Search Results (1,602)

This narrative review examines the evolving landscape of AI-based security in Cyber–Physical Systems 2.0 (CPS 2.0) within the context of AI-driven autonomous cybersecurity solutions for the Internet of Things (IoT). This article presents a narrative review, supported by a structured literature search inspired by the PRISMA 2020 project and descriptive publication statistics. It combines transparent study selection with qualitative conceptual synthesis, rather than a formal systematic review or bibliometric analysis. CPS 2.0 represents a new generation of interconnected systems that tightly integrate physical processes with intelligent computational components, enabling increased autonomy and operational efficiency. However, this growing complexity introduces advanced security threats and privacy challenges that traditional centralized security frameworks are ill-equipped to address due to limitations in scalability, latency, and data sensitivity. The paper explores how artificial intelligence (AI), machine learning (ML), and generative AI (GenAI) enhance real-time threat detection, prediction, and response in distributed environments. It highlights the role of edge computing in decentralizing intelligence, thereby reducing latency and limiting exposure of sensitive data. Additionally, federated learning (FL) is discussed as a privacy-preserving paradigm that enables collaborative model training across distributed nodes without sharing raw data. The integration of GenAI, FL, and edge computing is presented as a synergistic approach that enables adaptive, context-aware, and proactive defense mechanisms against dynamic and evolving cyber threats. The review further analyzes architectural frameworks, key advantages, and inherent vulnerabilities of CPS 2.0, along with mitigation strategies and real-world applications, particularly in industrial control systems. By synthesizing current advancements and challenges, this work provides a comprehensive roadmap for designing resilient, scalable, and privacy-aware CPS infrastructures. The findings contribute to the development of secure and intelligent systems aligned with the future demands of Industry 4.0, 5.0, and beyond. Full article

This study presents a systematic literature-based discovery of the energy management process within household microgrids, combining the methodologies of Systematic Literature Review (SLR) and Literature-Based Discovery (LBD). The objective is to identify and structure key activities that ensure the efficient, scalable, and resilient operation of household microgrids. Drawing on an extensive analysis of the literature, the study proposes a conceptual, process-oriented framework that integrates technological and organizational perspectives into an eight-step roadmap for household energy management. These steps include data acquisition, local weather forecasting, energy production and consumption prediction, demand and supply management, energy generation and storage, power distribution, control of technological and organizational infrastructure, and compliance with safety and regulatory standards. The model supports the integration of predictive, self-learning control systems and highlights the importance of user competence development alongside automation. By mapping out a structured and replicable approach to household microgrid energy management, the study provides a foundation for improved energy independence, operational reliability, and effective integration into decentralized energy markets. The roadmap offers practical insights for both researchers and practitioners aiming to support the sustainable development and governance of household microgrids. Full article

The integration of Unmanned Aerial Vehicle (UAV) swarms with Cell-Free massive Multiple Input Multiple Output (CF-mMIMO) networks offers promising prospects for large-scale crop monitoring in precision agriculture. CF-mMIMO provides macro-diversity and uniform channel quality across large agricultural fields. However, practical deployment demands jointly optimizing 3D trajectories and transmit power to maximize energy efficiency and field coverage simultaneously. This is challenging due to the limited battery capacity, mandatory return-to-depot constraints, and collision avoidance requirements. In this paper, we introduce a joint sensing–communication utility function that captures the trade-off between energy efficiency and field coverage completeness. To provide a scalable and distributed solution for rotary-wing UAV swarms, we develop a multi-agent deep reinforcement learning (MADRL) methodology based on the Multi-Agent Proximal Policy Optimization (MAPPO) approach. We adopt the Centralized Training with Decentralized Execution (CTDE) strategy, in which a CF-mMIMO central processing unit (CPU) serves as a global critic during training. At execution time, each UAV independently runs a lightweight local policy that adapts its trajectory and transmit power in real time based on battery state and air-to-ground channel variations. Simulation results reveal that the proposed MAPPO-CTDE approach outperforms existing benchmarks. Unlike prior methods that require instantaneous global CSI or neglect the sensing–communication coupling, the proposed approach simultaneously achieves high field coverage completeness, robust communication energy efficiency, and a high depot-return rate under hard battery constraints without any inter-UAV communication overhead at execution time. Full article

Large Language Models (LLMs) are emerging as key enablers of Future Internet ecosystems, supporting intelligent, adaptive, and context-aware services across distributed cyber-physical environments. Beyond traditional natural language processing, LLMs are increasingly integrated into Smart Internet of Things (SIoT) systems to enable semantic interoperability, edge intelligence, multimodal interaction, and autonomous service orchestration. This paper presents a taxonomy-based review of LLM architectures, training paradigms, deployment strategies, and emerging applications within Future Internet infrastructures. The review classifies existing studies by deployment environment, architecture, training strategy, accessibility, and application scope, and analyses the role of LLMs in intelligent IoT environments, with emphasis on edge-based reasoning, agentic AI, human-centric automation, and context-aware decision-making. Key challenges are examined, including scalability, inference latency, privacy, trustworthiness, security, hallucination, and energy efficiency in resource-constrained environments. A comparative analysis of representative LLMs is presented, based on deployment feasibility, multimodal capability, accessibility, and suitability for distributed intelligent services. The originality of the review lies in conceptualizing LLMs as cognitive middleware that provides semantic, reasoning, and coordination capabilities across Smart IoT infrastructures. Finally, future research directions are highlighted, including decentralized AI architectures, digital twins, the Model Context Protocol, retrieval-augmented generation, multimodal sensing, and autonomous agent-based ecosystems. Full article

This study evaluates how architectural heterogeneity and reward design influence coordination in decentralized IPPO-based Multi-Agent Reinforcement Learning (MARL) systems operating in Search-and-Rescue (SAR) environments with strict sequential task dependencies. Controlled experiments compare homogeneous and heterogeneous teams across multiple reward structures, observation ranges, task complexities, and asymmetric role distributions in a partially observable, communication-denied, decentralized environment. The results show that homogeneous teams consistently achieve faster convergence, higher coordination efficiency, and more stable cooperative behavior than balanced heterogeneous configurations. Under hybrid global–local reward schemes, homogeneous teams achieve a higher success rate than local-only reward schemes. A dedicated test confirms this advantage stems from team composition itself, not parameter sharing: it persists even under independent, non-shared policy networks. Additional experiments suggest that asymmetric role allocation may influence coordination dynamics in heterogeneous teams. The study provides empirically grounded design recommendations for cooperative multi-agent systems, derived from controlled simulation experiments under communication-limited sequential coordination constraints. Full article

The high rate of growth of misinformation on decentralized platforms causes a risk to public confidence and the integrity of decisions and requires a system of verification that is not only accurate but can be causally informed and interpreted via proxy causal metrics, reliable, and privacy-safe as well. The state-of-the-art federated learning (FL)-based fact-verification models mainly use correlation-driven patterns and do not provide ways to deal with causal reasoning, measuring formal reliability, or being resilient to Byzantine adversaries. This paper proposes a unified framework called CORE-FACT (causal optimization and reliability-enhanced fact-tracking) that can be used to conduct interpretable and robust misinformation detection in a distributed environment by combining causally informed optimization with reliability-weighted federated optimization. The proposed three-tier architecture includes: (1) a causal graph construction module, where variational attention is utilized to learn directed relationships of claims and evidence; (2) a reliability-weighted federated optimization module, where Byzantine-resilient aggregation (adaptive trust scoring) is achieved; and (3) an adaptive fact-tracking module, which is used to achieve fusion of multi-source evidence by combining hierarchical consistency verification with knowledge-graph embeddings. Empirical testing on the LIAR and FEVER datasets shows that CORE-FACT achieves 94.7% and 96.3% accuracy, respectively, outperforming state-of-the-art baselines by 3.5 to 4.8 percentage points in accuracy, with 17% lower latency than GEAR and 31% lower latency than DAGNN, and 98.2% robustness against 30% Byzantine attacks. Under differential-privacy guarantees verified at ε = 10.96, δ = 10⁻⁵ (Renyi DP composition, empirical membership inference validation committed for revision), CORE-FACT achieves a 31% reduction in false positives through explicit causally informed reasoning. These findings make CORE-FACT a scalable and interpretable framework that consolidates causal optimization, trustworthiness evaluation, and secure aggregation for next-generation federated misinformation detection. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges for power system stability while creating new opportunities for decentralized energy management. Peer-to-peer (P2P) energy networks have emerged as a promising approach for transforming EVs from passive loads into coordinated grid assets. This paper presents a comprehensive review of EV-P2P integration through a three-layer architectural framework that systematically connects physical infrastructure, market mechanisms, and intelligent control strategies. The Physical Layer reviews how V2X technologies and bidirectional charging enable EVs to operate as flexible storage resources and ancillary service providers. The Transactional Layer reviews on blockchain-based platforms, auction mechanisms, and game-theoretic models for secure energy trading. The Intelligence Layer reviews advanced control strategies, including decentralized optimization methods such as the Alternating Direction Method of Multipliers (ADMM) and Deep Reinforcement Learning. Collectively, the reviewed studies demonstrate that these approaches enable EVs to operate as flexible loads, distributed storage resources, and ancillary service providers, while improving energy trading efficiency, reducing operating costs, and alleviating network congestion under simulated operating conditions. Despite these promising results, a substantial gap remains between simulation-based studies and practical implementation. Future research should prioritize integrated pilot projects to evaluate scalability, interoperability, cybersecurity, and regulatory compliance under realistic operating conditions. Full article

Biomimetic optimization transfers biological information-processing mechanisms into computational systems. Ant colony optimization (ACO) is a canonical example: artificial ants functionally abstract pheromone-mediated stigmergy, decentralized exploration, trail decay through algorithmic evaporation, and adaptive path reinforcement. Building on this functional biological analogue, we present a controlled cross-paradigm evaluation of routing-heuristic generation. A standardized interface embeds human-designed rules, the genetic programming hyper-heuristic GHPP, a resource-constrained DeepACO-MLP proxy, and an offline ReEvo-style proxy into the same ACO solver. The methods are evaluated on held-out TSP and CVRP instances in terms of solution quality, reported generation or training cost, interpretability, and cross-scale behavior under a matched distribution. GHPP yields the shortest routes at all tested scales; the ReEvo-offline proxy and strong human-designed rules generally form a second tier, whereas the resource-constrained neural proxy degrades markedly as problem size increases. These results do not establish an intrinsic ranking of full-capability paradigms. Instead, they show that method selection depends on the operating constraint and on evidence provenance: longer locally measured offline search favors GHPP, while auditable explicit rules characterize the human and ReEvo-offline proxies. By holding the ant-inspired execution mechanism fixed and varying the source of heuristic information, the benchmark clarifies how evolutionary, neural, and LLM-style design strategies interact with a common biomimetic substrate. Full article

Blockchain technology has introduced a rapidly expanding range of consensus mechanisms, each designed to satisfy different operational requirements related to security, scalability, decentralization, transaction throughput, and energy efficiency. Selecting an appropriate consensus mechanism has consequently become a complex multi-criteria decision problem, particularly for developers and organizations without extensive expertise in distributed systems and blockchain architectures. Existing tools primarily address protocol benchmarking and static documentation, leaving the decision-support dimension largely unaddressed. This paper presents a web-based expert system designed to support the selection of blockchain consensus mechanisms according to specific user-defined operational requirements. The proposed solution was implemented using the MERN technology stack, consisting of MongoDB, Express.js, React, and Node.js, enabling a modular and scalable architecture suitable for future expansion and maintenance. The recommendation process is based on a two-phase filtering and scoring algorithm. In the first phase, mechanisms incompatible with mandatory user-defined constraints, including network type and key resource type, are systematically eliminated. In the second phase, the remaining mechanisms are ranked using attribute matching across criteria encompassing energy efficiency, scalability, security, decentralization, and transaction speed. The system returns the three most suitable consensus mechanisms for the given operational scenario together with their key characteristics. In addition to recommendation functionality, the application supports user authentication, recommendation history management, and administrative maintenance of the consensus mechanism database, which currently contains 38 distinct blockchain consensus protocols. Experimental evaluation through twelve representative usage scenarios demonstrated that the system consistently produces contextually relevant recommendations aligned with user-specified requirements. A comparative analysis with existing tools confirms that the proposed system occupies a distinct decision-support role currently absent from the available tooling landscape. A comparative analysis against four representative existing tools indicates that the proposed system combines a set of decision-support capabilities not jointly offered by any one of them. The presented approach contributes a transparent and extensible decision-support framework intended to simplify architectural planning and management of blockchain-based distributed systems. Full article

The increasing integration of renewable energy resources, distributed energy systems, and intelligent sensing technologies has accelerated the transformation of conventional power grids into interconnected cyber–physical smart energy ecosystems. In this context, the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a key enabler for intelligent monitoring, adaptive energy management, resilient grid operation, and sustainable energy coordination. Although numerous studies have investigated AI, IoT, blockchain, and cybersecurity technologies individually, many existing reviews focus on isolated domains without adequately addressing the interactions between intelligent operational control, communication infrastructures, decentralized coordination, sustainability, and cyber resilience. Accordingly, this paper presents a comprehensive system-level review of AI–IoT-enabled smart energy ecosystems, focusing on smart grids, microgrids, intelligent energy management, blockchain-enabled decentralized coordination, carbon emissions monitoring, and cyber-resilient energy infrastructures. Unlike existing surveys that primarily emphasize individual technologies or algorithmic performance, this work highlights the cross-layer integration and architectural interdependencies between AI-driven operational intelligence, IoT-enabled monitoring, secure communication frameworks, and sustainability-oriented energy management. The paper also discusses key challenges related to interoperability, scalability, cybersecurity, communication latency, and distributed coordination, in addition to future research directions toward resilient, autonomous, and sustainable intelligent energy ecosystems. Full article

The rapid development and expansion of the Internet of Things (IoT) ecosystem require managing increasing computational demands with the aid of advanced hierarchical architectures. The integration of a complete four-tier hierarchy—including Mobile, Edge, Fog, and Cloud layers—and the priority requirements are being overlooked in the literature despite the effort of existing studies. The goal of this study is to fill these gaps by proposing a novel, context-aware task-offloading framework designed for multi-dimensional ecosystems involving multi-server and multi-application environments. A targeted biomimetic approach is utilized at the core of this research. The decentralized foraging behavior of biological swarms is translated into a concrete engineering solution. This solution is designed specifically for computational offloading and resource management. To achieve this, a “Learning-guided Meta-heuristic” hybrid model is developed. Within this framework, bio-inspired Ant Colony Optimization (ACO) is directly integrated with an Upper Confidence Bound (UCB)-inspired exploration mechanism. Natural, pheromone-based imitation is solely relied upon by traditional biomimetic algorithms. In contrast, higher-order cognitive learning is fully incorporated by this hybrid synergy. Consequently, underlying system dynamics are adaptively learned. Local minima traps are also successfully avoided. This avoidance is achieved by dynamically selecting the optimal layer for each individual task. Both energy consumption and latency are optimized simultaneously. Meanwhile, strict operational feasibility is ensured through a dynamic penalty-based mechanism. Battery and deadline constraints are explicitly handled by this mechanism. Extensive simulations demonstrate the superiority of the proposed UCB-ACO model over state-of-the-art meta-heuristics, including Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), ACO, Artificial Bee Colony Optimization (ABO), and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The findings reveal that the proposed framework outperforms the methods compared by achieving 22.5% lower latency and 23% lower energy consumption. This study effectively maps the current literature and then introduces a pioneering solution for next-generation resource management in distributed computing. Full article

Vehicle-to-Everything (V2X) communication is an essential building block of intelligent transportation systems, supporting high-data-rate vehicle-to-infrastructure (V2I) services, and ultra-reliable low-latency vehicle-to-vehicle (V2V) communication. However, in dense urban environments, V2X services can be significantly degraded by the presence of severe blockage, fast channel variations, and high levels of interference. Intelligent Reflecting Surfaces (IRSs) can be employed to reconfigure wireless propagation environments to improve V2X communication. However, the joint optimization of transmit power, spectrum reuse, and IRS reflection coefficients is a mixed-integer non-linear problem, which is further complicated by the fast vehicular mobility and time-varying interference in V2X networks. To tackle this challenging problem, this work proposes a scalable and deployable decentralized hierarchical multi-agent deep reinforcement learning (DH-MDRL) framework. The key design principle is the separation of control timescales, whereby each V2V link functions as an autonomous agent that responds to local observations at a fast timescale and determines its transmit power and spectrum reuse decisions, while the IRS controller at the base station (BS), using global network observations, updates the IRS reflection coefficients at a slower timescale. This hierarchical architecture reduces coordination signaling associated with centralized resource allocation while enabling distributed resource allocation. The IRS-assisted V2X network is modeled as a Markov decision process, where the reward design is tailored to optimize the V2I sum data rate while guaranteeing the latency and reliability constraints associated with safety-critical V2V communication. Simulation results show that the proposed DH-MDRL framework outperforms conventional schemes without IRSs and achieves an excellent trade-off between V2V link constraints’ satisfaction probability and V2I link sum data rates compared to centralized resource allocation approaches. Full article

Knowledge graphs (KGs) organize relational facts in a form that supports machine reasoning across many domains. In decentralized Internet of Things (IoT) environments, such graphs can describe interactions among distributed devices and services. However, privacy constraints, intermittent communication, and incomplete observations can leave the resulting graphs sparse and incomplete. This sparsity weakens path availability and makes missing-fact inference difficult. Multi-hop reasoning can recover unobserved links through explicit reasoning paths, but conventional hierarchical reinforcement learning (HRL) approaches may explore inefficiently when only a few valid paths are available. This paper presents attentiON exploratIon of cluster relatioN (ONION), a framework for sparse KG reasoning. ONION introduces a cluster relation augmentation (CRA) module to add cluster relation cues to the action space, together with a relation-level attention (RLA) mechanism to emphasize relations that are relevant to the query. These components guide hierarchical exploration toward more informative paths. ONION is evaluated on four public sparse-KG benchmarks, and the results show competitive performance with the clearest gains on highly sparse datasets. Full article

Tourism development in coastal Mediterranean regions has posed a significant pressure on water local resources, particularly the extensive use of water-intensive recreational infrastructures. This study investigates the spatial distribution and water consumption of public and private swimming pools in the Kassandra peninsula, Halkidiki, Greece. Using Geographic Information Systems (GIS) and high-resolution satellite imagery from 2016 and 2024, the research quantified changes in pool density and estimated associated water losses from evaporation and filter backwashing. Our findings reveal a significant increase in swimming pools and related water demand, intensifying water scarcity during the tourist season. Growth was particularly strong among small private and tourism-related pools, which increased by 192.4% overall and by more than 400% in some districts. This trend reflects a shift from mass hotel-based tourism toward decentralized and flexible tourism models. Furthermore, cartographic outputs and thematic mapping highlight spatial concentrations of water consumption near environmentally sensitive coastal zones. The applied geospatial approach identifies interactions between tourism infrastructure and vulnerable ecosystems, offering an integrated geographic perspective on environmental pressures in coastal areas. Our results emphasize the urgent need for sustainable water management policies, including water reuse practices and strategies to improve the resilience of tourism-dependent regions under climate stress. Full article

Federated learning (FL) and edge computing are transformative technologies that enhance privacy, efficiency, and real-time intelligence for distributed machine learning across diverse edge device networks. FL enables devices to collaboratively train models locally, so sensitive data remains on each device, ensuring privacy and regulatory compliance (like GDPR and HIPAA). Edge computing complements FL by bringing data processing closer to sources such as IoT sensors and smartphones, which reduces latency, bandwidth use, and dependence on cloud servers. This architecture is vital for smart cities, healthcare, industry, and autonomous systems, supporting real-time decision-making. The review details challenges such as resource heterogeneity, communication constraints, security risks, and management complexity, while highlighting opportunities for scalable orchestration, decentralized architectures, and blockchain integration. Together, FL and edge computing create a robust paradigm for scalable, privacy-aware distributed intelligence across multiple domains. Full article