This chapter explores the intersection between blockchain-based energy systems and data privacy challenges, especially in peer-to-peer energy markets. It highlights how the transparency and immutability of blockchain, combined with real-time data from smart meters and IoT devices, can expose users to re-identification risks and legal conflicts with principles such as the right to be forgotten. The authors examine technical solutions including zero-knowledge proofs, permissioned blockchains, smart contracts with embedded privacy logic, and off-chain storage. Additionally, the chapter emphasizes the importance of privacy-by-design and security-by-default principles in system architecture. It also calls for adaptive legal frameworks such as regulatory sandboxes, along with participatory governance to ensure equitable implementation, especially in the Global South where regulatory and infrastructure disparities persist.
H. M. Imran Hassan, Syed Enam Reza, Md Rafiqul Islam, A N M Obaidullah
The study explores the development of the I-REC standardized REC Trading Framework for Bangladesh, which can facilitate both local & international REC trading through a Nodal Agency. To implement such REC trading system, it is essential to establish a reliable, robust, transparent and secured REC trading platform that can gain global recognition and acceptance. Blockchain technology can play a crucial role to ensure transparency, security and trust in REC trading and verification system, as it offers a tamper-proof distributed ledger where RECs can be tracked from the issuance to the retirement. The entire process can be made automated using the self-executing algorithm called Smart Contract. This approach accelerates transaction processing and improves efficiency of record-keeping, minimizing the administrative overhead. Transactions such as the issuance, transfer, and redemption of RECs are securely recorded on the blockchain using cryptographic hashes of each block, making the process tamperresistant and easily auditable by all parties, thereby reducing the risk of fraud. Since blockchain employs a distributed ledger that is accessible to all stakeholders, global validators can independently verify the REC claims. The system's viability for national-scale deployment was validated using Ethers.js and the Remix VM, while its resilience was confirmed through a STRIDE-based threat assessment.
Md Moniruzzaman, Shahroz Abbas, Ajmery Sultana, Georges Kaddoum
The increasing adoption of electric vehicles (EVs) and distributed energy resources has led to the rise of peer-to-peer (P2P) energy trading, in which participants exchange energy within local markets. Blockchain technology has emerged as a secure and transparent solution for managing these transactions. However, the advancement of quantum computing poses a significant threat to the traditional cryptographic mechanisms used in blockchain systems. This paper proposes a quantum-safe blockchain framework designed specifically to secure P2P energy trading networks. The proposed system integrates quantum-resistant cryptographic techniques, including lattice-based cryptography and quantum key distribution (QKD), to safeguard transactions against quantum attacks. Additionally, a quantum-safe consensus mechanism, Quantum Delegated Proof of Stake (QDPoS), is introduced to enhance network security and scalability. Experimental evaluations demonstrate that the proposed approach improves transaction security while maintaining efficiency and reducing computational overhead. The findings highlight the need to integrate quantum-safe solutions into blockchain systems to ensure long-term security in decentralized energy trading networks.
Fahd Alhaidari, Sarah AlQahtani, Noura AlDossary, Rachid Zagrouba
As the Internet of Things (IoT) continues to expand across various domains, the number of connected devices is rapidly increasing, exposing IoT environments to large-scale Distributed Denial-of-Service (DDoS) botnet attacks. Due to limited computational and memory resources, IoT devices remain particularly vulnerable to traffic flooding and coordinated malicious behavior. This paper presents a layered security framework that integrates machine learning, protected gateway servers, and blockchain-based smart contracts to detect and mitigate DDoS botnet attacks in IoT environments. The proposed model performs behavioral anomaly detection off-chain using a two-stage machine learning process, while leveraging smart contracts on the blockchain for tamper-resistant logging, automated policy enforcement, and controlled economic penalties. A bounded spending mechanism and quarantine policy are introduced to discourage large-scale malicious traffic while limiting the impact on compromised legitimate devices. The system architecture and enforcement algorithms are presented to demonstrate the feasibility, scalability, and security advantages of the proposed framework.
The rapid expansion of IoT in smart hospitals enables continuous patient monitoring, automated diagnosis and real-time clinical decision support. However, centralized healthcare data systems remain vulnerable to unauthorized data modification, single points of failure and poor audit transparency threatening patient safety and regulatory compliance. This paper proposes a Block-chain-IoT (BC-IoT) framework built on a three-tier hierarchical architecture. The first tier connects heterogeneous medical devices (ECG monitors, glucose sensors, infusion pumps, pulse oximeters) through a lightweight IoT sensor layer. The second tier applies AI-based anomaly detection at edge computing nodes. The third tier employs a dual-block-chain approach, combining the IOTA Tangle protocol for feeless micro-transactions with a permissioned Hyperledger Fabric network for enterprise-grade data governance. Medical data is encrypted using AES-256 and TLS 1.3, screened for anomalies at edge nodes and stored on an immutable distributed ledger. Smart contracts enforce role-based access control, ensuring only authorized personnel can access or modify patient records. The Inter Planetary File System (IPFS) handles decentralized storage of large medical files, with cryptographic content identifiers stored on-chain for full traceability. Evaluation on a simulated smart hospital testbed with 350 IoT nodes across five department’s demonstrated strong results: 42ms transaction latency, 1,250 transactions per second, a 99.7% tamper detection rate and a 99.98% data integrity score outperforming existing block-chain-IoT healthcare systems across all key metrics. The BC-IoT framework offers a scalable, energy-efficient and standards-compliant solution for securing digital health infrastructure.
Efficient pricing mechanisms are critical for optimizing water consumption, managing demand, and promoting equitable resource distribution. This paper presents a Time-of-Use (TOU) dynamic pricing model integrated with Blockchain Technology (BT) to address these challenges. The proposed model categorizes time into Peak, Off-Peak, and Shoulder hours, applying distinct pricing tiers responsive to demand fluctuations, further refined through seasonal adjustments and demand constraints to support sustainability and regulatory compliance. Implemented on the Polygon blockchain - chosen for its low transaction cost, high scalability, and ethereum compatibility - the framework leverages Smart Contracts (SC) for secure, transparent and automated real-time pricing adjustments. Full on-chain data storage eliminates off-chain dependencies, ensuring data integrity and traceability throughout the water supply chain. Pseudonymous blockchain identities maintain user privacy while providing full auditability via immutable on-chain records. Security vulnerabilities such as input manipulation, arithmetic overflows, and unauthorized access receive proactive mitigation through secure coding practices, role-based access control, and logic constraints. Performance evaluation using the k6 load testing tool under simulated real-world conditions shows robust system behavior: response times ranged from 2.14s to 5.64s, with the 90th and 95th percentiles at 3.5s and 4.26s, respectively. Latency ranged from 5.96ms (median) to 49.54ms (95th percentile), validating the system’s responsiveness, scalability, and reliability under concurrent user requests. The test results further highlight the system’s ability to handle concurrent user requests efficiently, demonstrating reliable performance under simulated varying workloads.
The water supply chain is vulnerable to risks such as unauthorized usage and identity impersonation. Traditional solutions lack transparency, tamper resistance, and scalability, making them unsuitable for multi-stakeholder environments. To address these challenges, our paper presents BEDLAM, a Blockchain-Enabled Dual-Layer Authentication Model framework, designed to secure water supply chain operations. The framework employs two complementary authentication layers, namely, (i) a blockchain-based identity management layer that provides verifiable stakeholder authentication while leveraging Zero-Knowledge Proofs (ZKPs) and (ii) a smart contract-based verification layer that regulates access control, service allocation, and transaction validation among multiple entities. The first layer of BEDLAM is implemented on the Mina Blockchain, via the Auro Wallet, and evaluated by using Tinkercad-based circuit simulations. The second layer is implemented using smart contracts to ensure user access control. Our proposed system ensures cryptographic data verification with finality, achieving a latency of 153 ms and generating tamper-proof records. Sensor data are processed on resource-constrained IoT devices, producing compliance proofs. Multiple simulations involving batch users demonstrate linear scalability (average proof time of 26 s per user, 0.038 transactions per second) and significant stability. The success rate of transactions is 99.3% with exponential back-off retries under 40% simulated packet loss.
Tamara S. Alakbarova, Parvin A. Abbasova, Samira B. Baratzade
In the study, cryptographic authentication approaches for ensuring secure automated access in Cyber-Physical Systems were modeled and examined. The proposed research analyzed the efficiency of three cryptographic models based on Public Key Infrastructure, Zero-Knowledge Proof, and Elliptic Curve Cryptography with the challenge-response mechanism. It was investigated how each model performs under varying latency, computational, and scalability conditions in smart grids, autonomous vehicle systems, and industrial Internet of Things environments. It was identified that the Elliptic Curve Cryptography model provides the best performance in real-time and resource-constrained scenarios. It was studied that the Zero-Knowledge Proof approach ensures higher privacy protection and stronger attack resistance compared to other models. It was defined that the Public Key Infrastructure model remains effective in structured networks but exhibits higher latency. It was established that simulation tools such as Matrix Laboratory and Network Simulator 3 confirm the reliability and reproducibility of results. It was developed a comparative framework that allows researchers to select optimal authentication methods for specific operational contexts. It was justified that hybrid approaches combining multiple cryptographic mechanisms can enhance both efficiency and resilience in Cyber-Physical Systems.
Sadly Syamsuddin, Salama Manjang, Muhammad Bachtiar Nappu, Ady Wahyudi Paundu
The increasing demand for sustainable and decentralized energy systems has driven the adoption of blockchain technology in microgrid networks. However, conventional consensus mechanisms, such as proof of work (PoW) and proof of stake (PoS), suffer from high energy consumption, limited adaptability, and fairness issues, which hinder their suitability for dynamic microgrid environments. This paper proposes a novel consensus mechanism, proof of dynamic influence and participation activity (PoDIPA), which integrates prosumers’ real-time participation activity and historical influence into the validator selection process. The proposed mechanism is evaluated through deterministic simulations and compared with PoW and PoS in terms of energy efficiency, transaction processing time, and security resilience. Simulation results demonstrate that PoDIPA significantly reduces average energy consumption and adapts more rapidly to network dynamics while maintaining security performance comparable to existing consensus mechanisms under majority attack scenarios. Although PoDIPA exhibits higher short-term variability due to its adaptive nature, the overall efficiency–stability trade-off remains favorable. These results indicate that PoDIPA is a promising consensus solution for supporting fair, energy-efficient, and decentralized energy trading in future microgrid systems.
Venkatesh Babu R, Vignesh D, Sibaath Ahmed S, M P Ramkumar · 5 authors
The conventional messaging platform such as WhatsApp or Telegram is based on a centralized server, which fundamentally creates a gateway to censorship, surveillance, and points of failure. In essence, that is damaging to the user privacy and information security. In this paper, therefore, we develop and implement a Decentralized Chat Application (DCA) using Ethereum blockchain. Based on the fundamental capabilities of distributed ledger technologies, namely, immutability, transparency, and trustlessness, we are building a reliable, censorship-resistant chat service. The application operates under Ethereum Smart Contracts to handle decentralized user identities and to store public encryption keys safely as well as establishing chat channels. Our practical message content and media flows are stashed into an effective Peer-to-Peer (P2P) network, potentially stashed in off-chain storage such as Interplanetary File System(IPFS), but all messages are End-to-End Encrypted.The DCA model provides a good framework of the next-gen secure, private, and autonomous social interaction, as it leaves the end-users with complete ownership and control over their digital communication by abandoning the central authority.
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 cyber–physical 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.
Umar Majeed Sada, Abrar Ahmad, Najam Saqib, Md. Sarwar
Introduction: With the increasing adoption of solar photovoltaic, bioenergy, and wind energy in distributed networks, consumers are transforming into prosumers, utilizing renewable energy resources within distributed microgrid systems. In these environments, peer-to-peer (P2P) energy trading emerges as a highly promising energy management solution. Methods: This paper introduces a Polygon-based blockchain smart contract designed to execute trading and payment rules autonomously, significantly enhancing the security and fairness of energy trading compared to traditional database technologies. The smart contract consists of two core components: a bidding and settlement module and a payment module, both deployed on a Polygon-based test network, Cardona, to assess real-time interactions. Furthermore, to validate this model, simulations were conducted using realistic data on an Ethereum Virtual Machine (EVM) in a microgrid setting with six participants. Results: The results illustrate the model's potential to facilitate the integration of renewable energy within microgrids, thereby encouraging the adoption of distributed energy resources and supporting localized P2P energy trading. The proposed methodology shows that Polygon-based smart contracts with auction mechanisms can enable efficient, transparent, and automated P2P energy trading, enhancing grid stability in non-autonomous microgrids. However, the approach requires validation at larger scales to assess performance under real-world conditions . Conclusion: This approach offers a robust framework for designers aiming to build sustainable, decentralized energy markets.
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 human–machine 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 human–AI 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% (±4.1%) 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 blockchain–MAS 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 identified—basic, intermediate, and advanced—distinguished 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: Blockchain–MAS 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.
Blockchain technology has emerged as a core enabler of decentralized digital infrastructure by offering inherent features such as immutability, distributed trust, and transparency. These characteristics position blockchain as a promising foundation for building sustainable and accountable digital ecosystems. However, despite its growing adoption across domains such as governance, finance, supply chain management, and public services, blockchain systems continue to face critical challenges. Security vulnerabilities, scalability constraints, energy consumption, and transparency—privacy trade-offs remain major barriers to their long-term reliability and sustainability. This study presents a structured analysis of security and transparency challenges in blockchain-based decentralized systems, with specific emphasis on their role in sustainable digital infrastructure. Key architectural components, including consensus mechanisms, decentralization models, and immutable ledger design, are examined to identify structural weaknesses and performance bottlenecks. The primary contribution of this work is the introduction of a unified layered challenge taxonomy that categorizes threats across protocol, network, and application layers, supported by a dataset-driven comparative evaluation of representative blockchain platforms. The findings demonstrate that sustainable blockchain deployment requires carefully balanced design strategies that integrate efficiency, security, and transparency rather than optimizing any single dimension in isolation. This work provides practical insights and reference points for future research aimed at developing secure, scalable, and sustainability-oriented decentralized systems.
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 “service holding time” (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.