John Alexander Taborda, Cesar Enrique Polo Castro, Alexander Armando Bustamante, Holman Dario Bustos
The transition toward decentralized renewable energy systems creates monitoring problems that current digital infrastructures do not solve: sustainability claims are produced by the same actors they evaluate, environmental evidence is reported periodically rather than observed continuously, and the communities most affected by deployment cannot inspect the data used to represent their territories. Existing integrated platforms combine subsets of the blockchain, Internet of Things (IoT) sensing and life cycle assessment (LCA) at the data layer, but they do not organize that integration through an explicit governance structure. This paper contributes a cybernetic governance framework in which the Viable System Model (VSM) supplies the organizing structure of a blockchain–IoT–LCA monitoring architecture, so that sensing, distributed trust, strategic intelligence and participatory governance are recursively coupled rather than sequentially chained. The framework was developed and evaluated under the Design Science Research paradigm, and instantiated in the IMPACT Energy.CO platform across two technology routes, wind and solar, in La Guajira, Cesar, Atlántico and Magdalena, Colombia. Evaluation against six pre-declared criteria reports 45 executed test cases with a 100% pass rate, 90% unit and 87% integration code coverage, load tests up to 5000 concurrent users with zero errors and sub-second mean response, an operating hash-chained provenance layer issuing verifiable LCA certificates, 14 participatory validation workshops, 199 users trained and 166 technicians certified. We use traceability in a deliberately narrow sense throughout: the property whereby a committed record can be linked to the ingested data series, model version and computation that produced it, and its integrity and ordering checked by a party that does not trust the producer. It is provenance and integrity traceability from the point of ingestion onward, and it is not metrological traceability: the architecture cannot verify that an original sensor measurement corresponds to the physical quantity it purports to represent. We accordingly make explicit what the architecture does not guarantee: a ledger protects records after commitment but cannot certify measurement at the point of capture, and we present a threat model, a set of implemented controls and the residual risk that remains. This study contributes an architecture, a reproducible development and evaluation method, and a calibrated account of what verifiable environmental monitoring can and cannot deliver in contested Global-South territories.
Haitham A. Mahmoud, Ahmed Soliman, Mohammed El-Meligy, Azhar Imran · 5 authors
Abstract Modern digital ecosystems rely mostly on blockchain technology, such as decentralized and immutable ledger systems. This technology avails guarantees of secure transaction and data administration in keeping with the privacy of consumers. Thus, the blockchain systems often suffer in resource-constrained environments to experience considerable computational overhead along with low scalability and issues in handling real-time data. To overcome these restrictions, this research incorporates federated learning, decentralized storage using IPFS, and lightweight cryptographic methods to deliver secure, scalable, and real-time analytics in the IoT system. This research has proposed a novel framework based on blockchain, privacy-preserving techniques, and predictive maintenance models to address some of the security, scalability, and reliability challenges observed in IoT ecosystems. The framework guarantees secure data management, efficient real-time analytics, and robust anomaly detection by using the most advanced technologies such as federated learning, decentralized storage, and lightweight cryptographic methods. The suggested technique exceeds traditional methods by means of accuracy and error reduction with the astonishingly low FPV value of 0.005954% and FNR value of 0.000274% while giving extraordinary performance metrics that reach 99.88% accuracy, 99.89% precision, 99.97% recall, and 99.93% F1-score. This solution establishes secure, scalable, and tamper-proof infrastructure for all the applications from industrial automation, healthcare to vehicular networks, hence enabling smart and sustainable IoT governance for these applications.
This paper presents and evaluates a hybrid blockchain architecture for organizational information systems in which PostgreSQL remains the operational database while Ethereum Sepolia is used as an externally verifiable transaction-recording layer. The implemented proof-of-concept is a university wallet system combining a React and TypeScript frontend, an Express.js backend, PostgreSQL with Drizzle ORM, and an OpenZeppelin-based ERC-20 smart contract deployed on Ethereum Sepolia. The system associates successful application transactions with corresponding Ethereum transaction hashes stored in a dedicated relational table. The experimental evaluation uses sequential workloads of 10, 50, and 100 transactions, comprising 160 measured application transactions in total, together with a separate 30-transaction database-mutation experiment. The performance evaluation measures database insertion time, blockchain transaction time, end-to-end execution time, success rate, and gas consumption. All 160 performance-test transactions completed successfully. Mean database insertion time remained below 32 ms, while blockchain transaction time ranged from approximately 15.26 to 21.93 seconds and dominated end-to-end execution time. Mean gas consumption was approximately 40,324 gas per successful transfer. The database-mutation experiment modified the amount field of 7 of 30 successfully recorded transactions after their blockchain references had been established. All seven modified records retained their corresponding blockchain transaction references. However, the experiment did not perform field-level comparison between the modified database records and blockchain event contents and therefore is not presented as a complete cryptographic tamper-detection validation. The implementation and experimental artifacts are publicly available through the associated project repository. The paper presents the work as a proof-of-concept implementation and empirical evaluation of a hybrid database-to-blockchain transaction architecture.
Blockchain is one important building blocks of the Internet of the future, called Web3. The Blockchain technology supports a wide range of applications, spanning from Smart Cities and automotive industries, from agriculture to energy. The healthcare sector, in particular, has experienced a profound impact from blockchain-based technologies, paving the way for the development of true digital healthcare systems. By enabling secure and immutable data storage, and facilitating the sharing of this information among all nodes possessing a local copy of the distributed ledger, blockchain plays a vital role in the analysis of healthcare data. This paper provides a comprehensive survey of the main blockchain platforms utilized in the digital healthcare, integrated with a comparative analysis. In addition, the implementation of Innovative permissioned Blockchain for eHealth (IBEH) is presented and discussed in detail. IBEH addresses key challenges in digital health data management, including secure and controlled access to sensitive health information, ensuring data integrity and traceability, and secure sharing between different healthcare institutions and organizations. This is made possible by decoupling the application and blockchain layers and by a flexible, customizable, and easily deployable infrastructure. IBEH integrates the application-oriented and embedded layer with that of a blockchain network built with the MultiChain platform, which uses smart contracts with permissions, REST APIs, and RPC calls. The main features and its associated smart contracts within the healthcare domain are discussed. Finally, the analysis of performance is provided.
The rapid expansion of the digital currency market and the growing role of stablecoins as potential intermediaries have brought its interconnectedness with traditional financial markets to the forefront of global financial research. Using daily data from 4 January 2021 to 30 September 2025, this study constructs a variable system with the price indices of USDT and USDC as core digital currency proxies, alongside traditional financial asset indices for stocks, bonds, and gold derived via the entropy weight method. We employ a comprehensive set of econometric techniques, including static correlation analysis, vector autoregression (VAR), impulse response functions, and extreme-event shock tests, to systematically investigate the interdependence structure, risk spillover dynamics, time-varying co-movements, and structural changes between the two markets during extreme risk episodes. The findings reveal an overall weak and asymmetric bidirectional spillover relationship between the cryptocurrency and traditional financial markets. Volatility in the digital currency market is found to be largely endogenous, with a limited capacity to transmit shocks externally. Conversely, traditional financial markets—particularly the equity market—exert a more pronounced influence on the digital currency market. Critically, under the impact of extreme risk events, the cross-market linkages exhibit structural breaks; the direction and intensity of correlation can strengthen significantly or even reverse, demonstrating a clear state-dependency. This research provides empirical evidence for understanding the functional role of digital assets within the macro-financial system, their risk transmission pathways, and their implications for systemic financial stability. The findings offer valuable theoretical and practical insights for financial regulators in designing robust cross-market risk prevention frameworks and for investors seeking to optimize asset allocation strategies.
Dwi Fitrizal Salim, Farida Titik Kristanti, Hosam Alden Riyadh, Mailinda Tri Wahyuni
Type of the article: Research ArticleAbstractThis study evaluates and compares risk measurement models for ten major cryptocurrencies: Bitcoin, Ethereum, Tether, Ripple, Dogecoin, Cardano, Binance Coin, Polkadot, Solana, and USD Coin. Using daily log-return data from January 2017 to October 2024, the analysis applies Modified Cornish-Fisher Value-at-Risk and standard, exponential, threshold, and Markov-switching generalized autoregressive conditional heteroskedasticity models. The main comparison is conducted at the 99% confidence level, while model reliability is assessed through out-of-sample backtesting using 500 observations and the Kupiec unconditional coverage and Christoffersen conditional coverage tests. The results reveal substantial heterogeneity in cryptocurrency risk. Modified Cornish-Fisher Value-at-Risk produces highly sensitive estimates for assets with extreme skewness and kurtosis, particularly Ripple, Cardano, and Dogecoin. However, no single model performs consistently better across all assets. Bitcoin is the only cryptocurrency for which all tested models pass both backtesting procedures. The Markov-switching specification provides acceptable coverage for Bitcoin, Ripple, and Dogecoin but does not consistently outperform conventional volatility models. Standard and asymmetric volatility models provide stronger support for Cardano, Binance Coin, and Polkadot, whereas Ethereum, Solana, and USD Coin remain difficult to model under the examined specifications. These findings demonstrate that cryptocurrency risk measurement requires asset-specific model selection based on both estimated loss magnitude and formal backtesting evidence.
Short Summary - Current Internet protocols move, encrypt, authenticate, delegate, and record data—but they never answer one question: was this specific machine-generated act authorised to become real? This article proposes an execution-finality layer between computation and consequence for AI, cloud, telecom, payments, and critical infrastructure. The internet solved transport, secrecy, identity, delegation, and record-keeping. TCP/IP moves the data. TLS and HTTPS protect the channel and authenticate the endpoint. OAuth delegates access. EMV validates the payment credential. Distributed ledgers order and record the event. Every one of these remains essential. None of them answers the question that now matters most: Was the specific act represented by this data authorised to become externally effective? A packet can be delivered perfectly. A channel can be encrypted flawlessly. An endpoint can be genuine. A token can be valid. A cryptogram can verify. A transaction can be recorded. And still — none of that proves that an AI-generated command, a data export, a telecom transmission, a payment, an infrastructure change, a satellite instruction, a database write, or a physical actuation was ever authorised to cross from computation into consequence. WE BUILT OUR SAFEGUARDS FOR HUMAN TIME. MACHINES NO LONGER RUN ON IT. Earlier digital systems lived inside human reaction time. A suspicious payment could be reviewed. A wrongful disclosure could be investigated. Access could be revoked. A harmful output could be pulled down. AI-native infrastructure does not grant that luxury. A modern AI system can call tools, invoke APIs, export files, initiate payments, rewrite databases, reconfigure networks, drive machines, issue telecom commands, and trigger downstream workflows in milliseconds. By the time a log is read, the data has left the jurisdiction. The payment has settled. The command has executed. The infrastructure state has already changed. So the real problem is no longer detection. The real problem is this: Can the system stop the act from becoming effective before validation is complete? Post-event logging is evidence. Evidence is not prevention. THE LAYER THAT WAS NEVER BUILT The disclosed architecture introduces an execution-finality layer between computation and consequence. It replaces nothing. TCP/IP, TLS, HTTPS, OAuth, EMV, identity systems, policy engines, and ledgers all continue to do exactly what they do today. It adds the one technical condition none of them supply: A computational result does not become externally effective merely because a machine generated, signed, routed, or prepared it. An AI model, telecom function, cloud workload, payment system, satellite controller, application, or autonomous device may generate a proposed operation. The architecture treats that operation as a Candidate Act, held in a non-effective state. A Candidate Act may be an AI output, packet, tensor, API call, payment instruction, file export, storage write, model-memory update, telecom transmission, rendering event, actuator command, or any other consequential operation. Before that act can become real, a protected hardware or cryptographically isolated domain validates the required conditions — which may include authority, purpose, consent, jurisdiction, destination, revocation status, policy epoch, runtime integrity, freshness, quota, protected state, and the identity of the intended effectuation boundary. Only on success is protected evidence committed and a narrowly scoped, non-bearer capability released — bound to that particular act, scope, protected state, evidence, destination, and applicable Finality Sink. THE FINALITY SINK: WHERE COMPUTATION BECOMES CONSEQUENCE The Finality Sink is the precise point at which an act would first become externally effective — a model-output emitter, API dispatcher, telecom gateway, radio chain, SmartNIC, DPU, payment terminal, ledger bridge, memory controller, storage writer, renderer, satellite-command interface, or physical actuator. The Finality Sink verifies the capability before permitting release. Verification fails → the act remains non-effective. Verification succeeds → the capability is consumed before or atomically with effectuation, reducing replay, substitution, duplicate execution, and cross-sink misuse. WHY THIS IS NOT "BETTER SECURITY" Conventional systems place checks around an execution path. The application, model server, network function, or payment system typically retains the technical ability to complete the act anyway. This architecture removes that ability. The ordinary compute environment may calculate or prepare the act — but it does not independently hold the final authority to make the act effective. Authority is separated from computation, and verified again at the consequence boundary. Stated in one line each: Layer Question it answers TCP/IP How is information transported? TLS / HTTPS Is the channel protected? OAuth Who may delegate access? EMV Is the payment credential valid? Ledgers What happened, and in what order? Execution Finality May this specific act become real? The contribution is not another policy engine, authentication scheme, audit system, or cryptographic token. It is a structural dependency: protected validation becomes a technical precondition of effectuation. ONE GAP. EVERY INDUSTRY. The computation-to-consequence gap is not an AI problem. It is an infrastructure problem that appears wherever machines act faster than institutions can respond. Artificial intelligence — model outputs, tool calls, agent actions, code execution, data exports, memory writes, retrieval operations, autonomous workflows. Telecommunications and 5G/6G — packet forwarding, network slicing, roaming, radio emission, gateway egress, satellite communications, non-terrestrial networks, machine-to-machine commands. Cloud and data-centre infrastructure — CPUs, GPUs, AI accelerators, memory controllers, DMA engines, SmartNICs, DPUs, storage controllers, accelerator-interconnect boundaries. Financial systems — payment finality, account transfers, settlement, digital assets, CBDCs, ledger commitments, trading instructions. And beyond — data sovereignty, cross-border data use, industrial control, robotics, vehicles, healthcare infrastructure, energy systems, digital twins, content publication, cybersecurity response, critical infrastructure. Critically, the architecture supports jurisdictional and enterprise control without blanket data localisation and without duplicating national infrastructure. Computation may remain distributed and interoperable; only the authority to produce an external consequence stays protected. 8,598 PAGES. YOU ONLY NEED THREE STEPS. Readers are not expected to work through the specification sequentially. 1. Start with the short invention summary.It covers the Candidate Act, non-effective state, Protected Enforcement Domain, validation evidence, scoped capability, Finality Sink, the difference from conventional systems, the novelty position, and industrial applicability. 2. Download the navigation file.It explains the common inventive concept and routes you to the industry-specific embodiments relevant to AI, telecom, satellites, payments, cloud infrastructure, or cybersecurity. The industry mapping sits at approximately pages 57–61 of the main disclosure. 3. Download the main specification — and go straight to your embodiment.The length reflects the number of implementation environments, effectuation boundaries, hardware arrangements, failure states, and anti-bypass variants. It is not one example repeated 8,598 times. THE ONE SENTENCE THAT HOLDS THROUGHOUT A machine may compute, prepare, or propose an act — but computation alone does not create the authority to make that act externally effective
Arif Billah Dar, Isha Kumari, Safika Praveen Sheikh
Abstract The financialization of gold is investigated by analyzing the dynamic return spillovers between traditional, equity-based, exchange-traded and blockchain-based gold investment means. The empirical system includes physical gold, the SPDR Gold Shares ETF (GLD), VanEck Gold Miners ETF (GDX), NYSE Arca Gold BUGS Index (HUI), First Trust Gold Miners ETF (FTGM), PAX Gold (PAXG), and Tether Gold (XAUT). The study employs the Time-Varying Parameter Vector Autoregressive connectedness framework with generalized forecast error variance decomposition to estimate the magnitude, direction and evolution of shock transmission. The findings show that the financial economy around gold is quite tightly connected, with an average TCI of 81.13%, meaning that most of the forecast error variance of the system is attributable to cross-market shocks. GLD is the biggest net shock transmitter followed by GDX and XAUT, while the biggest net receivers are physical gold and PAXG. The results indicate that the price discovery and information transmission have been shifting from the underlying physical bullion market to exchange-traded markets and digital gold markets. The dynamic analysis also reveals that connectedness remains high but fluctuates throughout the sample, whereas PAXG gradually evolves from a net receiver to a net transmitter in the later part of the sample. Using the alternate Quantile connectedness methodology, we also find that the results are robust. Overall, it is clear that the results reflect the gradual transformation of gold from a commodity asset to a highly networked financial asset. The results are relevant to portfolio diversification, hedging, market monitoring and regulation of new gold products.
Quantum computers pose a fundamental threat to blockchain systems that rely on elliptic-curve cryptography. This work reviews the quantum vulnerabilities and associated economic risks of major blockchain platforms, with a focus on Bitcoin, Ethereum, and Solana. We distinguish between at-rest, on-spend, and on-setup attacks and identify the blockchain components most exposed to quantum adversaries. We further review practical migration strategies toward post-quantum security, including NIST-standardized digital signatures and emerging solutions for Solana, Algorand, and Ethereum.
Blessing Emmanuel Oladele, Adekunle Olugbenga Ejidokun, Chukwuemeka O. Agwu
Academic credential verification remains difficult for institutions because manual checks are slow, fragmented, and vulnerable to fraud. Blockchain can improve trust by anchoring credential proofs, but repeated verification requests and growing off-chain repositories can still create performance bottlenecks. This study presents an adaptive blockchain-powered academic credential repository that combines off-chain MySQL storage, Solidity-based hash anchoring, Redis verification caching, and metadata-aware sharding. Full academic records are not stored on-chain or in Redis; only credential hashes, verification responses, and related metadata are used for trust validation and performance optimization. A CodeIgniter 4 prototype was evaluated using synthetic academic credential records and controlled workloads of 1,000, 5,000, and 10,000 verification requests under fresh, mixed, and repeated access patterns. The results show that Redis caching substantially reduced repeated blockchain queries, especially under mixed and repeated workloads, while metadata-aware sharding improved repository organization and supported more targeted credential retrieval. Sepolia testnet validation confirmed smart-contract feasibility, including issuance, verification, revocation, gas use, confirmation time, and event evidence, but was treated separately from scalability testing. The findings indicate that combining blockchain trust anchoring with cache-aware verification and metadata-based repository partitioning can improve the scalability of academic credential repositories, provided that cache consistency, revocation handling, and deployment limitations are carefully managed.
Adoption of peer-to-peer (P2P) trading is very challenging, mainly due to numerous issues and limitations such as lack of trust in the concept and awareness of the technical, economic and social benefits. This paper aims to understand how blockchain technology can address the current issues/limitations of P2P distributed solar energy (DSE) trading. A series of semi-structured interviews were conducted with 23 community energy stakeholders to confirm and expand the stakeholder issues identified in the literature review. A case representing community energy projects was selected to (1) develop and implement a blockchain system and (2) evaluate its ability to eliminate (or reduce) stakeholder issues and meet stakeholder expectations. A blockchain-enabled P2P trading platform was developed using an Ethereum backend. The system clearly demonstrated its ability to deliver full or partial solutions to 12 stakeholder issues. Two stakeholder issues are unable to be addressed via the blockchain platform since they uncovered the weaknesses of blockchain technology. The P2P trading platform has also demonstrated its ability to facilitate decentralized trading and data management. The outcome of this study indicates the areas of P2P trading projects that can be improved by the application of blockchain technology.
Automated anti-money laundering (AML) on public blockchains is usually framed as a detection problem. Because the ledger is public and permanent, automated detection is feasible, but that record shows only that value moved, without showing who moved it or why. We review automated blockchain anti-money laundering as a system in which machine models and human analysts share each decision, following it through four stages: detection, attribution, adjudication, and reporting, and asking at each stage what automation does well, what it must leave to human judgment, and what goes wrong when that judgment is misplaced. The evidence shows a consistent asymmetry. Machine learning is strong at pattern-finding over the permanent public record, where models rank suspicion and clustering heuristics scale, but it weakens sharply as the task turns from finding a pattern to assigning meaning, identity, intent, or accountability. Drawing first on emerging AML-specific studies and then, where direct evidence remains insufficient, on human-factors research from adjacent high-stakes domains, we find an uneven evidence base. Automation is best supported for large-scale detection, alert prioritization, and parts of blockchain attribution, while the evidence becomes more limited as decisions require contextual interpretation, evidential judgment, accountability, and reporting. AML-specific studies identify explainability, flexibility, tool integration, supervisory justification, and human review as important operational requirements, but they do not yet establish how frequently analysts over-rely on, reject, or selectively follow automated recommendations. Evidence from aviation, healthcare, and public administration is therefore used to identify plausible failure mechanisms rather than to claim AML-specific effects. The result is an evidence-weighted allocation matrix that records, for each stage, the strength of the case for automation, the function retained by the analyst, the dominant failure mode, and the empirical question that remains unresolved. Support is strongest for detection, direct but context-dependent for attribution, and more provisional for adjudication and reporting.
Sunday Yunisa, A. Ajah Ifeyinwa, Eturpa Salami Emmanuel
Internet of Medical Things (IoMT) devices, due to their resource constraints, require specialized security solutions that can operate efficiently without compromising performance and maintaining data confidentiality and integrity while minimizing computational overhead. This review examines various IoMT-based security frameworks designed to secure healthcare records in the cloud, emphasizing their effectiveness, challenges, and best practices. The study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. 100 studies were identified published between 2020to 2025 and 50 papersthat were relevant to the study was carefully selected through a structured screening process. The papers were obtained from major academic databases such as PubMed, Scopus, IEEE Xplore, SpringerLink, Wiley, and Google Scholar. A systematic review protocol was developed before the literature search to establish clear criteria for inclusion and exclusion, ensuring transparency and reproducibility. The review showed that despite the progress made in safeguarding IoMT cloud-based health records, numerous prevailing frameworks predominantly emphasize either encryption or blockchain technology in a singular context, thereby neglecting to exploit the synergistic advantages inherent in the integration of both methodologies. Also, the encryption techniques currently employed for the protection of records within IoMT cloud environments frequently fail to achieve the essential equilibrium between security and operational performance which is characterized by limited resources. The study recommends the formulation of a framework that integrates several encryption schemes and blockchain technology to address the prevailing security problems.
Purpose This study investigates to investigate the main determinants of financial service industry adoption blockchain technology and their impact on financial transparency and efficiency. In particular, the paper looks at how technological readiness, organizational control and supporting regulations enable blockchain use among financial institutions working in the banks of Jordanian banking sector. Design/methodology/approach A quantitative research design based on a structured questionnaire was employed, which was distributed throughout Jordanian banks to senior and middle-level management. Responses were collected from senior executives, heads of divisions, IT managers and branch managers who are involved in both the financial and technological decision-making processes. SmartPLS was used to conduct Partial Least Squares Structure Equation Modeling (PLS-SEM) analysis using SmartPLS on 192 valid responses to check the measurement model and test the theorized relationships in proposed research framework. Results Results show that technological readiness, organizational governance and regulatory support play an important role in encouraging blockchain technology uptake by financial services. Furthermore, financial institutions adopting blockchain update general ledger journal records in an interactive way which is positively associated with the degree to which financial transparency and operational efficiency are attained. This underscores the part played by technological capabilities as well as governance frameworks and regulatory contexts in encouraging successful blockchain adoption while helping to improve institutional performance. Originality/value In articulating a new frame of analysis from a TOE perspective that takes into account technological, organizational, and environmental factors together, the paper contributes to an emerging literature on adoption of blockchain by financial services. Moreover, it carries out an empirical examination of the performance effects associated with introducing blockchain technology into the banks of Jordan the country which is focused on enhancing financial transparency and operational efficiency. JEL classification G32; K22; O16; L60.
Accurate carbon footprint accounting is fundamental for urban environmental governance. However, multi-stakeholder transit networks struggle with data manipulation, privacy risks, and labor-intensive manual auditing. To resolve these trust and scalability bottlenecks, this paper introduces a tri-layer hybrid blockchain framework based on an “off-chain storage, on-chain evidence” paradigm. The architecture synergizes a relational database (MySQL) for high-throughput structured data, the InterPlanetary File System (IPFS) for decentralized raw evidence, and Hyperledger Fabric to immutably anchor dual-layer cryptographic hashes. We engineer a smart contract auditing pipeline that autonomously executes deterministic verification of hash consistency, emission thresholds, and physical logic integrity. Empirical evaluations utilizing a large-scale urban transit dataset injected with adversarial mutations demonstrate high robustness, achieving F1-scores of 1.000 across multidimensional anomalies. This replaces manual testing with statistically significant verification. Ultimately, this framework provides environmental regulators and transit authorities with a highly scalable, privacy-preserving, and trust-minimized infrastructure for continuous carbon footprint traceability.
Homomorphic-encryption blockchain frameworks for IoT sensor aggregation generally rely on classical cryptographic hardness assumptions and seldom account for network topology in liveness and performance analysis. This work introduces Phi-PHE-BC, a topology-aware homomorphic blockchain architecture for secure and privacy-preserving IoT sensor data aggregation. The framework combines threshold Paillier decryption with graph-parameterized security and performance analysis, linking protocol behavior to the validator graph. On-chain Paillier ciphertexts support homomorphic aggregation while providing IND-CPA confidentiality under the Decisional Composite Residuosity assumption, and authentication signatures provide EUF-CMA transaction integrity. Threshold partial-decryption shares are protected by a noise-flooding wrapper that provides information-theoretic privacy under the configured statistical-hiding condition. Under partial synchrony and Byzantine fault-tolerance assumptions, liveness requires validator connectivity kappa(Gv) >= f+1. We derive topology-dependent throughput bounds for tree, star, mesh, and scale-free networks, together with a per-block communication-cost model. A game-theoretic analysis shows that honest validator participation is a dominant strategy under the stated utility model, yielding an all-honest Nash equilibrium. Experiments on Hyperledger Fabric 2.5 show lower end-to-end latency than the selected traditional PHE-blockchain baseline while maintaining controllable threshold-decryption overhead. Results across topology scaling, validator sensitivity, threshold decryption, and Byzantine-load experiments indicate that Phi-PHE-BC is a practical architecture for secure, privacy-preserving, and topology-aware IoT sensor aggregation.
We present an empirical performance evaluation of SILM, a national-scale music-royalty administration platform prototyped for LMKN, Indonesia's collective rights management agency, implemented as a chain of ten event-driven Go microservices connected through an in-memory publish/subscribe bus (Apache Kafka in the production blueprint). The study contributes a Dapper-style trace-per-event instrumentation yielding per-stage latency distributions, a five-point throughput sweep from 100 to 10,000 play events used to locate the operating point and the degradation knee, and a money-conservation and correctness suite. At every tested burst scale up to 10,000 events the pipeline delivers 100\% event delivery and exact money conservation, while median end-to-end (E2E) latency grows approximately 17-fold (0.81 s at 100 events to 13.7 s at 10,000 events); per-stage spans attribute 85.0\% of E2E average latency at the largest scale to a single cross-service queueing stage. A 30-second sustained-load soak at approximately 1,042 events/s exposes the single-consumer ceiling: the bounded subscriber queue overflows in its tail, dropping 7,097 of 31,255 submitted plays (22.7\%), the first measured reliability failure of the platform. All findings are compared against recent published results on tail latency, bottleneck attribution, and channel sizing in event-driven architectures.
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Mrs. A. Asrin Mahmootha, B. Aysha Banu, Mohammed Muhajir S, Mohamed Anas A · 5 authors
Voting is a central component of a country’s political life cycle. Privacy, authentication, and integrity of citizens’ votes are essential requirements of any electronic voting programme. To address these concerns, this paper proposes a hybrid e-voting system that integrates personal and public blockchain with a machine learning–based intrusion detection mechanism. The personal blockchain governs voter registration and vote casting, while the public blockchain stores the Merkle root hash for result integrity verification. An ML-based intrusion detection system monitors voting data centres and e-voting stations for anomalous behaviour. Homomorphic encryption and zero-knowledge proofs preserve voter anonymity. Experimental evaluation demonstrates that the proposed framework achieves an accuracy of 97.4%, precision of 96.8%, recall of 97.1%, and F1-score of 96.9% in detecting intrusion attempts. The system also reduces transaction latency by 34% compared to conventional blockchain voting systems. Results confirm that the framework delivers transparency, tamper-resistance, and strong security guarantees, making it a viable solution for modern democratic elections
Mr. Harshal Kadam, Mr. Mayur Prajapati, Mr. Amit Yadav, Prof. Sonali Karthik
ConQuote Connect is a smart digital platform designed to solve common problems in the construction industry, such as unclear project details, payment delays, miscommunication and the difficulty of finding trustworthy contractors. It creates a single, streamlined space where builders can post their construction projects and contractors can submit structured and easy to compare quotations. A key part of the system is the use of Building Information Modeling (BIM), which allows builders to upload 3D models of their projects. These models help both parties clearly understand the scope of work and visually track progress through milestones, such as marking when the foundation, floors, or roofing are completed. To make payments more secure, transparent, and fair, ConQuote Connect uses blockchain-powered smart contracts. These contracts safely hold project funds and only release payments when a builder confirms that a milestone has been completed through the BIM model. The platform also includes AI tools that assist in comparing contractor quotes and helping builders make faster, more informed and data backed decisions. When a contractor successfully completes a project, they receive a digital certificate in the form of an NFT, which becomes part of their verifiable reputation and track record on the platform. Both builders and contractors have their own personalized dashboards to manage tasks, communicate updates, track progress and approve or verify completed work. By combining BIM, blockchain and AI in one easy to use system, ConQuote Connect offers a modern, transparent and trustworthy way to manage construction projects reducing disputes, saving time and improving industry collaboration.
Adaeze Linus Miracle, Moniaye Ayadi, Dr. J. O. Omokehinde
Blockchain technology has emerged as one of the most transformative digital innovations with the potential to improve transparency, security, and operational efficiency in accounting and auditing. Despite these potential benefits, adoption among accounting firms in developing economies remains limited due to concerns regarding implementation costs, technological complexity, and regulatory uncertainty. This study examines the cost–benefit dynamics of blockchain adoption among accounting firms in Nigeria by investigating the influence of implementation costs and operational benefits on blockchain adoption and evaluating the effect of blockchain adoption on operational performance. A cross-sectional survey research design was adopted, and primary data were obtained from 220 accounting professionals drawn from accounting and auditing firms in Lagos State, Nigeria. Descriptive statistics, Pearson correlation, and linear regression techniques were employed to analyse the data. The findings indicate that implementation costs significantly reduce the likelihood of blockchain adoption, whereas perceived operational benefits significantly enhance adoption. Furthermore, blockchain adoption exerts a positive and statistically significant effect on the operational performance of accounting firms through improvements in reporting accuracy, transparency, operational efficiency, and client confidence. The findings support the Technology Acceptance Model and Transaction Cost Economics by demonstrating that organisations evaluate emerging technologies based on the balance between expected benefits and associated implementation costs. The study concludes that although blockchain implementation requires substantial initial investment in infrastructure, integration, training, and regulatory compliance, its long-term operational benefits outweigh these costs. The study recommends phased implementation strategies, investment in digital competencies, and the development of supportive regulatory frameworks to accelerate blockchain adoption within the Nigerian accounting profession.
The Moroccan Ministry of Justice aims to enhance the process of the judicial system. Through digitalization, given the sensitive information and the complexity of managing this volume of data, along with the multiple electronic materials exchanged, several challenges regarding the security, integrity, and confidentiality of personal data are presented that indicate difficulties in confirming authenticity. Using blockchain technology, the Ministry of Justice can exchange data and knowledge in a secure and transparent way. The goal of the proposed method is to automate the procedure for generating birth certificates to strengthen trust, security, and operational efficiency within the Moroccan judicial system.
Background. Technical analysis remains among the most accessible forms of market decision support, but its incremental predictive value depends on design choices, and repeated model selection can create spurious backtest performance. Objective. This study evaluates whether widely used technical indicators, reconstructed from public formulas, can predict the next daily price direction of Bitcoin, the S&P 500, and gold. Methods. Daily data from 1 January 2015 to 14 July 2026 are examined using a chronological walk-forward design. Every signal observed at the close of day t is matched only with the sign of the close-to-close return from t to t+1. The main out-of-sample period begins in 2019, with 2019–2022 used for model selection and 2023–2026 reserved for confirmation. Performance is measured primarily by balanced accuracy and supplemented by accuracy, directional recall, stationary-bootstrap confidence intervals, and after-cost trading outcomes. Results. The best single indicator, Ichimoku 9/26/52, produced a macro balanced accuracy of 50.9%, while the best predetermined combination, Volume Confirmed, reached 50.8%. A new ridge-logistic hybrid indicator, SPAH-1, achieved 51.9% in validation and 51.3% in confirmation. Its confirmation balanced accuracy was 49.8% for Bitcoin, 49.2% for the S&P 500 proxy, and 55.0% for gold; only gold's bootstrap interval excluded 50%, but its predictions were strongly biased toward the upward class. After trading costs, Volume Confirmed underperformed buy-and-hold for all three assets. Additional selective experiments did not support an 80% daily prediction target. Conclusion. Technical indicators may assist regime description and decision confirmation, but they do not provide a robust universal next-day forecasting edge.
Cryptocurrency began as a marginal experiment among cypherpunk technologists and has since become an asset class worth trillions of dollars globally, and its rise has been matched by a parallel rise in criminal activity that feeds off the pseudonymity, decentralisation and borderless movement these assets allow. India counts among the countries with the largest populations of crypto holders anywhere, yet Parliament has still not enacted any statute dedicated to virtual digital assets; oversight instead comes piecemeal, through tax provisions, anti-money-laundering notifications and rulings handed down case by case. This article sets out the scale of crypto-related crime worldwide, reviews the current statistics, and examines the jurisdictional and enforcement difficulties that follow from them, with particular attention to how those difficulties play out inside India. India's liability framework is then measured against a handful of foreign models, the Indian judiciary's engagement with the resulting legal gaps is assessed against international standards and model policy approaches, and the article closes by setting out a proposed regulatory framework.
Harini D., S. Aruna, Santhanalakshmi V., D. P. Sivasakti Balan · 5 authors
Cryptocurrencies have emerged as a significant component of the global financial systems, attracting considerable attentions from investors, researchers and policymakers. Simultaneously, Indian sectoral stock market plays a crucial role in the country’s economic development and investment landscape. Due to their high volatility, crypto currency markets may influence traditional financial markets and investment decisions .This study investigates the impact of crypto currency market movements from on selected Indian sectoral stock markets, including the information Technology, Banking, Pharmaceutical sectors. Historical data from major cryptocurrencies and sectoral stock indices like Bitcoin, Ethereum, Nifty IT, Nifty Bank, and Nifty Pharma indices which was collected and analyzed using explainable regression techniques. Based on the given datasets, the findings provide valuable insights into the relationship between cryptocurrency markets and selected Indian sectoral stock market indices, particularly in understanding how crypto currency market fluctuations during major global events, such as pandemics, geopolitical conflicts, and economic uncertainty, may influence investor behavior and sectoral performance across the Indian economy.