Meeting the global demand for fresh, minimally processed food requires us to rethink how we monitor food safety. Traditional laboratory methods are often too slow, labor-intensive, and impractical for real-time applications. To overcome these delays, biosensors have emerged as a rapid, highly sensitive, and cost-effective alternative. This study explores how biosensing technology accurately detects pathogens, chemical contaminants like heavy metals and pesticides, and spoilage indicators across dairy, meat, produce, and packaged foods. What makes these tools truly transformative is their seamless integration with modern digital infrastructure. By combining biosensors with the Internet of Things (IoT), artificial intelligence (AI), nanotechnology, edge computing, and blockchain, we can create intelligent, continuous monitoring systems. These interconnected frameworks allow for real-time, farm-to-fork traceability, enabling early hazard detection, extending shelf life, and significantly reducing food waste through data-driven decisions. Despite this immense potential, bringing smart biosensors to the commercial market involves overcoming distinct practical hurdles. We examine current technical barriers, including biofouling, long-term sensor stability, power management, and high manufacturing costs. More importantly, we highlight the emerging innovations actively solving these bottlenecks, such as biodegradable materials, battery-free platforms, advanced printed electronics, and smart packaging technologies.
Scalable event-driven architectures are now the focus of enterprise supply chain and logistics research as this information is surfaced from transport assets, warehouses, suppliers, platforms and risk environments at a more frequent rate to allow for faster decision making. This review looks at the concepts of peer-reviewed studies of 2015â2025 that have focused on architectures that have the ability to transform distributed events into traceability, resilience, visibility, and automated coordination. The review of the literature shows that there is no single concept but rather scattered concepts in the domain of scalable event-driven logistics within the fields of Internet of Things (IoT) in logistics, Logistics 4.0, big data analytics, blockchain traceability, multi-agent control and digital supply chain twins. These streams have significant challenges around event capture, real time analytics, decentralized provenance, and disruption response. Key gaps remain in latency benchmarking, cross-enterprise semantic interoperability, governance of shared event streams, and validated architecture-level performance evidence. The field is significant due to the increased reliance on enterprise architectures that extend beyond the organizational boundary that are also responsive, auditable and resilient.
ABSTRACT Real Estate Tokenisation started with the introduction of blockchain technology. It is changing the worldâs way of investing in assets by providing proportional ownership, improving liquidity and basically giving retail investors access to HNI investment opportunities they didn't have before. This technology is getting a lot of global attention but institutional adoption is largely dependent on the environment that regulatory bodies set for digital assets and tokenized securities. This study help to present the role of regulation in institutional adoption of real estate tokenization through a comparative analysis of three main jurisdictions India, IFSC GIFT City and the United Arab Emirates (UAE). Using secondary data the study analysed regulatory documents, policy reports and academic literature through a qualitative comparative policy analysis and thematic content analysis. The research looks at the impact of regulatory certainty, recognition through laws, investor protection, licensing, digital marketplace infrastructure and frameworks for foreign investments on institutional confidence. The finding show that institutions are more likely to participate where regulatory certainty, licensing frameworks and innovation-friendly policies can coexist. India has shown increasing regulatory engagement with digital asset innovation with IFSC GIFT City has given a more progressive regulatory environment for international financial activities whereas the UAE has supported itself by dedicated virtual asset regulations and innovation based policies and has established itself as a leading jurisdiction for institutional tokenization initiatives. Proposed by this study is the Institutional Regulatory Readiness Framework (IRRF) which is an six dimensional conceptual framework for measuring the institutional readiness towards real estate tokenization in various jurisdictions. The paper proposes a Regulatory Readiness Framework that combines legal, technological and institutional dimensions to provide knowledge of jurisdictions for large-scale real estate tokenization. By comparing three regulatory ecosystems, the study offers practical advice for policymakers, regulators, financial institutions and market participants who are seeking to improve institutional adoption. Index Terms: Real Estate Tokenization, Institutional Adoption, Blockchain, Regulation, IFSC GIFT City, United Arab Emirates, India, Real-World Assets (RWA), Digital Assets, Regulatory Readiness.
This study examines the current state of digital asset auditing and proposes a clearer future vision through a systematic review of relevant literature and prior studies. It highlights the fundamental differences between digital and traditional assets, explains the classification of digital assets and their close association with blockchain technology, and analyzes the existing accounting and auditing frameworks considering international standards and provides a brief overview of the status of Egyptian legislation. The study also discusses the evolving role of auditors and the main stages of the audit process in the digital environment. The findings indicate that rapid digital transformation requires the development of advanced auditing standards and methodologies, and that the adoption of data analytics, smart contracts, and continuous auditing, together with enhancing auditorsâ technical and professional competencies, contributes to improving audit quality, transparency, and risk management related to digital assets.
Open access
Security, Politics, and Digital Transformation
Financial Reporting and XBRL
Innovations and Analysis in Business and Education
P. Thanalakshmi, V. G. Kiruthika, J. C. Gokul Abinash, P. Saravanan ¡ 6 authors
Wireless Sensor Networks (WSNs) are vulnerable to malicious nodes and sensor node failures, which compromise data integrity and network reliability. These threats result in incorrect decisions and reduce system trust. To address this, machine learning algorithms enable anomaly detection by identifying abnormal sensor nodes, while blockchain ensures secure and tamper-proof data storage. However, reliable consensus is essential before data validation in the blockchain. A hybrid framework combining ML, blockchain, and a modified HotStuff consensus algorithm with post-quantum cryptographic systems provides secure, fault-tolerant, and quantum-resistant consensus, ensuring trustworthy and resilient WSN operations.
Online voting platforms that rely on classical cryptography and centralized trust anchors face escalating challenges as the demand for secure and transparent digital elections grows. Such systems remain exposed to quantum-era threats, insider manipulation, and delayed audit mechanisms, which together can undermine public confidence and electoral legitimacy. To counter these risks, a quantum-resistant, multi-layer blockchain architecture has been developed to enable remote voting with continuous verifiability and resilience. This architecture resolves key weaknesses through five integrated layers. Quantum-Resistant Distributed Ledger Initialization (QR-DLI) embeds lattice-based cryptography, specifically Kyber and Dilithium variants, directly within the genesis block, ensuring the ledger is tamper-proof from inception and immune to quantum brute-force attacks. The Self-Adaptive Smart Contract Governance Engine (SASCG) introduces dynamic, participation-aware rule adjustments, allowing principled governance without manual overrides and ensuring that voting periods and eligibility rules adapt securely in real time. Homomorphic Vote Encryption with Multi-Authority Shard Key Distribution (HVE-MASKD) guarantees ballot confidentiality and authenticity by combining fully homomorphic encryption with distributed key shares, eliminating single points of trust. The Zero-Knowledge ProofâBased Real-Time Audit Layer (ZKP-RTAL) continuously validates ballot integrity while concealing vote content, creating a public and immutable audit trail. Finally, the Federated Performance & Threat Intelligence Optimizer (FPTIO) aggregates live telemetry and historical attack data to proactively tune consensus parameters and predict potential intrusions without interrupting the election process. Collectively, these layers achieve sub-second cryptographic operations, transaction throughput exceeding 1,500 TPS, over 99 % fraud detection accuracy, and strong scalability. The model provides a future-ready, auditable replacement for current e Voting infrastructures, strengthening digital democracy through post-quantum security, adaptive governance, and intelligent, continuous optimizations.
Multi-cloud adoption has widened the enterprise attack surface to a degree that perimeter-based defence can no longer address. Traffic is now flowing continuously across AWS, Azure, and GCP, and the majority of deployed Zero Trust Architecture (ZTA) systems are still using static rule tables, with no ability to provide an audit trail of the reasoning behind decisions, and with logs stored in datastores that can be modified by an insider without detection. This paper proposes ZT-ChainGuard, a framework that overcomes these three limitations in one architecture that integrates an ensemble machine learning trust-scoring engine, ZTA policy enforcement and a blockchain-based audit trail. The trust-scoring engine is a two-layer stacking ensemble, with XGBoost and Random Forest as base learners, and Logistic Regression as a meta-learner, and it returns a continuous trust score, P(Attack | flow), for each network flow, which is then used to trigger the ZT policy decision at a threshold of 0.5. The explanation of each decision is provided by SHAP values at both the global and per-flow level, and each decision is stored as an immutable, SHA-256 hash-chained block. On CICIDS2017 (2.83 million flows, 14 attack classes) the framework achieves 99.90% accuracy, 99.71% F1-score, and 99.99% ROC-AUC; on ToN-IoT (2.23 million IoT records, 9 attack types) it achieves 99.81% accuracy, 99.88% F1-score, and 100% ROC-AUC. The latency of inferences is 0.006ms per sample, and the overhead of auditing the blockchain is 0.019ms per block. This performance is not just a quirk of a particular split, as it is shown to be stable across the three folds of three-fold cross validation.
To handle the main problem of double-spending attacks in blockchain networks, this paper introduces a new, Light-weight Graph Neural Network (LGNN) approach named Dynamic Sparse Graph Attention Network (DSGAT). To effectively detect double spending behavior, DSGAT method integrates adaptive graph sparsification with attention based on the fundamental graph-structured nature of blockchain transactions. Unlike computationally intensive GNNs, DSGAT may be implemented on edge devices or distributed monitoring systems with low-tech, low-cost hardware since it is optimized for resource-limited environments and doesn't need much processing capacity. To detect double-spending attack, this paper explains building blockchain transaction graphs from a large set of node and edge features. A set of simulated transactions involving double-spending attack is generated using large-scale simulations with the BCASim blockchain simulator, and the performance of DSGAT is compared with normal baselines. The experiment's outcomes prove that DSGAT is able to reduce model sizes and inference latency while keeping high detection rates, proving its feasibility and effectiveness for real-time double spending detection in low-resource environments. To improve blockchain security against double-spending attacks, this paper introduces a novel and realistic alternative.
.This critical review evaluates the article âFactors Influencing Blockchain Adoption in the Tourism Industry: An Empirical Study,â focusing on its scientific quality, theoretical foundations, methodology, empirical findings, and contribution to the literature. The review examines the formulation of the research problem, the integration of the HOT-fit and TOE frameworks with sustainability dimensions, the application of PLS-SEM, and the interpretation of the studyâs findings. It also identifies the articleâs main strengths and limitations and assesses its theoretical and practical relevance to blockchain adoption, digital transformation, innovation management, and tourism research.
This urgent book foregrounds the role of the senses in understanding the more-than-human. It explicates the aesthetics of the more-than-human, emphasizing the critical situation it faces today through multiple environmental crises, including pollution, climate change, and species extinction. Drawing on perspectives fr
MARBIYAT TAHIR GIDADO, BASHIRU ABDULGANIYU, MOHAMMED NASIR MUSA, Umaru Umaru
The increasing digitalization of smart grids has significantly improved the efficiency, reliability, and sustainability of modern power systems. However, the integration of advanced technologies, such as artificial intelligence, the Internet of Things, and cloud computing, has introduced new cybersecurity vulnerabilities that threaten critical energy infrastructure. This study presents a blockchain-enabled privacy-preserving Artificial intelligence framework designed to enhance cybersecurity in smart grid environments, with a particular focus on Northeast Nigeria as a case study. The framework integrates blockchain technology, federated learning, differential privacy, edge computing, and artificial intelligence (AI)-driven intrusion detection into a unified architecture to provide secure, intelligent, and privacy-aware protection for smart grid systems. The proposed framework was developed using the design science research methodology and evaluated through simulation and comparative performance analysis. The framework achieved excellent detection performance with an accuracy of 96.8%, precision of 95.9%, recall of 96.4%, and F1-score of 96.1%, significantly outperforming conventional centralized AI and blockchain-only approaches. The integration of federated learning and differential privacy effectively protected consumer information with a privacy leakage rate of only 2.7% while maintaining high model utility of 94.8%. The blockchain performance evaluation showed a transaction latency of 184.6 Ms, a throughput of 421.3 transactions per second, and efficient smart contract execution. The suitability of the framework for practical deployment with moderate resource requirements by computational assessment. The findings demonstrate that combining blockchain, privacy-preserving learning, and AI provides a comprehensive, scalable, and resilient cybersecurity solution for SGIs. This study contributes to the growing body of knowledge on smart grid cybersecurity and offers practical insights for utility providers, researchers, and policymakers seeking to strengthen the security and resilience of emerging smart grid systems, particularly in developing regions with infrastructural challenges.
This paper presents a comprehensive study on integrating Deep Learning (DL) modelling Long Short-Term Memory (LSTM)-based models with blockchain technology to deal with the most critical problems in healthcare data management, security and analytics. Escalating the size of healthcare data exponentially due to the development of e-HRs (electronic health records), wearables, and real-time monitoring systems pushed traditional data storage and processing practices into the limelight as their most significant weaknesses. LSTM networks are perfect for analyzing time-series data in health care, such as disease classification, anomaly detection, and patient outcome prediction over the long run. Nevertheless, these models require sound data protection techniques and privacy measures to be followed per the regulations while maintaining trust. Blockchain technology fills in the gaps beyond LSTM by offering a decentralized, tamper-proof platform to safely store and share data, keeping confidentiality, integrity, and availability simultaneously. This paper surveys the available literature on hybrid models by flushing out the topic with the help of LSTM and blockchain. It explores their potential use in real-time healthcare analytics applications, along with the challenges of scalability and interoperability. By presenting a model through the use of these technologies, the research centres on sharpening health information systems such as accuracy, security, and transparency, which in turn intensify the trust of both the patients and the providers of care, thus enabling the development of a patient care solution that is more reliable and efficient.
This study maps the development, collaboration patterns, citation structure, and thematic evolution of research on blockchain technology in the waqf sector. A bibliometric analysis of 417 Scopus-indexed publications published from 2006 to 12 July 2024 was performed using Bibliometrix in RStudio and VOSviewer. The analysis covered publication trends, influential sources and contributors, country productivity, citation impact, collaboration networks, and keyword co-occurrence. The results show increasing scholarly attention to the intersection of blockchain, Islamic finance, fintech, and waqf management. Malaysia and Indonesia emerged as the most productive and most cited countries, while an international co-authorship rate of 29.74% indicated moderate cross-border collaboration. Keyword analysis revealed that the field is anchored in Islamic finance, fintech, blockchain, and waqf, with growing attention to cash waqf, crowdfunding, financial inclusion, digital transformation, smart contracts, cybersecurity, and technology adoption. However, these patterns demonstrate scholarly attention and thematic associations rather than empirical proof of blockchainâs operational benefits in waqf institutions. This study identifies priority gaps in empirical implementation, Shariah governance, stakeholder adoption, technical feasibility, and socioeconomic impact evaluation of blockchain-enabled waqf systems.
Rafael Hoffmann, Carlos Moratelli, Alex S. R. Pinto
ABSTRACT Background Preserving the quality and safety of perishable products requires continuous monitoring and reliable traceability. Although the Internet of Things (IoT) enables realâtime data collection, multiâorganizational supply chains lack a common mechanism for assigning data custody while maintaining transparency, integrity, and performance. Objective This study proposes and evaluates an architecture integrating IoT, edge/fog computing, and hybrid storageâan offâchain traditional database combined with a permissioned blockchainâto monitor and trace perishable products. Methods A prototype was implemented using IoT devices and simulators, edge and fog components, and hybrid storage. Highâvolume sensor data and critical records were stored offâchain in MongoDB, while their corresponding hashes were stored onâchain using Hyperledger Fabric. Four controlled experiments assessed insertion response time, the impact of increasing sensors and edge devices, blockchain queue performance under burst workloads, and blockchain storage consumption. The hybrid approach was compared with MongoDBâonly and Hyperledger Fabricâonly storage. Results Hybrid storage achieved insertion up to six times faster than blockchainâonly storage. Response times increased with simultaneous requests and additional edge devices, while asynchronous ordered insertion prevented transaction conflicts during bursts. The prototype achieved 18.5 transactions per second, below the 65 estimated for an illustrative supplyâchain scenario. Blockchain storage grew approximately 8 MB per 100 records, reaching about 1 GB for 12,800 hashes. Conclusion The prototype demonstrates the feasibility of combining offâchain storage, permissioned blockchain records, and edge/fog processing to provide verifiable traceability while reducing onâchain load. Largerâscale, realâworld evaluations and storageâmanagement strategies remain necessary.
This study is based on proliferation of IoT devices has created demand for high-rate, low-cost microtransactions, yet conventional blockchains impose fees, latency, and throughput limits that hinder scalable IoT finance. This study aimed to evaluate a hybrid architecture that places microtransactions on a lightweight DAG(IOTA/Tangle-style) plane while periodically anchoring compact commitments to Ethereum to reconcile performance with public auditability. The study implemented a reproducible simulation that ingests IoT telemetry (TON_IoT-style), a DAG transaction and tip-selection model at the edge and samples Ethereum fee/confirmation priors from Google BigQuery public datasets to generate realistic on-chain settlement costs and latencies. Scenario attacks (flooding, replay, recipient-entropy) were injected, and detection models (sequence + +graph features) were evaluated. DAG operation yielded near instant local confirmation (â1.4 s) and â1200 tx/s throughput versus â13.2 s and â14 tx/s on Ethereum. Periodic anchoring reduced amortized per-payment cost from â$0.72 (naive on-chain) to â$0.0144 with modest finality delay (median â600s), a â98% cost reduction. F1 detection was high (â0.85-0.91) with an acyclic graph (DAG). Ethereum is a practical approach for transparent, secure IoT financial transactions since it preserves DAG performance while providing immutable auditability at negligible amortized cost. Exploring privacy-preserving anchors as well as multi-gateway resilience has become a stepping stone for future research.
Abstract: The evolution of monetary systems has transformed human civilization from simple barter exchanges to sophisticated digital financial ecosystems powered by blockchain technology. This review examines how barter systems evolved into con-temporary virtual currencies across history and assesses how cryptocurrencies fit into the circular economy. The study explores the shortcomings of conventional monetary systems and looks at how decentralized, transparent, and effective forms of economic transaction have been made possible by digital currencies like Bitcoin. Additionally, the study examines how blockchain technology might be used to support waste reduction, sustainability, resource efficiency, and transparent supply chain management. The study also assesses the difficulties posed by virtual currencies, such as market volatility, cybersecurity threats, regulatory ambiguity, and environmental issues pertaining to cryptocurrency mining. The review identifies significant research gaps and future prospects for incorporating virtual currencies into sustainable economic systems by synthesizing the body of existing work. The results indicate that through openness, decentralization, and technological innovation, blockchain-enabled financial systems have a great deal of potential to promote circular economy goals. Keywords: Virtual Currency, Cryptocurrency, Bitcoin, Blockchain, Circular Economy, Sustainable Finance, Digital Economy, Decentralization, Green Finance, FinTech, Supply Chain Management
Blockchain interoperability remains a major challenge because heterogeneous blockchain networks cannot securely and efficiently exchange cross-chain data and transactions. Existing interoperability solutions often rely on central relays or trusted intermediaries, creating security vulnerabilities, limited fault tolerance, and a single point of failure. To address these limitations, this paper proposes VeriMesh, a decentralised mesh-based interoperability framework that combines trust-adaptive routing, multi-path relay verification, and Zero-Knowledge Proof (ZKP)-based validation for secure cross-chain communication. VeriMesh models relay nodes as a trust-weighted graph in which routing decisions dynamically adapt based on node behaviour and delivery reliability. Multi-path routing improves resilience against adversarial relay nodes, while transport-layer ZKP verification enables privacy-preserving validation without exposing sensitive information. The framework was implemented using Python relay nodes, Solidity smart contracts, and an Ethereum (Ganache) environment. Experimental evaluation using structured event-driven workloads demonstrated stable latency below 34 ms and delivery success rates above 85% up to 40% malicious node presence. Comparative evaluation against single-path and random multi-path relay baselines showed improved fault tolerance and routing reliability. The results demonstrate favourable scalability and robustness within the evaluated network range ( N = 10â30), while larger-scale evaluation remains future work. All experiments were conducted in a controlled local Ganache blockchain environment rather than on a public Ethereum testnet or mainnet, so the reported latency, gas, and delivery figures characterise protocol-layer behaviour under controlled conditions and should not yet be interpreted as representative of performance under public-network conditions such as real gas markets, block propagation delays, or network congestion.
The tokenization of Real-World Assets (RWAs) represents a paradigm shift in bridging traditional financial instruments with decentralized infrastructures. However, as the market transitions from proof-of-concept to institutional scale, it faces a critical structural bottleneck: the "walled garden" liquidity crisis. Driven by stringent regulatory requirements, tokenized assets are currently deployed across fragmented, permissioned blockchain networks utilizing static, hard-coded compliance logic. This siloed architecture inherently restricts cross-chain mobility, fracturing secondary market liquidity and necessitating redundant authentication processes across jurisdictions. This paper proposes a comprehensive architectural framework to resolve the interoperability trilemma inherent in regulated digital assets. By synthesizing recent advancements in cross-chain messaging protocols and Zero-Knowledge Proofs (ZKPs), we present a model for dynamic compliance. This framework utilizes Decentralized Identifiers (DIDs) and off-chain verifiable credentials to decouple regulatory logic from underlying asset ledgers, enabling seamless asset transfer across heterogeneous blockchains without compromising privacy or jurisdictional adherence. Ultimately, this research provides a technical and regulatory roadmap for policymakers and protocol developers to foster a unified, globally liquid market for tokenized RWAs.
The increasing demand for trustworthy and privacy-preserving credit reporting systems has exposed the limitations of both centralized and existing blockchain-based solutions, including scalability bottlenecks, weak privacy protection, and insufficient incentive mechanisms. To address these challenges, we propose LightCred, a novel consortium blockchain-based personal credit management framework that integrates lightweight nodes, Merkle proofs, multi-role smart contracts, and privacy-preserving cryptographic techniques. LightCred features a five-layer architecture that efficiently collects, verifies, stores, and serves credit data while ensuring data integrity, confidentiality, and regulatory compliance. Specifically, it (i) employs a low-cost and traceable data reduction mechanism through lightweight nodes and Merkle proofs to minimize storage and improve verifiability; (ii) introduces a multi-role smart contract model that enforces dynamic access control and fair incentive distribution based on participant reputations; and (iii) integrates zero-knowledge proofs and homomorphic encryption to support privacy-preserving credit scoring and querying. Experimental results demonstrate that LightCred achieves superior performance compared to five baseline methods, delivering up to 5% higher throughput, 3â5% lower privacy leakage, and 10â15% reduced storage costs, while maintaining competitive latency and auditability. These findings validate LightCred as a robust, scalable, and privacy-aware credit management solution, offering a viable alternative for modern credit reporting systems.
Agnes Nalini Vincent, Nassirah Laloo, Mohammad Sameer Sunhaloo, Uhoze Bagurubumwe
Digital elevation model (DEM)-based terrain analysis is an important geographic information system (GIS) methodology that serves as the core aspect for spatial analysis applications in geomorphological studies. These analyses can be performed using cloud-based platforms like Google Earth Engines or ArcGIS online, or using a local GIS platform called quantum GIS (QGIS). The resulting terrain data must be disseminated. Geospatial data sharing and dissemination are crucial for promoting cooperation, effectiveness, efficiency, and optimized decision-making in a variety of industries. Geospatial terrain data plays a crucial role in site selection and industrial planning, automated logistics, autonomous vehicle navigation, and infrastructure resilience in manufacturing ecosystems. However, existing literature states that traditional systems lack mechanisms to detect tampering in elevation models, land surveys, or hydrological data. Because of this, manufacturing systems face risks such as flawed factory site selection, disrupted supply routes, or unsafe autonomous vehicle navigation, data tampering in production logs, 278 counterfeit parts in supply chains, and a lack of real-time traceability. Hence, to manage the limitations of conventional terrain data storage and handling, this study proposes a blockchain-based framework to secure QGIS-processed terrain data, ensuring immutability and traceability for smart manufacturing applications. Blockchain distributed ledgers can permanently store high-resolution terrain data, minimize the chance of unintended alterations, and promote transparent, unrestricted collaboration. Using the country of the Republic of Mauritius as a case study toward tropical island states, this work demonstrated how elevation, slope, and aspect data extracted via QGIS can be securely stored and verified on a distributed ledger. Furthermore, this study incorporates an integration layer into the framework. The purpose of this integration layer is to enable real-time terrain alerts, smart contract-driven compliance checks, and to arrive at closed-loop feedback from IoT sensors. Thus, this proposed framework bridges blockchain-secured terrain data with manufacturing execution systems (MES) and IoT-enabled logistics networks.