Search Results (595)

The equilibrium between electricity demand and consumption is vital to ensure the stability of the transmission and distribution systems grid (TS & DS) and to ensure the stable operation of the electrical system. The aim of this review study is to highlight the current legislative and technical situation and the possibilities for managing peak loads, decentralization, sharing, storage, and sale of electricity generated from renewable sources in Slovakia. The European Union′s (EU) goal of achieving carbon neutrality by 2050 and a minimum of 42.5% renewable energy consumption by 2030 brings with it obligations for individual member states. These are transposed into national strategies. The current share of renewable sources in Slovakia is approximately 24% and the EU target by 2030 is probably unrealistic. Water resources are practically exhausted; other possibilities for increasing the share of renewable energy sources (RES) are in photovoltaics, wind, and thermal sources. Due to long-term geographical and historical development, electricity production in Slovakia is based on large-scale solutions. The move towards decentralization requires legislative and technical support. The review article examines the possibilities of increasing the share of RES and energy sharing in Slovakia, and examines the legislative, economic, and social barriers to their wider application. At the same time as the share of renewable sources in electricity generation increases, the article examines and presents solutions capable of ensuring the stability of electricity networks across Europe. The study formulates diversified strategies at the distribution network level and the consumer and building levels, and identifies physical (various types of electricity storage, electromobility, electricity liquidators) and virtual (electricity sharing, energy communities, virtual batteries) solutions. In conclusion, it defines the necessary changes in the legislative, technical, social, and economic areas for the most optimal improvement of the situation in the area of increasing the share of RES, supporting the decentralization of the electric power industry, and sharing electricity in Slovakia, also based on experience and good examples from abroad. Full article

The widespread circulation of digital misinformation exposes a critical shortcoming in prevailing detection strategies, namely, the absence of robust mechanisms to confirm the origin and authenticity of online content. This study addresses this by introducing VeriTrust, a conceptual and provenance-centric framework designed to establish content-level trust by integrating Self-Sovereign Identity (SSI), blockchain-based anchoring, and AI-assisted decentralized verification. The proposed system is designed to operate through three key components: (1) issuing Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) through Hyperledger Aries and Indy; (2) anchoring cryptographic hashes of content metadata to an Ethereum-compatible blockchain using Merkle trees and smart contracts; and (3) enabling a community-led verification model enhanced by federated learning with future extensibility toward zero-knowledge proof techniques. Theoretical projections, derived from established performance benchmarks, suggest the framework offers low latency and high scalability for content anchoring and minimal on-chain transaction fees. It also prioritizes user privacy by ensuring no on-chain exposure of personal data. VeriTrust redefines misinformation mitigation by shifting from reactive content-based classification to proactive provenance-based verification, forming a verifiable link between digital content and its creator. VeriTrust, while currently at the conceptual and theoretical validation stage, holds promise for enhancing transparency, accountability, and resilience against misinformation attacks across journalism, academia, and online platforms. Full article

This paper presents a comparative mathematical analysis of electrode configurations used in active fog water harvesting systems based on electrostatic ionization. The study begins with a brief overview of fog formation and typology. It also addresses the global relevance of fog as a decentralized water resource. It also outlines the main methods and collector designs currently employed for fog water capture, both passive and active. The core of the work involves solving the Laplace equation for various electrode geometries to compute electrostatic field distributions and analyze field line density patterns as a proxy for potential water collection efficiency. The evaluated configurations include centered rod–cylinder, symmetric parallel multi-rod, and asymmetric wire–plate layouts, with emphasis on identifying spatial regions of high field line convergence. These regions are interpreted as likely trajectories of charged droplets under Coulombic force influence. The modeling approach enables preliminary assessment of design efficiency without relying on time-consuming droplet-level simulations. The results serve as a theoretical foundation prior to the construction of electrode layouts in the portable HygroCatch experimental harvester and provide insight into how field structure correlates with fog water harvesting performance. Full article

Blockchain is described as a technology of trust. Its design relies on cryptography, decentralization, and immutability to ensure secure and transparent transactions. Yet users frequently report confusion, frustration, and skepticism when engaging with blockchain applications. This tension is the blockchain trust paradox: while trust is engineered into the technology, trust is not always experienced by its users. Our article examines the paradox through three theoretical perspectives. Socio-Technical Systems (STS) theory highlights how trust emerges from the interaction between technical features and social practices; Technology Acceptance models (TAM and UTAUT) emphasize how perceived usefulness and ease of use shape adoption. Ostrom’s commons governance theory explains how legitimacy and accountability affect trust in decentralized networks. Drawing on recent research in experience design, human–computer interaction, and decentralized governance, the article identifies the barriers that undermine user confidence. These include complex key management, unpredictable transaction costs, and unclear processes for decision-making and dispute resolution. The article offers an integrated framework that links engineered trust with experienced trust. Seven propositions are developed to guide future research and practice. The conclusion argues that blockchain technologies will gain traction if design and governance evolve alongside technical protocols to create systems that are both technically secure and trustworthy in experience. Full article

With the gradual shift towards the use of electric vehicles (EV), electricity demand is expected to increase especially in energy communities. Therefore, it is important to investigate how energy is generated as the provenance of electricity supply is directly linked to climate change. There are only a few studies that investigated the internet of energy and energy provenance, but this area of research is important to prevent the rebound effect of CO₂ emission due to the lack of a transparent approach that verifies the source of electricity consumed for charging EVs. The energy system is a complex network, which results in difficulty verifying the source of electricity as related to the generation of energy. Identifying the provenance of electricity is challenging since electricity is a non-physical element. Moreover, the volatility of a Renewable Energy Source (RES), such as solar and wind power farms, in relation to the complex electricity distribution system makes tracking and tracing challenging. Disruptive technologies, such as Distributed Ledger Technologies (DLT), have been previously adopted to trace the end-to-end stages of products. Likewise, artificial intelligence (AI) can be adopted for the optimization, control, dispatching, and management of energy systems. Therefore, this study develops a decentralized intelligent framework enabled by AI-based DLT and smart contracts deployed to accelerate the development of the internet of energy towards energy provenance in energy communities. The framework supports the tracing and tracking of RES type and source consumed for charging EVs. Findings from this study will help to accelerate the production, trading, distribution, sharing, and consumption of RES in energy communities. Full article

Human-powered electricity generation (HPEG) systems offer promising sustainable energy solutions, yet existing deterministic models fail to capture the inherent variability in human biomechanical performance. This study develops a comprehensive stochastic framework integrating advanced Monte Carlo uncertainty quantification with multi-component fatigue modeling and Pareto optimization. The framework incorporates physiological parameter vectors, kinematic variables, and environmental factors through multivariate distributions, addressing the complex stochastic nature of human power generation. A novel multi-component efficiency function integrates biomechanical, coordination, fatigue, thermal, and adaptation effects, while advanced fatigue dynamics distinguish between peripheral muscular, central neural, and substrate depletion mechanisms. Experimental validation (623 trials, 7 participants) demonstrates RMSE of 3.52 W and CCC of 0.996. Monte Carlo analysis reveals mean power output of 97.6 ± 37.4 W (95% CI: 48.4–174.9 W) with substantial inter-participant variability (CV = 37.6%). Pareto optimization identifies 19 non-dominated solutions across force-cadence space, with maximum power configuration achieving 175.5 W at 332.7 N and 110.4 rpm. This paradigm shift provides essential foundations for next-generation HPEG implementations across emergency response, off-grid communities, and sustainable infrastructure applications. The framework thus delivers dual contributions: advancing stochastic uncertainty quantification methodologies for complex biomechanical systems while enabling resilient decentralized energy solutions critical for sustainable development and climate adaptation strategies. Full article

Biological swarms communicate through decentralized, adaptive behaviors shaped by local interactions, selective attention, and symbolic signaling. These principles of animal communication enable robust coordination without centralized control or persistent connectivity. This work presents a proof of concept that identifies, evaluates, and translates biological communication strategies into a generative visual language for unmanned aerial vehicle (UAV) swarm agents operating in radio-frequency (RF)-denied environments. Drawing from natural exemplars such as bee waggle dancing, white-tailed deer flagging, and peacock feather displays, we construct a configuration space that encodes visual messages through trajectories and LED patterns. A large language model (LLM), preconditioned using retrieval-augmented generation (RAG), serves as a generative translation layer that interprets perception data and produces symbolic UAV responses. Five test cases evaluate the system’s ability to preserve and adapt signal meaning through within-modality fidelity (maintaining symbolic structure in the same modality) and cross-modal translation (transferring meaning across motion and light). Covariance and eigenvalue-decomposition analysis demonstrate that this bio-agentic approach supports clear, expressive, and decentralized communication, with motion-based signaling achieving near-perfect clarity and expressiveness (0.992, 1.000), while LED-only and multi-signal cases showed partial success, maintaining high expressiveness (~1.000) but with much lower clarity (≤0.298). Full article

The increasing frequency of climate- and cyber-induced blackouts in modern distribution networks calls for restoration strategies that are both resilient and control-aware. Traditional black-start schemes, based on predefined energization sequences from synchronous machines, are inadequate for inverter-dominated grids characterized by high penetration of distributed energy resources and limited system inertia. This paper proposes a novel multi-layered black-start planning framework that explicitly incorporates the dynamic capabilities and operational constraints of grid-forming energy storage systems (GFESs). The approach formulates a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), jointly minimizing total restoration time, voltage–frequency deviations, and maximizing early-stage load recovery. A graph-theoretic partitioning module identifies restoration subgrids based on topological cohesion, critical load density, and GFES proximity, enabling localized energization and autonomous island formation. Restoration path planning is embedded as a mixed-integer constraint layer, enforcing synchronization stability, surge current thresholds, voltage drop limits, and dispatch-dependent GFES constraints such as SoC evolution and droop-based frequency support. The model is evaluated on a modified IEEE 123-bus system with five distributed GFES units under multiple blackout scenarios. Simulation results show that the proposed method achieves up to 31% faster restoration and 46% higher voltage compliance compared to MILP and heuristic baselines, while maintaining strict adherence to dynamic safety constraints. The framework yields a diverse Pareto frontier of feasible restoration strategies and provides actionable insights into the coordination of distributed grid-forming resources for decentralized black-start planning. These results demonstrate that control-aware, partition-driven optimization is essential for scalable, safe, and fast restoration in the next generation of resilient power systems. Full article

Artificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified review of 256 peer-reviewed publications to bridge that gap. We examine how emerging AI paradigms, such as explainable AI (XAI), AI-augmented software development, and federated learning, are shaping technological progress across both domains. In computer science, AI is increasingly embedded throughout the software development lifecycle to boost productivity, improve testing reliability, and automate decision making. In cybersecurity, AI drives advances in real-time threat detection and adaptive defense. Our synthesis highlights powerful cross-cutting findings, including shared challenges such as algorithmic bias, interpretability gaps, and high computational costs, as well as empirical evidence that AI-enabled defenses can reduce successful breaches by up to 30%. Explainability is identified as a cornerstone for trust and bias mitigation, while privacy-preserving techniques, including federated learning and local differential privacy, emerge as essential safeguards in decentralized environments such as the Internet of Things (IoT) and healthcare. Despite transformative progress, we emphasize persistent limitations in fairness, adversarial robustness, and the sustainability of large-scale model training. By integrating perspectives from two traditionally siloed disciplines, this review delivers a unified framework that not only maps current advances and limitations but also provides a foundation for building more resilient, ethical, and trustworthy AI systems. Full article

Blockchain technology has introduced new paradigms for identity management systems (IDMSs), enabling users to regain control over their identity data and reduce reliance on centralized authorities. In recent years, numerous blockchain-based IDMS solutions have emerged across both practical application domains and academic research. However, prior reviews often focus on single application areas, provide limited cross-domain comparison, and insufficiently address security challenges such as interoperability, revocation, and quantum resilience. This paper bridges these gaps by presenting the first comprehensive survey that examines IDMSs from three complementary perspectives: (i) historical evolution from centralized and federated models to blockchain-based decentralized architectures; (ii) a cross-domain taxonomy of blockchain-based IDMSs, encompassing both general-purpose designs and domain-specific implementations; and (iii) a security analysis of threats across the full identity lifecycle. Drawing on a systematic review of 47 studies published between 2019 and 2025 and conducted in accordance with the PRISMA methodology, the paper synthesizes academic research and real-world deployments to identify unresolved technical, economic, and social challenges, and to outline directions for future research. The findings aim to serve as a timely reference for both researchers and practitioners working toward secure, interoperable, and sustainable blockchain-based IDMSs. Full article

Rapid advancements in peer-to-peer (P2P) streaming technologies have significantly impacted digital communication, enabling scalable, decentralized, and real-time content distribution. Despite these advancements, challenges persist, including dynamic topology management, high latency, security vulnerabilities, and unfair resource sharing (e.g., free rider). While software-defined networking (SDN) and blockchain individually address aspects of these limitations, their combined potential for comprehensive optimization remains underexplored. This study proposes a distributed SDN (DSDN) architecture enhanced with blockchain support to provide secure, scalable, and reliable P2P video streaming. We identified research gaps through critical analysis of the literature. We systematically compared traditional P2P, SDN-enhanced, and hybrid architectures across six performance metrics: latency, throughput, packet loss, authentication accuracy, packet delivery ratio, and control overhead. Simulations with 200 peers demonstrate that the proposed hybrid SDN–blockchain framework achieves a latency of 140 ms, a throughput of 340 Mbps, an authentication accuracy of 98%, a packet delivery ratio of 97.8%, a packet loss ratio of 2.2%, and a control overhead of 9.3%, outperforming state-of-the-art solutions such as NodeMaps, the reinforcement learning-based routing framework (RL-RF), and content delivery networks-P2P networks (CDN-P2P). This work establishes a scalable and attack-resilient foundation for next-generation P2P streaming. Full article

The convergence of decentralized artificial intelligence (DAI), blockchain technology, and smart contracts is reshaping the design and governance of intelligent systems. As these technologies rapidly evolve, addressing privacy within their architecture, usage models, and associated risks has become increasingly critical. This systematic literature review examines architectural patterns, governance frameworks, real-world applications, and persistent challenges in DAI systems. It identifies prevailing designs such as federated learning integrated with consensus protocols, smart contract-based incentive mechanisms, and decentralized verification methods. Drawing from a diverse body of recent literature, the review highlights implementations across sectors, including healthcare, finance, IoT, autonomous systems, and intelligent infrastructure, each demonstrating significant contributions to privacy, security, and collaborative innovation. Despite these advancements, DAI systems face ongoing obstacles such as scalability limitations, privacy trade-offs, and difficulties with regulatory compliance. The review emphasizes the need for integrative governance approaches that balance transparency, accountability, incentive alignment, and ethical oversight. These elements are proposed as co-evolving pillars essential to establishing trustworthiness in decentralized AI ecosystems. This work offers a comprehensive review for understanding the current landscape and guiding the development of responsible and effective DAI systems in the Web3 era. Full article

Hydrogen is key to achieving a net-zero carbon future, yet current production remains predominantly fossil-based. Biohydrogen derived from agricultural residues represents a sustainable alternative aligned with circular economy principles. While several studies have assessed the bioenergy potential from agricultural residues in various African countries, their potential in Togo remains largely unexplored. This study employed an exploratory mixed-methods approach to quantify residue availability, evaluate production pathways, and estimate potential biohydrogen yields. Secondary data on crop production from the Food and Agriculture Organization (FAO) and theoretical conversion factors were used to assess the availability of agricultural residues from the eight major crops in Togo, resulting in a residue potential of 7.95 million tons per year. Considering ecological and competing aspects of residue utilization, a sustainable share of 3.1 to 6.6 million tons was estimated to be available for biohydrogen production, depending on the residue recoverability assumptions. A multi-criteria decision analysis (MCDA) was used to evaluate different biohydrogen production processes, identifying dark fermentation as the most suitable due to its low energy requirements and decentralized applicability. The theoretical biohydrogen potential was estimated at 20,991–42,293 tons per year (2.5–5.1 PJ per year) based on biochemical residue composition data and stoichiometric calculations. This study established a baseline assessment of biohydrogen potential from agricultural residues in Togo, offering a methodological framework for assessing biohydrogen potential in other regions. The results also underscore the need for site-specific data to reduce uncertainty and support evidence-based energy planning. Full article

The global transition from fossil fuels to renewable energy has become a critical priority in addressing environmental and sustainability challenges. However, the intermittent nature of renewable energy sources, such as solar and wind, poses significant challenges in ensuring a stable and reliable energy supply. This article explores the role of Internet of Things (IoT) technology in optimizing renewable energy storage systems to address these challenges. Using a systematic literature review (SLR) approach, the study identifies recent innovations in IoT technologies, including the integration of artificial intelligence (AI), big data analytics, and smart energy management systems, which enhance efficiency, sustainability, and reduce operational costs of energy storage systems. IoT enables real-time data monitoring, energy demand prediction, and decentralized energy management, reducing energy losses, extending the lifespan of storage devices, and supporting grid stability. The findings highlight that IoT integration offers innovative solutions to mitigate the intermittency of renewable energy, supporting the global energy transition toward more reliable, cost-effective, and environmentally friendly energy systems. This study provides valuable insights into the potential of IoT in advancing renewable energy storage technologies and contributes to the development of sustainable energy solutions for the future. Full article

The characteristics of Food Supply Chains (FSC) make them hard to manage properly, and many efforts have been conducted to alleviate the difficulties related to their management, especially when it comes to integrating the latest Information and Communications Technologies. The Internet of Things (IoT) has shown to be very beneficial in providing a holistic and real-time vision of FSCs. Blockchain, with its decentralization and immutability, is another promising technology, that is showing a great potential in managing FSCs. A lot of research has been carried out to prove the advantages of each of these technologies on its own. However, the research investigating their adoption together is still not enough. Our paper presents a study of recent advances in the integration of IoT and Blockchain in Food Supply Chain Management (FSCM) over the past five years. We identify key research trends, analyze the benefits and limitations of IoT–blockchain integration, and highlight major challenges hindering large-scale adoption. Finally, we propose future research directions to address these challenges and improve the adoption of IoT–blockchain solutions in FSCs. This study aims to serve as a reference for researchers and practitioners seeking to understand and advance the integration of these emerging technologies in FSCM. Full article