Search Results (1,859)

The construction industry faces significant challenges in materials management, including inefficient supply chains and limited adoption of Circular Economy (CE) goals, which blockchain may address through automated tracking and verification systems. This systematic review examines blockchain technology applications in construction materials management to support CE strategies. Following PRISMA guidelines, 138 articles were selected from 1891 publications across four databases covering 2018 to 2025. The findings present a lifecycle-based framework across five building stages integrating smart contracts, IoT sensors, digital material passports, and tokenized waste exchange systems. Blockchain enables automated supply chain transparency, eliminates manual verification, and facilitates continuous material tracking. This research contributes by transforming conventional materials management into autonomous, data-driven workflows through a blockchain-enabled framework that systematically maps automated solutions for tracking, compliance, and circular resource flows across five building lifecycle stages, enabling practitioners to implement automated CE strategies. Full article

Environmental comparisons of blockchain systems are often reduced to electricity per transaction, although operational services also depend on validators, cloud gateways, storage, monitoring, key management, recovery, and hardware replacement. This study develops a lifecycle assessment framework with non-compensatory security and utility gates and applies its operational-energy module to Ethereum’s transition from proof of work (PoW) to proof of stake (PoS). Three units are separated: 24 h of observed network operation (FU-O), 24 h of fully security- and utility-qualified service (FU-Q), and one million included layer-1 transactions (FU-B, an attributional diagnostic). FU-Q is not evaluated because several mandatory gates remain UNRESOLVED. Matched 28-day activity windows are combined with dated network-energy estimates, not continuous metering over those windows. Using the independent Cambridge baseline, daily operational electricity decreased from 58,617.39 to 5.376 MWh, a factor of 10,903.5 and a reduction of 99.99083%. The CCRI replication factor was 8804.9, while an adverse bounded pairing still yielded a factor of 3424.7. Across 100,000 Monte Carlo realizations generated by the supplied executable workflow, the median FU-O reduction was 99.98698%, with a central 95% interval of 99.97492–99.99441%. Jansen sensitivity analysis identified post-Merge annual energy as the dominant input to the FU-O factor. The additional post-Merge cloud and annualized embodied burden required to eliminate FU-O parity was 21,408 GWh/year. The result is a bounded operational-energy application and does not establish the complete lifecycle, cloud, cybersecurity, or functional-equivalence framework. Full article

The increasing penetration of distributed energy resources, variable renewable generation, battery energy storage systems, electric vehicles, and power-electronic interfaces is changing the way modern smart grids and microgrids are operated, protected, and controlled. This systematic literature review follows the PRISMA 2020 framework and examines 87 original research papers, complemented by a supplementary synthesis of 18 contextual studies that provide bibliometric, historical, and conceptual perspectives on the evolution of AI in smart grids. The primary studies are organized into six thematic clusters: energy management and forecasting; cybersecurity and intrusion detection; renewable energy integration and microgrid management; fault detection, diagnosis, and grid stability; explainable and trustworthy artificial intelligence; and emerging technologies, including digital twins, blockchain, the Internet of Things, edge computing, and federated learning. The review shows that deep learning, reinforcement learning, and ensemble machine learning are increasingly used for load forecasting, demand response, converter-interfaced renewable integration, intrusion detection, and operational optimization. However, the literature remains uneven. Fault detection, converter-aware protection, and real-time stability assessment receive considerably less attention than energy management and cybersecurity, despite their importance for inverter-based resources, grid-forming converters, electric-vehicle charging systems, and battery interfacing. Four critical gaps are identified: limited cross-grid generalizability, weak validation under realistic converter and protection constraints, insufficient adversarial robustness of AI-enabled defense systems, and limited explainability in real-time safety-critical applications. The paper provides a structured taxonomy, identifies deployment barriers, and proposes research directions for trustworthy AI in power-electronic-rich smart grids and microgrids. 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

This systematic review summarises advances in artificial intelligence (AI) and machine learning (ML) for foodborne pathogen detection, covering applications in various technologies (AI-assisted microscopy, spectroscopy, biosensors and sensor-based systems), food supply chains, analytical performance, operational metrics and regulatory developments, addressing gaps in previous reviews limited to individual technologies or lacking regulatory analysis. Following PRISMA 2020 guidelines, Scopus, PubMed, and Web of Science were searched from 1 January 2010 to 25 June 2026 using a validated string. Inclusion criteria were explicit detection of a pathogen, clearly described AI/ML algorithm, study evaluation on food or supply chains, and quantitative validation metrics. Exclusion criteria were chemical-only studies, human-diagnostic studies, or purely theoretical studies. Given heterogeneity in the evidence, qualitative quality indicators were favoured over formal quantitative risk-of-bias tools, in distinction to internal cross-validation versus independent external validation. Key data were extracted using a standardised matrix, and after screening and snowballing, the final corpus consisted of 152 studies. CNN (Convolutional Neural Network)-based microscopy provides >99% accuracy in bacterial identification, SERS (Surface-Enhanced Raman Spectroscopy) and CNN 98.68% for pathogens and 99.85% for resistant strains. ML-driven biosensors show 80–100% prediction accuracy in the presence of environmental noise. Yet, performance drops dramatically on external validation, with models falling from 95% internal to 78–82% on independent test sets. Supply chain applications cover meat, dairy, seafood and produce, but most are still at pilot scale. The main constraints are data heterogeneity, lack of public benchmarks, matrix interference, non-standard validation protocols, and regulatory dissonance. However, the integration of AI with Internet of Things (IoT), blockchain and edge computing improves sensitivity, reduces false results and enables real-time monitoring despite the challenges. AI is a powerful decision-support tool that complements existing food safety controls rather than replacing them. To translate these technologies reliably into routine practice, effective implementation requires rigorous external validation and regulatory harmonisation. Full article

This paper presents a systematic literature review examining how blockchain technologies can enhance the security and performance of multi-cloud systems. Multi-cloud architectures offer resilience, scalability, and flexibility; however, they also pose complex security challenges related to APIs, service-level agreements (SLAs), orchestration, and authentication. The promise of blockchain technology to improve the security and transparency of numerous applications, including cloud storage systems, has attracted considerable attention in recent years. Much research has focused on decentralized storage in cloud environments, spanning supply chains, FinTech, healthcare, and education. Still, the integration of blockchain with the cloud and its potential to enhance security and performance warrant an in-depth study. Using the PRISMA methodology, a structured search was conducted across six major scientific databases, including IEEE, ACM Digital Library, ScienceDirect, Scopus, Web of Science, and IJIMAI. Twenty-four primary papers published between 2019 and 2025 were selected for analysis after clear inclusion and exclusion criteria were applied. This review examines the security dimensions in multi-cloud environments—architectural vulnerabilities, API security, authentication, orchestration and automation vulnerabilities, SLAs, and cybersecurity compliance issues—in relation to blockchain technology. Based on the identified gaps, we propose a SOC-LLM-augmented security architecture that integrates blockchain-based evidence integrity, statistical anomaly detection, machine learning, large language models, and autonomous AI agents to enable intelligent attack detection and response. The proposed framework introduces specialized agents for detection, correlation, threat intelligence retrieval, blockchain evidence validation, explanation generation, and response planning. The analysis shows that integrating SOC-LLM capabilities with blockchain can move multi-cloud security from passive auditability toward proactive, explainable, and human-in-the-loop cyber defense. Finally, this paper discusses open challenges, including LLM hallucination, data scarcity, real-time scalability, evaluation standardization, and trustworthy deployment in critical multi-cloud infrastructures. The study’s conclusion highlights research gaps and suggests future lines of inquiry concerning scalable blockchain architectures and the incorporation of AI for proactive cloud security monitoring. Full article

The rapid deployment of sixth-generation (6G) Internet of Things (IoT) networks demands lightweight authentication mechanisms that provide low latency, high scalability, and robust security for resource-constrained devices operating in dynamic wireless environments. Conventional authentication approaches based on Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS), blockchain-assisted architectures, and Generic Bootstrapping Architecture (GBA)-based schemes introduce significant computational complexity, communication overhead, synchronization delays, and infrastructure dependencies, limiting their suitability for large-scale edge-assisted IoT environments. This paper proposes Lightweight Timed Efficient Stream Loss-Tolerant Authentication over the Constrained Application Protocol (L-TESLA-CoAP), a lightweight and infrastructure-independent authentication framework that integrates adaptive TESLA delayed-key authentication, CoAP communication, edge-assisted synchronization, replay-aware synchronization, SHA3-HMAC-based symmetric authentication, and rotating pseudonym identities to provide continuous packet-level authentication. The proposed framework was implemented and evaluated using a Python-based simulation environment under constrained 6G IoT communication scenarios with network sizes ranging from 50 to 1000 IoT devices. The comparative evaluation against CoAP, DTLS, TLS, Blockchain-CoAP, and GBA-Hybrid TESLA shows that the proposed framework achieves low authentication latency (approximately 0.8–1.3 s) and low energy consumption (approximately 60–75 mJ) while maintaining packet-loss recovery capability, reduced communication overhead, reduced computation time, low memory consumption, and authentication throughput. Furthermore, the proposed framework provides resilience against replay, packet injection, impersonation, synchronization manipulation, and denial-of-service attacks through adaptive synchronization and delayed key disclosure. These results indicate that L-TESLA-CoAP provides an efficient, scalable, and lightweight authentication solution suitable for next-generation edge-assisted 6G IoT applications. Full article

This article provides a systematic literature review of recent research on money laundering in crypto-asset environments, focusing on the main operational challenges and technical solutions proposed. Following PRISMA 2020 guidelines, the review draws on searches in Web of Science, Scopus and Google Scholar, which identified 680 records and led, after screening and the application of inclusion and exclusion criteria, to a final sample of 58 academic studies published between 2020 and 2025. The review identifies four core challenges in blockchain-based anti-money laundering: pseudonymity, label scarcity and class imbalance, structural and computational complexity, and cross-blockchain data fragmentation. In response, the literature proposes several detection approaches, particularly feature-based and graph-based models, focusing on transactions, addresses, mixers and service providers. The findings show that, although these methods improve the detection of suspicious activity, important limitations remain regarding real-world identity attribution, reliable AML-specific ground-truth labels, scalability, operational validation and cross-chain flow reconstruction. The article contributes an analytical framework that links structural AML challenges with methodological responses, supporting clearer comparison of models and identifying priorities for future research. It also highlights the need for multidisciplinary collaboration across data science, finance, regulation, economics and forensic investigation. Full article

As a booming application domain in sharing economy, the crowd logistics services (CLSs) have emerged in recent years to take advantage of under-utilized resources for generating economic value. However, unduly designed CLS system platforms may suffer substantial problems such as sensitive information exposure, excessive commission fees, and trust issues. To mitigate such problems, we propose an approach comprising four initiatives: (1) exploring the applicability of blockchain technology and its affiliated technology, smart contract, in CLSs to manifest blockchain-enabled benefits including service traceability, process transparency, system automation and disintermediation; (2) adopting blockchain and smart contract technologies to design a blockchain application system architecture (BASA) suitable for reengineering current CLSs; (3) demonstrating the blockchain-based crowd logistics services (BCLSs) system design, implementation, and deployment details; and (4) evaluating the functionality and benefit of BCLSs approach to confirm its feasibility and applicability. After presenting the system design and implementation outcomes, this study elaborates the benefits and implications of BCLS systems through four theoretical frameworks: e-Commerce Value Creation Theory (VCT), Innovation Diffusion Theory (IDT), Principal Agent Theory (PAT), and Transaction Cost Analysis (TCA), deriving important findings and implications. Such benefits and implications suggest that BCLSs may help CLSs (1) mitigate PAT frictions and reduce transaction costs; (2) redefine value creation and accelerate innovation diffusion; (3) eliminate platform monopolies and achieve real-time settlement; (4) implement data sovereignty and enhance privacy/security; and (5) reconfigure trust mechanisms and generate digital credit assets. The research results of this study may help the CLS industry clarify the BCLS system’s upgrade path, promote business model innovation, and enhance fair governance and social sharing. Full article

Blockchain technology has profoundly revolutionized decentralized applications across financial systems, global supply chains, and applied informatics. However, it remains susceptible to systemic security hazards. This systematic review comprehensively evaluates core architectural vulnerabilities within blockchain infrastructures, consensus mechanisms, and peer-to-peer (P2P) network layers spanning the decade from 2015 to 2025. We focus primarily on the mechanics, operational taxonomy, and evolutionary trajectories of Sybil attacks, wherein malicious actors forge multiple pseudonymous identities to gain disproportionate systemic influence. By synthesizing the foundational academic literature with real-world empirical case studies, such as automated airdrop farming exploits in Layer-2 ecosystems (e.g., Arbitrum, zkSync) and decentralized finance (DeFi) governance manipulations, we analyze attack mechanisms, quantifiable impacts, and mitigation vectors. Our findings chart the structural evolution of Sybil strategies from rudimentary P2P routing disruptions to complex, economically driven application-layer interventions. Finally, we evaluate contemporary defenses, such as Proof-of-Personhood (PoP) systems and zero-knowledge (ZK) cryptography, offering actionable recommendations for the integration of W3C-compliant decentralized identity (DID) frameworks and behavioral analytics to enhance systemic fault tolerance. Full article

A blockchain address serves as the unique identifier for users within a blockchain network. While existing blockchain solutions typically employ the Elliptic Curve Digital Signature Algorithm (ECDSA) for address generation, researchers including Ruimin Wang and Jisheng Dong have proposed alternative schemes based on SM9 identity-based cryptography to circumvent the inherent limitations of the Public Key Infrastructure (PKI). However, while preserving the address privacy of transacting parties, these schemes lack traceability, creating vulnerabilities to Sybil attacks in application scenarios such as voting. To address this issue, this paper proposes a novel blockchain address generation scheme based on SM9 identity-based cryptography. Incorporating the concept of hierarchical cryptography, we design a two-tier key derivation structure to guarantee the address privacy of both transacting parties. Furthermore, the generation of derived keys integrates secret sharing technology, thereby achieving source traceability with provable linkage. Provided that participants have registered a legitimate identity (blockchain address), utilizing the proposed scheme to construct derived addresses effectively prevents them from launching Sybil attacks via these derived addresses. The security properties of the proposed scheme—including uniqueness, unpredictability, pseudonymity, traceability, and Sybil resistance—are formally proven under the random oracle model. Finally, theoretical analysis and experimental results demonstrate that, alongside enhanced security (100% Source Tracing Accuracy), the proposed scheme incurs no significant increase in key and address lengths compared to existing solutions, with only a marginal increase in computational overhead. Full article

This article examines Liquid Equity Rewards (LERs), a proposed blockchain-enabled mechanism designed to provide shareholders with time-weighted, utility-only incentives as a potential tool for improving corporate governance. LERs employ a dual architecture comprising voucher-based rewards for off-chain equities and programmable on-chain units for tokenized stocks to encourage shareholder retention amid proxy battles, activist challenges, and corporate political complexities. Drawing on NASDAQ’s tokenized stock framework, stablecoin infrastructure, and DeFi liquid staking principles, this article develops a conceptual and normative framework for LERs and evaluates its potential effectiveness relative to conventional defenses such as poison pills. The analysis assesses LER’s plausible legal compatibility with Delaware corporation law, U.S. securities rules, and the EU’s MiCA framework, while acknowledging that definitive legal conclusions require case-specific adjudication and future regulatory interpretation. The article advances four testable hypotheses regarding LER’s potential to mitigate stock price volatility, reduce activist success rates, and address ESG, M&A, and political expenditure disputes in a market context shaped by shareholder activism. The proxy-fight context is the principal application; ESG, M&A, political-spending, and executive-compensation contexts are discussed as illustrative extensions of the framework rather than as equally mature use cases. A comparative evaluation against existing governance mechanisms and a cost–benefit analysis suggest that LER’s governance enhancements and market opportunities may outweigh implementation challenges, subject to empirical validation. This article contributes a structured analytical framework and identifies conditions under which LERs could offer a scalable, transparent alternative that fosters stakeholder alignment. Full article

The transition from centralized fossil fuel-based power systems toward decentralized smart grids with a high penetration of renewable energy sources (RES) introduces substantial challenges in monitoring, control, coordination, and management. These challenges are particularly evident at the active power grid periphery, defined in this work as the decentralized edge layer of modern power systems comprising low-voltage distribution networks, distributed energy resources (DERs), prosumers, energy storage systems, electric vehicles (EVs), and localized intelligent control entities operating near the consumer side of the grid. This review systematically examines the role of multi-agent systems (MASs) in addressing these emerging challenges. A total of 160 articles, drawn predominantly from top-tier Q1 journals and published up to March 2026, were systematically analyzed to evaluate recent methodological advances, identify persistent research gaps, and compare existing problem formulations and mathematical techniques. The review covers MAS-based applications including distributed energy management, voltage and frequency regulation, demand-side management, microgrid coordination, EV charging coordination, resilience enhancement, and cyber-physical supervisory control. The findings indicate that although MASs offer enhanced scalability, flexibility, resilience, and decentralized decision-making capabilities, existing approaches continue to face significant limitations associated with communication latency, cybersecurity vulnerabilities, interoperability constraints, heterogeneous agent dynamics, and limited real-time experimental validation. Furthermore, this review proposes six emerging research hypotheses targeting underexplored domains, presents a methodological decision flowchart for MAS implementation and selection, and discusses future research directions involving the integration of digital twins, blockchain technologies, edge intelligence, and advanced communication architectures with MAS frameworks. Full article

Inter-organizational construction projects depend on intensive information exchange, interface coordination, and joint action among multiple organizations. However, their temporary, fragmented, and multi-actor nature often creates information asymmetry, unclear responsibility attribution, and weak trust foundations, thereby increasing collaboration costs and reducing governance efficiency. Although blockchain technology is often considered capable of addressing these problems through tamper-resistant records, transparent sharing, and smart contract execution, existing studies remain largely focused on technical functions or application scenarios. Less attention has been paid to how blockchain influences trust formation and inter-organizational collaboration through informational governance mechanisms. To address this gap, this study adopts a conceptual theory-building research design and develops a governance-oriented framework explaining how blockchain-enabled informational attributes affect trust and collaboration in construction projects. The study conceptualizes the blockchain’s governance role as an informational governance mechanism rather than a mere technical deployment, focusing on four informational attributes embedded in project transaction records: security, fidelity/authenticity, transparency, and procedural consistency. It further distinguishes interpersonal trust from inter-organizational trust, arguing that blockchain informational attributes may shape both interaction-based trust among boundary spanners and organization-level collaboration expectations. Interpersonal trust may also inform broader trust judgments toward partner organizations. At the collaboration level, the study conceptualizes inter-organizational collaboration as a process structure consisting of coordination and cooperation, with coordination creating conditions for deeper cooperation. The resulting framework links blockchain informational attributes, dual-level trust, coordination, and cooperation, and develops hypotheses for future empirical testing. This study contributes by shifting construction blockchain research from technology adoption and functional description toward informational governance explanation, extending dual-level trust logic into blockchain-enabled construction project governance, and applying the coordination–cooperation process logic to unpack how blockchain-enabled informational governance may influence inter-organizational collaboration. Full article

Public blockchain transactions need compliance support without exposing stable identity anchors. This paper presents a privacy-preserving transaction admission framework for regulated cryptocurrency transaction paths. It adds an admission and recovery layer to admission-aware applications, contracts, gateways, or chain policies without introducing a new cryptocurrency. In ordinary use, a user submits a public transaction object and a single Groth16 admission proof, which checks registration membership, current state, hidden home-domain membership, hidden home-domain policy compliance, transaction binding, replay control, and trace-object well-formedness while hiding identity-related witnesses. When a valid warrant targets an accepted transaction, recovery committees perform transaction-scoped trace recovery through outer opening and domain-local inner recovery. We implement an end-to-end prototype with off-chain modules and an Ethereum Virtual Machine (EVM)-compatible public admission contract. The evaluation reports a 28,370-constraint circuit, 3.40 s average proving time, about 536k local EVM admission gas, 30/30 replay rejection, 8.46 ms local layered recovery latency, and 3.84 ms domain-dispatch latency. Full article