The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyberâphysical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities.
Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning.
Smart grids are a modern model for developing electric power infrastructure based on the integration of information and communication technologies and intelligent control systems. These networks enable the creation of a highly efficient, reliable, and adaptive energy environment capable of quickly responding to changes in electricity generation and consumption patterns. Key principles of a smart grid include adaptive load management, two-way data exchange between power system elements, the integration of distributed energy resources, and the use of modern digital technologies, including the Internet of Things, artificial intelligence, and distributed ledger technologies. The implementation of smart grids optimizes the generation, transmission, and distribution of electricity, improves the reliability and sustainability of the power system, and develops effective consumer interaction mechanisms based on intelligent energy management and dynamic pricing.
The Fifth Industrial Revolution (Industry 5.0) foregrounds humanâmachine collaboration, sustainability, and resilience as organizing principles for next-generation cyber-physical systems. Yet the identity and access management (IAM) architectures inherited from Industry 4.0 remain perimeter-centric, policy-static, and blind to the behavioral dynamics of humanâAI teaming. This paper introduces the Human-Centric Zero Trust Identity Architecture (HC-ZTIA), a novel framework that repositions identity as the adaptive control plane for Industry 5.0 environments. HC-ZTIA integrates three mutually reinforcing innovations: (1) a Joint Embedding Predictive Architecture (JEPA)-driven Behavioral Identity Assurance Engine (BIAE) that learns abstract world models of operator and machine-agent behavior to perform continuous, context-aware identity verification without relying on raw biometric surveillance; (2) a Privacy-Preserving Adaptive Authorization Protocol (PP-AAP) employing zero-knowledge proofs and federated policy evaluation to enforce least-privilege access across human, non-human, and hybrid identity classes while satisfying data-minimization mandates; and (3) a Resilience-Oriented Trust Degradation Model (RO-TDM) that guarantees fail-safe identity governance under adversarial, degraded, or disconnected operating conditions characteristic of operational technology (OT) and critical infrastructure. The framework is grounded in the Agile-Infused Design Science Research Methodology (A-DSRM) and formally extends NIST SP 800-207 and the CISA Zero Trust Maturity Model by addressing five identified gaps in human-centric identity governance. We present the formal system model, threat model, architectural specification, and a multi-scenario evaluation spanning energy-sector OT, smart manufacturing, and vehicle-to-everything (V2X) environments. Simulation results, validated through Monte Carlo trials with 95% confidence intervals, demonstrate that HC-ZTIA reduces identity-related breach exposure by 73.2% (Âą4.1%) while maintaining sub-200 ms authorization latency, offering a principled bridge between Zero Trust rigor and Industry 5.0 human-centricity.
Background: Blockchain and Multi-Agent System (MAS) are increasingly combined to support decentralized, secure, and autonomous peer-to-peer energy trading in microgrid environments. Objectives: This systematic review investigates how blockchain and MAS are integrated to support microgrid energy trading, identifies architectural and operational models, examines real-world implementations, and highlights technical, regulatory, and security challenges. Unlike prior reviews that focus on blockchain or MAS in isolation, this study provides a unified and comparative analysis of their joint integration. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in IEEE Xplore, ACM Digital Library, and ScienceDirect, with the last search performed on 10 January 2025. Eligible studies focused on blockchainâMAS integration in microgrid energy trading; non-energy and non-microgrid applications were excluded. Study selection was performed independently by two reviewers, and methodological quality was assessed using an adapted Joanna Briggs Institute (JBI) checklist. A narrative synthesis categorized integration levels, blockchain platforms, MAS roles, and implementation contexts. Results: A total of 104 studies were included. Three dominant integration levels were identifiedâbasic, intermediate, and advancedâdistinguished by how decision-making responsibilities are distributed between MAS and smart contracts. Ethereum and Hyperledger Fabric were the most commonly used platforms. MAS agents perform concrete operational functions such as bid and offer generation, price negotiation, matching, and local energy optimization, fundamentally transforming control and monitoring processes. By enabling distributed, intelligent agents to perform real-time sensing, analysis, and response, an MAS enhances system resilience and adaptability. This architecture allows for proactive fault detection, dynamic resource allocation, and coherent, large-scale operations without centralized bottlenecks. Blockchain ensured transparency, trust, and secure transaction execution. Major challenges include scalability constraints, interoperability limitations with legacy grids, regulatory uncertainty, and real-time performance issues. Limitations: Most included studies were simulation-based, with limited real-world deployment and substantial heterogeneity in evaluation metrics. Conclusions: BlockchainâMAS integration shows strong potential for secure, transparent, and decentralized microgrid energy trading. Addressing scalability, regulatory frameworks, and interoperability is essential for large-scale adoption. Future research should emphasize real-world validation, standardized integration architectures, and AI-enabled MAS optimization. Funding: No external funding. Registration: This systematic review was not registered.
Abstract Non-Fungible Tokens (NFTs) are blockchain-based digital assets that provide verifiable proof of ownership and authenticity. Despite their rapid proliferation, NFT markets face ongoing challenges related to user trust, legal ambiguity, and sustainable technological integration. We offer a comprehensive hybrid review by combining bibliometric and systematic approaches of 190 peer-reviewed NFT-related articles published since 2022. Through structured keyword mining, abstract-level thematic classification, and co-occurrence network visualization, we trace the intellectual evolution of NFT research across disciplines and time. Our analysis spans 119 journals and identifies six major thematic clusters: User and Market Dynamics Legal and Ethical Considerations Blockchain and NFT Technology Applications and Use Cases Digital Transformation and Innovation and Challenges and Issues . Temporal keyword trends reveal a progression from foundational blockchain infrastructure to user adoption and experiential design, toward regulatory integration, metaverse ecosystems, and industry-specific deployments in the recent years. Network visualizations highlight converging interests in topics such as decentralized identity, interoperability, and sustainability. In addition to mapping existing knowledge, this review identifies critical research gaps in areas such as regulatory frameworks, long-term infrastructure design, socioeconomic inclusion, and trust verification mechanisms. These findings offer a forward-looking research agenda centered on standardization, interdisciplinary integration, and user-centric innovation, paving the way for a more resilient and inclusive NFT ecosystem.
Jay BojiÄ Burgos, Urban Sedlar, MatevĹž PustiĹĄek
Integrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without sharing representative evaluation data with vendors, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. This paper introduces a framework combining Zero-Knowledge Proofs (ZKPs) with smart contracts to enable trust-minimized, cryptographically verifiable competitive model procurement in Industrial Cyber-Physical Systems (ICPS). Vendors cryptographically prove that their model outperforms a legacy baseline without disclosing proprietary weights, a process we term cryptographic performance attestation, while the on-chain workflow automates escrow, proof verification, and best-vendor selection with arbiter-based dispute resolution. ZKP privacy is scoped to vendor model weights; operator-side evaluation-data confidentiality is managed separately via synthetic, de-identified, or public benchmark data. We analyze three ZKP workflow variations and evaluate them on consumer-grade hardware, achieving proving times of approximately three seconds and sub-dollar on-chain verification costs under Layer-2 fee assumptions for the recommended single-proof variation, while identifying computational trade-offs of recursive proof aggregation. The entire verification phase operates offline with no impact on real-time OT control paths, bridging the IT/OT pre-transaction trust gap while deferring artifact deployment to existing OT tooling.
Smart grids require real-time ancillary services from large-scale distributed energy storage (DES), creating a conflict between second-scale physical response needs and the slow confirmation of trust mechanisms like blockchain. Traditional VPPs lack scalability and trust for massive participation, while decentralized approaches struggle with mismatched time scales. We propose a framework that decouples real-time dispatch from asynchronous settlement. An off-chain matcher uses a physics-aware model, including a novel âservice holding timeâ (Tservice) constraint and power (kW) envelopes, for fast assignments. A separate on-chain proof-of-stake (PoS) layer handles incentives and penalties (slashing) asynchronously. We formulate the MILP dispatch problem and provide a fast online heuristic alongside a MINLP decomposition benchmark. Co-simulations (IEEE 33-node) show that our scheme significantly outperforms baselines in success rate and latency, is robust against non-compliant nodes due to the PoS mechanism, and thereby offers a scalable and trustworthy solution.
Hamza Ibrahim, Love Allen Chijioke Ahakonye, Jae-Min Lee, D. Kim
The Industrial Internet of Things (IIoT) has transformed critical infrastructure but has also introduced severe security vulnerabilities, with breaches capable of causing catastrophic physical and operational damage. While blockchain technology offers a promising foundation for tamper-proof logging, existing platforms are often ill-suited for IIoT due to high latency, low throughput, and excessive energy consumption. Furthermore, most current research treats intrusion detection, secure logging, and system recovery as isolated components, lacking a unified framework for autonomous, verifiable resilience. To bridge this critical gap, this paper introduces PureChain, a holistic, secure, and resilient ecosystem. PureChain integrates a custom lightweight blockchain with a deep learning-based intrusion detection system and a novel verifiable recovery protocol, creating a closed-loop security model. The framework leverages a novel Proof of Authority and Association (PoA2) consensus mechanism, achieving high throughput (16.82 TPS), low latency (0.0594 s), and minimal energy consumption (12.43 W), demonstrating suitability for resource-constrained IIoT environments compared to general-purpose platforms like Ethereum and Hyperledger which are optimized for different use cases. Upon intrusion detection by optimized models like XGBoost (99.87% accuracy), immutable blockchain logs actively trigger and cryptographically attest to infrastructure-enforced recovery actions such as device isolation via SDN switches or state rollback through hardware management controllers. Extensive evaluation on benchmark IIoT datasets (IoT-CAD and IoTForge) demonstrates a detection-to-recovery success rate of up to 98.59% while maintaining 100% data integrity. PureChain establishes a new paradigm that unifies real-time threat intelligence, blockchain-based trust, and provable autonomous recovery for next-generation IIoT security.
Tsvetelina Ivanova, L Koleva, Idilia Batchkova, G Kolev
Abstract Reliable vacuum control in high-precision installations such as the Electron Beam Melting and Refining (EBMR) plant requires an intelligent architecture that integrates physical subsystems and cyber entities under an adaptive control framework. This paper proposes a multi-agent system (MAS) representation of the EBMR vacuum creation subsystem, developed using the Organizational Multi-Agent Systems Engineering (O-MaSE) methodology. The model unites the IEC 61512 (S88) batch-process standard with the IEC 61499 distributed-control architecture to form a modular and interoperable cyber-physical system (CPS). Each pump, valve, and sensor is modelled as an autonomous agent with defined goals, roles, and communication protocols. The O-MaSE-based design enhances scalability, fault tolerance, and system adaptability, enabling decentralized decision-making and efficient vacuum regulation. The integrated case study demonstrates that MAS-based CPS design substantially improves the responsiveness and resilience of EBMR operations, supporting the principles of Industry 4.0.
The increasing deployment of Agricultural Internet of Things (Ag-IoT) systems is transforming food production and enabling climate-resilient farming practices.However, the growing reliance on interconnected sensing, automation, and cloud platforms significantly expands the attack surface, exposing agricultural operations to cyber threats that can disrupt critical processes, compromise data integrity, and undermine food security.This paper explores adaptive cybersecurity mechanisms designed to enhance the resilience of Ag-IoT ecosystems operating under climate-induced environmental and network constraints.The proposed approach integrates context-aware access control, federated threat learning, zero-trust architectures, and distributed ledger technologies to secure dataflows, device interactions, and supply-chain processes.Experimental evaluations and simulated farm scenarios demonstrate improved attack detection, operational continuity, and system reliability during extreme weather events and adversarial conditions.The results suggest that adaptive cybersecurity strategies are essential for protecting next-generation digital agriculture and ensuring resilient, secure, and sustainable food systems in an era of accelerating climate variability.
With the increase in the rate of cyber threats, such as ransomware, social engineering, and zero-day exploits, it is urgent to adopt new security mechanisms like Security Orchestration, Automation, and Response (SOAR) systems. The increase in cyber threats has not only amplified in frequency but also in sophistication. This escalation has forced organizations to rethink traditional defense strategies. SOAR has shown itself to be an important solution by automating repetitive tasks and helping security teams in focusing on strategic threat hunting as well as mitigation. The integration of AI and ML in SOAR frameworks helps in predictive analytics, in which systems can anticipate potential breaches based on pattern recognition from vast datasets. The role of blockchain is to enhance data integrity and help enable secure and decentralized threat intelligence sharing between stakeholders. This paper presents a systematic literature review (SLR) on recent advancements in SOAR technologies, especially the incorporation of artificial intelligence (AI), machine learning (ML), and blockchain; it also reviews case studies across various industry sectors, such as healthcare, finance, industrial control systems, and critical infrastructures, as well as the challenges facing SOAR adoption. By examining 29 studies from academic research, industry case studies, and technical reports, the review synthesizes methodologies, architectures, and performance outcomes to summarize the current state of SOAR systems. The research found that SOAR can significantly reduce incident response times and improve threat detection accuracy, with findings indicating that SOAR can lower response times by up to 80% compared to legacy systems, although implementation costs may reach as high as $5 million. Additionally, specialized personnel are still needed to operate these systems. The skills gap increases barriers to adoption, as few professionals possess expertise in cybersecurity as well as in automation tools. Future directions emphasize developing hybrid models that blend human intuition with machine efficiency for more robust defenses. Finally, the review discusses future research directions to help SOAR further scale, interoperate across platforms, and enable autonomous decision-making
Slow Liquidity Drain (SLID) scams have recently emerged as a subtle and persistent threat within the decentralized finance (DeFi) environment. While prior studies have introduced heuristic and machine learning techniques for identifying SLID behaviors, deploying these methods in real-world industrial systems reveals substantial challenges. In particular, updated large-scale datasets collected from operational DeFi platforms show that SLID behaviors and their effective detection time-range evolve over time, rendering previously reported fixed thresholds unreliable for production use. This work presents a data-driven reassessment of SLID detection under contemporary DeFi conditions and demonstrates that the observation window required for reliable detection shifts as new data and new scam behaviors emerge. Building on these findings, we introduce an industry-oriented detection framework that decouples machine learning models from time-range selection and supports adaptive operation without retraining or feature redesign. Rather than proposing a single deployment strategy, we outline two practical operating modes: a slow-adaptive mode that prioritizes stability and auditability through periodic window updates, and a fast-adaptive mode that enables flexible sensitivity and tiered alerts for security-driven environments. Together, these designs translate empirical insights into concrete system architectures suitable for large-scale DeFi monitoring, bridging the gap between academic SLID detection research and production deployment requirements.
As artificial intelligence (AI) systems grow more powerful, autonomous, and embedded in critical infrastructure, their identification and traceability become foundational to regulatory oversight and sustainable digital governance. In digitally transformed enterprises, long-term sustainability depends on transparent, accountable, and lifecycle-governed AI systems, all of which require verifiable identity. This study proposes a conceptual and architectural framework for AI identification, combining technical and governance mechanisms to support lifecycle accountability. The framework integrates five components: model fingerprinting, cryptographic hashing, blockchain-based registration, zero-knowledge proof (ZKP)-based proof of possession, and post-deployment structural change screening. We introduce a dual-layer identifier, consisting of a machine-verifiable primary hash and a human-readable secondary identifier, anchored in a tamper-resistant registry. Identity validation is supported by selective ZKP-based verification at governance-defined checkpoints, while post-deployment changes are monitored using Lempel--Ziv Jaccard Distance (LZJD) as a governance-oriented screening signal rather than a semantic performance metric. The framework establishes an enforceable and transparent identity infrastructure that enables continuity, auditability, and policy-aligned oversight across AI system lifecycles. By embedding AI identification within enterprise architecture and governance processes, the proposed approach supports sustainable innovation, strengthens institutional accountability, and provides a foundation for selective, policy-defined verification during digital transformation.
v2: Corrected affiliation domain to pastoral.tech. This paper presents a unified framework for anticipatory cyber defense integrating eight convergent dimensions: adversarial machine learning countermeasures, supply chain and hardware implant analysis, quantum threat transition analysis, attribution resistance with deepfake forensics, autonomous defense game theory, zero-knowledge proof systems for operational security, temporal correlation at scale, and biological-physical security integration. We formalize the Mantis autonomous defense environment as a Gymnasium-compatible reinforcement learning system with self-play training, introduce Chameleon, a five-channel defensive steganography framework using dynamic key rotation and Shamir Secret Sharing, and develop a ZK-Evidence Ledger for cryptographic evidence chains with Merkle tree notarization and Circom-based inclusion proofs. The convergence of these systems produces an anticipatory architecture where offensive research (Helix synthetic organization detection), defensive operations (Mantis game-theoretic simulation), and attribution resistance (zero-knowledge Merkle proofs) form a closed operational loop.
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Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
This research presents the design and implementation of the Decentralized Smart City of Things (DSCoT), a novel framework leveraging Web3 architecture to enhance the security and authentication of assets in cyber-physical systems (CPSs) for smart cities on a private blockchain. Unlike traditional non-fungible tokens that primarily identify and distinguish financial assets, existing approaches lack robust mechanisms for attributing and authenticating CPS assets such as owners, users, and IoT-enabled smart devices. DSCoT addresses this gap by introducing an extended ERC721 protocol, enabling IoT-enabled devices to have unique blockchain identities similar to user accounts, which enhances device management and tracking. Novel smart contract modules facilitate secure identification and authentication of CPS assets. Evaluated results on a private Hyperledger Besu blockchain show that DSCoT achieves significant performance improvements, including sub-second latency (âź0.1â0.5 s for application programming interface (API) calls), minimal transaction costs (âź0.01â0.05 USD), and a high processing capacity (âź1000 transactions per second (TPS)). These results, along with improved security through immutable authentication records, demonstrate DSCoTâs effectiveness as a scalable and secure solution for smart city CPSs.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
The transition from centralized power systems to decentralized infrastructures with a high share of renewable energy sources calls for reliable settlement in P2P electricity trading across âsmartâ regions. Blockchain platforms can enhance transparency and facilitate automated settlement; however, double-spend attacks still pose a threat to transaction finality and, consequently, undermine trust in the payment layer. This paper quantifies this risk through a probabilistic analysis of classical double-spend scenarios for Proof-of-Work (PoW) and Proof-of-Stake (PoS) blockchains augmented with periodic checkpoints, which render the chain history prior to the latest checkpoint effectively irreversible. We develop attack models for both consensus mechanisms and derive explicit formulas for the attackerâs success probability as a function of the adversarial share, the spacing between checkpoints, and the number of confirmation blocks. On this basis, we compute the minimum confirmation depth needed to satisfy a predefined risk threshold. Numerical evaluation using the derived expressions shows that checkpoints consistently reduce double-spend probability relative to checkpoint-free baselines; in the evaluated settings, the reduction reaches up to 44% and becomes more pronounced as the adversarial share increases. Finally, the analysis yields practical guidance for energy trading applications: accept a payment after the computed number of confirmations when it fits within a single checkpoint interval; otherwise, treat finality as reaching the next checkpoint.
The rapid proliferation of smart home IoT devices has introduced unprecedented cybersecurity vulnerabilities, necessitating scalable and privacy-preserving intrusion detection systems (IDS). Federated Learning (FL) offers a promising decentralized approach by training models locally without sharing raw data, but it remains susceptible to poisoning attacks and relies on a vulnerable central aggregator. This paper presents a novel blockchain-enhanced FL framework tailored for smart home IDS, integrating multiple consensus mechanismsâProof-of-Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Proof-of-Authority (PoA)âfor the first time in this context. Our approach uniquely combines differential privacy (DP) and secure aggregation (SA) within a blockchain-managed workflow to mitigate gradient inversion and membership inference attacks while ensuring tamper-resistant, decentralized trust. Experimental evaluation using the N-BaIoT dataset demonstrates that the proposed system achieves up to 88.3% detection accuracy with manageable latency (~200 ms/round) and formal privacy guarantees ($\varepsilon$=1.0 DP). The framework introduces 52.8% system overhead compared to vanilla FLâa reasonable trade-off for enhanced security and privacy. This work establishes a robust, transparent, and scalable security infrastructure for smart homes, effectively addressing the limitations of both centralized and conventional FL-based IDS.
The integration of IoT technology in smart grids has revolutionized the energy sector by enabling decentralized energy production, real-time monitoring, and peer-to-peer energy trading. However, these advancements introduce challenges such as ensuring security, scalability, and data privacy, which are critical for the reliable operation of IoT-enabled smart grids. Blockchain technology has emerged as a promising solution to address these challenges by providing decentralized, secure, and transparent frameworks for managing energy transactions. This study aims to explore the application of blockchain in enhancing the security and scalability of IoT-enabled smart grids while addressing challenges related to resource limitations and privacy concerns. Simulation and experimental analyses were employed to evaluate blockchain performance in a decentralized energy network. The study focused on key metrics: latency, transaction throughput, energy consumption, and data integrity. The study shows Proof of Authority (PoA) excels in IoT smart grids with < 200 ms latency, 190 Tx/s throughput, and 0.5â0.9 J/Tx energy useâoutperforming PoW (450-780 ms, 5.2â10.3 J/Tx). While Proof of Stake (PoS) offers competitive 0.3â0.7 J/Tx efficiency and higher 210 Tx/s scalability, its latency (150â300 ms) remains slightly higher than PoA. These results position PoA as ideal for resource-constrained IoT nodes, while PoS better suits more extensive networks needing higher throughput. The findings highlight how consensus mechanisms can be tailored to different smart grid requirements, with PoA providing the best balance for most decentralized energy applications. Additionally, blockchain's immutable ledger ensured zero unauthorized data modifications, enhancing data security and transparency. The practical implementation of these results highlights blockchain's potential to transform IoT-enabled smart grids. By reducing security vulnerabilities and operational inefficiencies, blockchain enables secure and efficient peer-to-peer energy trading and enhances the resilience of decentralized energy systems. Future work should optimize scalability beyond 500 nodes and integrate advanced privacy-preserving mechanisms to ensure the widespread adoption of blockchain in innovative grid applications.
Niomi Langaliya, Vinay Thakor, Purna Tanna, Disha Shah
This research preprint presents Aegis, a zero-knowledge-proof-based security paradigm designed to mitigate validator-compromise attacks in cross-chain bridges. The work empirically evaluates a ZKP-based withdrawal verification mechanism against an optimized multi-signature validator model under controlled conditions, demonstrating complete resistance to unauthorized fund transfers at the cost of increased Layer 1 gas consumption. The study introduces the concept of the cost of trustlessness as an empirically derived techno-economic metric and provides quantitative justification for migrating cryptographic verification to Layer 2 environments. This work was previously presented at FINCONâ25, National Forensic Sciences University (NFSU), Gandhinagar, India. This version is released as a non-peer-reviewed research preprint for open dissemination and citation. Journal submission is in progress.
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Physical Unclonable Functions (PUFs) and Hardware Security
AbstractContemporary blockchain architectures face a critical impasse defined herein as the "Tetra-Lemma"âa four-dimensional optimization problem comprising decentralization, security, scalability, and thermodynamic sustainability. Proof-of-Work networks confront diminishing security budgets, while Proof-of-Stake systems risk validator centralization. This paper presents a Unified Monetary-Supply Framework designed to resolve these structural conflicts. By deriving a closed-form solution for supply dynamics that integrates a deterministic "Customized Halving Mechanism" with probabilistic asset attrition models, we demonstrate a mathematical convergence that maintains thermodynamic security over a secular horizon. Key Quantitative Findings: Asymptotic Convergence: While effective circulating supply may experience a temporary peak (approx. 27 million RIN), all evaluated models are engineered to stabilize below the 21 million threshold (specifically converging to 20.88 million RIN). Secular Stability: The framework secures a deflationary emission schedule mirroring Bitcoinâs scarcity model over a multi-century horizon of 443â703 years. Publication Status & RoadmapThis manuscript (v1.5.0) is maintained as a Living Research Document. It serves as the foundational theoretical framework for the Rincoin protocol. Future iterations will formalize the consensus mechanisms required to govern these algorithmic parameters. Integrity & Provenance ArchitectureThe scientific integrity and existence of this document are secured by a Triple-Verification Layer: 1. Academic Provenance: Indexed via Zenodo (DOI: 10.5281/zenodo.17141922). 2. Thermodynamic Timestamping: Anchored to the Bitcoin blockchain via OpenTimestamps. 3. Identity Assurance: Digitally signed by the author via a third-party certification authority (GMO Sign). Note: Verification data and the "Certificate of Authenticity" are available in the supplementary files. CorrespondencePrimary Author: Michiru Tokino (also known as Aevust in the decentralized infrastructure community). Academic Inquiries: edu@aevust.org Community Governance: @aevustus (Discord) / @aevust (X/Telegram)
As the United States Department of Defense (DoD) transitions toward Zero-Trust Architecture, the hardware and software supply chain remains a critical vulnerability. Current provenance models rely on centralized, siloed databases that lack the transparency required to counter sophisticated state-sponsored interdiction. This paper proposes a novel framework: AI-Enhanced Trust Graph Analytics over Distributed Ledgers. The architecture utilizes a permissioned Distributed Ledger Technology (DLT) substrate to host an immutable record of component lifecycles, anchored by Hardware Roots of Trust (RoT) through Physically Unclonable Functions (PUFs). By mapping silicon fingerprints to Software Bill of Materials (SBOM), the system constructs a multi-dimensional Trust Graph. We employ Graph Neural Networks (GNNs) to detect structural anomalies indicative of subversion, while Federated Learning enables inter-agency intelligence sharing without compromising operational security. Our findings demonstrate that this integrated approach significantly reduces the time to detect compromised assets in air-gapped and tactical environments, providing a strategic roadmap for an autonomous, self-healing supply chain.
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Physical Unclonable Functions (PUFs) and Hardware Security