage at 800 V and stabilize the grid-connected AC voltage at an effective value (RMS) of 380 V. Moreover, under conditions of peak/off-peak switching and load variations, the system effectively demonstrates its stability and efficacy in performing valley filling and peak shaving. The proposed strategy achieves a power factor above 0.99 and a total harmonic distortion (THD) below 5%, regulates the DC-link voltage at 800 V with a steady-state error within 1.75%, and prevents up to 66.4 kWh of over-contract energy consumption per day under a 35 kW contract capacity, thereby contributing to sustainable energy management and economic savings. Full article

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; therefore, cost escalation is driven by sequential interactions among procurement, schedule execution, equipment deployment, supervision, weather, logistics, and price volatility. The proposed framework models procurement management, construction scheduling, equipment allocation, and supervision-control units as decentralized agents embedded in a calibrated construction simulation environment. The environment is parameterized from 42 smart grid construction projects in Henan Province, China and generates disturbance scenarios involving weather efficiency loss, transportation delay, market-price volatility, labor shortage, and supply-chain interruption. A hybrid DQN–PPO mechanism represents mixed decision structures: value-based DQN modules handle discrete managerial choices such as task acceleration, supplier switching, and procurement timing, whereas PPO modules adjust continuous resource-allocation and recovery-intensity decisions. A hierarchical reward function combines local departmental objectives with project-level penalties for cost overrun, schedule delay, idle resources, recovery expenditure, safety risk, and environmental impact. The experimental protocol uses 30 paired random seeds, nonparametric bootstrap confidence intervals, Holm-adjusted Wilcoxon signed-rank tests, and comparison with deterministic optimization, rolling-horizon MPC, stochastic/robust optimization, single-agent DRL, MAPPO, MADDPG/MATD3, QMIX, and HAPPO baselines. The proposed framework achieves a mean cost-overrun rate of 6.83% and a mean schedule deviation of 16.82 days, reducing cost overrun by 18.7% and schedule deviation by 21.4% relative to rule-based construction management under the reported disturbance settings. The calibrated simulation evidence establishes a statistically evaluated decision-support framework for coordinated construction cost control and provides an artifact-level reproducibility pathway through configuration files, random-seed lists, anonymized synthetic benchmarks, and aggregated logs. Full article

The Social Internet of Things (SIoT) has emerged as a promising paradigm for addressing interoperability, adaptability, and intelligent collaboration challenges in smart city environments. However, existing solutions often provide only partial support for semantic interoperability, dynamic social relationships, and context-aware service coordination across heterogeneous IoT ecosystems. This paper presents SEMIWARE, a semantic social network-oriented middleware designed to support collaborative, interoperable, and context-aware SIoT applications. SEMIWARE adopts a layered architecture that combines a FIWARE-based middleware backbone with modular services for context management, semantic annotation, semantic reasoning, service discovery, social relationship management, profiling, security, and ontology alignment. Its semantic backbone is provided by an OWL2 ontology that models IoT entities, users, services, contextual information, and trust-aware social relationships. The middleware is validated through two representative applications in distinct domains: smart mobility, where semantic reasoning supports adaptive eco-friendly route computation, and healthcare, where semantically integrated wearable and environmental data support health-event detection for people with dementia. Experimental evaluation further examines the performance of semantic annotation, semantic reasoning, and context management services under increasing workloads. The results provide prototype-level evidence that SEMIWARE supports semantic interoperability, cross-domain adaptability, and graph-based processing under controlled workloads, indicating its potential suitability for complex, data-intensive SIoT applications. Full article

The digital sharing of healthcare data necessitates a careful balance between the need for verifiability and the protection of patient privacy. In many real-world scenarios, particularly in employer and third-party verification processes, excessive clinical information is disclosed beyond what is strictly required. This practice introduces significant privacy risks and conflicts with data minimization principles. To address this problem, this study proposes a blockchain-based, privacy-preserving system architecture that enables health report verification without revealing diagnosis information. The proposed system is built upon a dual-layer architecture that structurally separates clinical data from verification processes. In the clinical data layer, health reports are encrypted on the client side and stored in off-chain environments, while only reference data and access control information are recorded on the blockchain. The system further integrates revocation mechanisms, role-based access control, and auditability through a modular smart contract design. In conclusion, this study introduces a modular, privacy-oriented, and practically applicable solution for secure healthcare data verification. By eliminating the need for clinical data disclosure during verification, the proposed architecture offers a novel design perspective and contributes both conceptually and technically to the development of blockchain-based healthcare information systems. Full article

Flexible robotic fish are emerging as a promising class of deep-sea exploration platforms because they combine compliant bodies, low-disturbance fish-like propulsion, and the potential for distributed sensing and autonomy. Unlike conventional biomimetic robotic fish developed mainly for shallow or moderate-depth environments, deep-sea flexible robotic fish must simultaneously address high hydrostatic pressure, low temperature, darkness, limited communication, constrained power supply, and complex near-bottom terrain. This review synthesizes research at the intersection of deep-sea soft robotics, bio-inspired robotic fish, smart-material actuation, pressure-adaptive packaging, multimodal sensing, and autonomous control. The literature is organized around a system-level design chain: biological mechanisms that inspire pressure adaptation and perception, body architectures that distribute pressure and protect electronics, soft actuators that generate fish-like propulsion, and control strategies that enable near-bottom and long-duration tasks. The review highlights that the central challenge is not any single actuator or material, but the co-design of pressure-adaptive bodies, hybrid soft actuation, reliable interfaces, multimodal perception, energy management, and autonomy. To strengthen engineering translation, this revised review further adds design-principle abstraction, actuator-selection guidance, prototype-level comparison, failure-mode analysis, and a computational design workflow. Future research should prioritize long-term reliability tests, standardized deep-sea evaluation protocols, physics-informed modeling, and integrated prototype demonstrations under realistic mission conditions. Full article

Secure communication is one of the foundational requirements for modern smart grid operations, where Distributed Network Protocol 3 (DNP3) remains a primary protocol for monitoring and control. Key stakeholders in the smart grid—such as transmission operators, distribution operators, utilities, and balancing authorities—are increasingly required to strengthen their cybersecurity protections. They must maintain the reliability of communications across complex, bandwidth-constrained wide-area networks (WANs). This paper presents a practical evaluation of four DNP3 deployment models: DNP3 in its native unsecured form, DNP3 with Secure Authentication (SA), DNP3 over Transport Layer Security (TLS 1.3), and DNP3 protected using IPsec encryption at the network layer. The paper evaluates how each security approach performs in a production-like smart grid environment. This paper primarily focuses on the widely deployed DNP3-SA model, which provides authentication and integrity but not confidentiality. Although newer Secure Authentication versions exist, including SAv6, they are still less common in operational utility environments. Experiments are conducted using a hardware-informed simulation testbed that emulates realistic utility WAN conditions, including multiple routing hops, MPLS as a transport for packets, and varied link capacities. Performance is assessed in terms of bandwidth efficiency, round-trip latency, and additional computational overhead on the field devices. Based on the findings, the paper presents a decision framework to help utilities select DNP3 security implementations that align with regulatory expectations and operational reliability. Full article