The Proof of Stake(PoS) consensus procedure has become a largely embraced option to the energy-demanding Proof of Work (PoW) contract, guaranteeing the integrity, reliability, and decentralization in the latest Blockchain programme. In a PoS system, staking procedures, validator and delegation functions, reward distribution processes, and governance control operations must be implemented accurately to maintain the system's security. This paper represents the formal verification and validation of Stake Manager key features, as verification of validators and delegators, setting staking tokens, reward evaluation and distribution, and authentication of the owner. This formal model is evolved using the Communicating Sequential Processes (CSP#), and this model is verified concerning critical safety and liveness properties defined in Linear Temporal Logic formula (LTL). The Process Analysis Toolkit (PAT) model checker is used to check these properties, containing accurate reward distribution and exclusion of deadlocks. This verification mechanism ensures that all processes work properly in all feasible execution states. The results ensure the reliability and security of the Stake Manager Contract and emphasize the efficacy of formal verification in securing the PoS blockchain structure.
Michele Fabi, Viraj Nadkarni, Leonardo Leone, Matheus V. X. Ferreira
<div> We develop an axiomatic theory for Automated Market Makers (AMMs) in local energy sharing markets and analyze the Markov Perfect Equilibrium of the resulting economy with a Mean-Field Game. In this game, heterogeneous prosumers solve a Bellman equation to optimize energy consumption, storage, and exchanges. Our axioms identify a class of mechanisms with linear, Lipschitz continuous payment functions, where prices decrease with the aggregate supply-to-demand ratio of energy. We prove that implementing batch execution and concentrated liquidity allows standard design conditions from decentralized finance-quasi-concavity, monotonicity, and homotheticity-to construct AMMs that satisfy our axioms. The resulting AMMs are budget-balanced and achieve ex-ante efficiency, contrasting with the strategy-proof, expost optimal VCG mechanism. Since the AMM implements a Potential Game, we solve its equilibrium by first computing the social planner's optimum and then decentralizing the allocation. Numerical experiments using data from the Paris administrative region suggest that the prosumer community can achieve gains from trade up to 40% relative to the grid-only benchmark. </div>
Frequent digitalization of power systems has increased the areas of cyber attack on smart grids that require more developed security frameworks capable of responding to cyber and physical threats. The paper is a detailed discussion on the implementation of Artificial Intelligence (AI) and Block chain in improving the cyber security of smart grids. To prevent new cyber threats, we suggest a new hybrid security model, which uses AI-based anomaly detection and decentralized integrity checks, based on block chains. The architecture is a fusion of machine learning based real time threat detection and immutable block chain ledgers to keep transactions safe and under control. We prove with the help of the performance evaluation and case studies that the model is efficient in the detection of false data injection, distributed denial of service (DDOS) attacks, and other advanced threats. The paper also cites major issues such as the scalability, interoperability, and computational overhead, as well as outlining opportunities of future research in resilient smart grid infrastructure. We have found out that the AI-block chain implementation has the potential to enhance the accuracy of threat detection up to 30 percent relative to traditional approaches as well as integrity and transparency of data management in smart grids.
High-mobility Internet of Things (IoT) for Vehicle-to-Grid (V2G) Demand Response (DR), including roaming between Charge Point Operators (CPOs), requires privacy-preserving authentication with sub-1 ms responses and cross-domain scalability as devices exceed 200km/h. Mechanisms must run on constrained hardware while remaining compatible with EV-charging message flows such as ISO 15118–20 and OCPP 2.0.1. Many deployed schemes re-authenticate from scratch, which inflates computation and airtime; static credentials also ignore trajectory context and struggle with rapid mobility. We present a Zero-Knowledge Proof-based Authentication Scheme (ZKPAS) for V2G/DR that proves possession without disclosure and replaces heavy handshakes with compact, mobility-aware proofs, targeting latencyLO(n) toO(logn). (iii) Predictive token generation with Long Short-Term Memory (LSTM) models trained on GeoLife and T-Drive pre-computes material, yielding 84.7% token reuse along trajectories. (iv) Cross-domain authentication employs (t,n)-threshold cryptography for Byzantine-tolerant roaming across operators. We prove resistance to impersonation, replay, man-in-the-middle, and trajectory inference; under the Computational Diffie–Hellman Problem (CDHP), the adversary’s success probability satisfies Pr[break] ≤ 2−λ. On real transportation topologies, ZKPAS cuts computation by 71.8%, authentication latency by 93.9%, and energy by 69.5%, while interfacing with V2G/DR control flows. The protocol sustains a 98.5% authentication success rate at 250km/h.
Hailong Zhang, Hong Zhao, Qiannan Chen, Chong Sun · 12 authors
Despite growing blockchain adoption in energy markets, its integration into critical operational functions — particularly relay protection— remains limited. Existing solutions fail to meet the stringent requirements of cryptographic auditability, cross-domain coordination, and configuration integrity in hierarchical grid architectures. To address this gap, we propose a three-tier blockchain-based information architecture for relay protection, featuring hybrid PoA+PBFT consensus (1.8 s finality), zero-knowledge proofs (ZKPs) for privacy-preserving setting verification, and smart contracts for tamper-evident logging. Evaluated on a Hyperledger Fabric v2.5 testbed across provincial, municipal, and substation tiers, the system achieves 40.2% faster hierarchical synchronization and 100% integrity of operation records. While unsuitable for sub-100 ms tripping, the architecture excels in non-real-time workflows such as setting management, fault archiving, and collaborative diagnostics— laying the foundation for secure, auditable, and interoperable protection systems.
Gauhar Ali, Sajid Shah, Mohammed ElAffendi, Naveed Ahmad
Introduction Digital Twins (DT) have appeared as a significant tool in Industrial Internet of Things (IIoT) environments, allowing real-time monitoring, predictive maintenance, and maximizing device performance. However, integrating DTs with IIoT initiates serious security issues, specifically in the device’s authentication and authorization. The state-of-the-art mechanisms are exposed to insider threats, single points of failure, and privacy issues. Methods This study proposes a blockchain-based access control framework for cross-domain DTs. The blockchain (BC) integration eliminates reliance on the centralized authentication server. It uses platform verification from the manufacturer to validate IIoT device integrity and mitigate insider threats. Moreover, the authorization mechanism is implemented using smart contract and access control policies stored in BC. The proposed Non-Fungible Tokens enable role and permission delegation. Results and Discussion The integration of Hyperledger Fabric BC, platform hash verification, and NFT-based authorization in the proposed architecture enhanced its resilience against cyber-attacks i.e., replay, DoS/DDoS, insider, and spoofing attacks. Moreover, the proposed framework validates its viability with response times (approximately 300ms) for the authentication and authorization phases. Additionally, identity resolution attains 67 % depletion in latency compared to its counterpart.
G.B. Bhavana, R. S. Anand, J. Ramprabhakar, Josep M. Guerrero · 6 authors
Integration of renewable-energy (RE) sources of energy into local microgrids, decentralised energy markets using blockchain based peer-to-peer (P2P) energy trading is gaining more traction due to its ability to enable transparent, autonomous, and secure transactions among distributed energy resources (DERs). Due to the large variety in microgrid topologies and their respective operational constraints, a key challenge to obtaining the most effective trading framework is the choice of blockchain consensus protocol that suits a given microgrid type. Choice of consensus mechanism solely affects the reliability and security of a network. This paper presents a quantitative comprehensive evaluation of various blockchain consensus mechanisms such as Proof-of-Work (PoW), Proof-of-Stake (PoS), Delegated Proof-of-Stake (DPoS), Proof-of-Elapsed-Time (PoET), Practical Byzantine Fault Tolerance (PBFT), Raft, and Tendermint to determine their suitability for P2P energy trading in microgrids. Various metrics such as fault tolerance, energy efficiency, latency, throughput and consensus time were evaluated for multiple consensus mechanisms through simulations. This paper also studies node-scaling impact on the protocols and presents a final decision framework to match the suitable protocol with various microgrid topologies.
R Udayakumar, A Haja Alaudeen, Anjali Goswami, N Arvinth · 5 authors
Blockchain technologies incorporated with decentralized energy grids can offer new paradigms in energy management, distribution, and consumption. Trust building among participants in decentralized energy systems is a critical challenge in the absence of a central authority. Foremost, the self-executing, fully autonomous blockchain-based smart contracts and the decentralized automated systems enable real-time, secure, and transparent business transactions. This paper examines the domain of smart blockchain contracts in decentralized energy grids. Smart contracts facilitate self-enforcement, aiding trust-building. Invoice blockchain contracts enable automated energy exchange, thereby facilitating contract execution without third-party involvement. Trust-building, system decentralization, energy producers and consumers bypassing operators, and operational cost reduction for self-sustainability provide system autonomy. The paper also discusses self-governing blockchain contract systems engineering. In smart energy contracts, the paper outlines system challenges, which include load, interoperable infrastructure, and energy consumption. Empirical and case study research, which are the primary focus of the paper, present blockchain contracts for decentralized energy systems as a trust-management and transaction-integration technology.
The rapid growth of Industry 4.0 has transformed traditional manufacturing systems into connected cyber-physical environments. These systems continuously produce large amounts of operational and sensor data, which are vital for predictive maintenance and informed decision-making. However, the reliability, integrity, and security of this data remain significant challenges. This paper introduces a blockchain-based industrial monitoring framework that integrates Artificial Intelligence (AI) for predictive analytics with blockchain for decentralized and tamper-proof data management. The framework enhances transparency, traceability, and secure collaboration among stake-holders by maintaining verified records of equipment health and maintenance activities. Smart contracts also automate fault alerts, compliance checks, and maintenance scheduling, removing the need for centralized authorities. Experimental results on industrial datasets demonstrate better data integrity, lower risk of cyber threats, and improved predictive accuracy. The proposed hybrid architecture offers a scalable, auditable, and secure foundation for next-generation industrial systems.
This paper presents a blockchain-based smart pricing framework designed to enhance traditional electricity markets by enabling decentralized, peer-to-peer (P2P) energy trading among distributed renewable energy producers and consumers. The proposed system, implemented on the Ethereum blockchain, introduces SmartPricingExchange. The system employs energy credits managed through an internal ledger to facilitate transparent and automated transactions. By removing conventional intermediaries such as DISCOMs, the framework promotes market liberalization, improves price transparency, and incentivizes small-scale producers through fair and trustless settlements. Deployed on the Ethereum Sepolia testnet using Remix IDE and MetaMask, the model demonstrates the feasibility of decentralized energy exchange and offers a scalable path for the modernization of future power markets.
The integration of Artificial Intelligence (AI), Internet of Things (IoT), and Vehicle-to-Everything (V2X) technologies in green mobility systems introduces new cybersecurity and privacy challenges. This paper proposes a lightweight cybersecurity framework that integrates compact convolutional neural networks (CNNs) for real-time anomaly detection at the edge, federated learning for decentralized model training, and blockchain-based decentralized identity management with zero-knowledge proofs. These mechanisms collectively ensure sub-100 ms threat detection latency, reduced communication overhead, and GDPR-compliant privacy preservation. Simulation results demonstrate a 60% reduction in latency, 45% lower communication costs, 30% energy savings at edge nodes, and a detection accuracy of 93.4% compared to traditional cloud-centric models.
This work introduces the Adversarial Cost Model (ACM v1.0), a formal security framework unifying computational, economic, and physical attack costs in a single rational adversary model. Unlike traditional security models based purely on computational hardness, ACM evaluates real-world feasibility of attacks under post-quantum cryptography, behavioral authentication, zero-knowledge proof systems, and decentralized governance. The model formalizes adversarial actions through total cost functions combining time complexity, hardware requirements, capital liquidity, and physical laboratory constraints. Multiple critical attack classes are analyzed, including hybrid side-channel + Grover attacks, GAN-based behavioral cloning, flash-loan Sybil governance attacks, post-quantum brute-force exhaustion, and zero-knowledge proof forgery. The results demonstrate that many real-world system failures arise not from cryptographic weakness, but from mispriced economic atomicity and cost-free identity or governance acquisition. ACM provides a rationality threshold theorem formalizing when attacks become economically and physically irrational. The model directly informs secure system architecture design by enforcing multi-layer cost escalation across cryptographic, physical, behavioral, and governance layers. This work is intended for cryptography, blockchain security, adversarial machine learning, economic attack modeling, and post-quantum system design.
Open access
Cryptographic Implementations and Security
Smart Grid Security and Resilience
Physical Unclonable Functions (PUFs) and Hardware Security
This work introduces the Adversarial Cost Model (ACM v1.0), a formal security framework unifying computational, economic, and physical attack costs in a single rational adversary model. Unlike traditional security models based purely on computational hardness, ACM evaluates real-world feasibility of attacks under post-quantum cryptography, behavioral authentication, zero-knowledge proof systems, and decentralized governance. The model formalizes adversarial actions through total cost functions combining time complexity, hardware requirements, capital liquidity, and physical laboratory constraints. Multiple critical attack classes are analyzed, including hybrid side-channel + Grover attacks, GAN-based behavioral cloning, flash-loan Sybil governance attacks, post-quantum brute-force exhaustion, and zero-knowledge proof forgery. The results demonstrate that many real-world system failures arise not from cryptographic weakness, but from mispriced economic atomicity and cost-free identity or governance acquisition. ACM provides a rationality threshold theorem formalizing when attacks become economically and physically irrational. The model directly informs secure system architecture design by enforcing multi-layer cost escalation across cryptographic, physical, behavioral, and governance layers. This work is intended for cryptography, blockchain security, adversarial machine learning, economic attack modeling, and post-quantum system design.
Open access
2 source records
Cryptographic Implementations and Security
Smart Grid Security and Resilience
Physical Unclonable Functions (PUFs) and Hardware Security
Efficient energy sharing among solar-based microgrids was crucial for enhancing grid reliability, scalability, and sustainability in ‎modern energy systems. This research presents a novel blockchain-powered decentralized energy trading framework that integrates ‎Raspberry Pi 4, IoT-driven real-time monitoring, and Ethereum-based smart contracts to facilitate seamless and secure peer-to-peer ‎‎(P2P) energy exchange. The proposed system enables real-time data acquisition and transmission of critical energy parameters, ‎including current, voltage, and power generation, from five interconnected solar microgrids. Raspberry Pi 4 serves as the centralized ‎edge computing node, aggregating and transmitting real-time energy data to the ThingSpeak IoT platform, where advanced AI-driven ‎analytics optimize grid efficiency. Blockchain technology, specifically Ethereum with Ganache, was employed to create a tamper-proof, ‎transparent, and trustless energy marketplace, eliminating reliance on centralized energy intermediaries. The incorporation of Solidity-based smart contracts automates transactions, ensuring secure, immutable, and fair energy trading while enabling dynamic pricing ‎models based on real-time demand-supply conditions. Python, integrated with Web3.py, facilitates seamless interaction between ‎Raspberry Pi 4 and the blockchain network, ensuring low-latency transaction execution and verifiable trade settlements. Through the ‎integration of IoT-enabled smart grids, blockchain-based energy transactions, and AI-driven predictive analytics, the proposed system ‎offers a scalable, autonomous, and energy-efficient solution for decentralized energy management. Experimental validation confirms the ‎system's effectiveness, demonstrating its ability to achieve real-time energy balancing, seamless P2P trading, and enhanced security ‎through blockchain immutability. This cutting-edge approach significantly advances the adoption of renewable energy sources, ‎optimizes microgrid autonomy, and reinforces the resilience of next-generation smart power networks, paving the way for a sustainable ‎and decentralized energy economy‎.
Shanmugam N, C.Jehan, Khaled Alqawasmi, N E Chandra Prasad · 6 authors
Cyber infrastructures, as the substrate of digital ecosystems, are more and more the objects of advanced cyberattacks, security protocol violations, untrustworthy third-party interactions. It is essential to provide a resounding guarantee of reliability and to enforce compliance across these infrastructures, at a time when the scale and complexity of data exchange and operational dependencies are increasing. In this paper, we present a new approach, which uses blockchain technology and smart contracts for cybersecurity compliance enforcement and operation resiliency in cyber infrastructures. The approach employs the decentralized, immutable, and transparent natures of the blockchain in order to create a tamper-proof and auditable setting where the cybersecurity policy can be enforced. Smart contractable compliance are able to automate compliance checking, record security events, and take responsive actions without the trust in a centralized entity. These smart contracts are self-executing action protocols that encode cybersecurity procedures and validation standards that take care of itself when every organization do abide by the security standards. Such mechanism ensures anticipatory enforcement on anomalous behavior and automatic remediation action execution, based on predetermined policy-rules. In addition, a layered protocol structure is being incorporated so that it can enforce compliance throughout different infrastructure components e.g., networks, cloud platforms, IoT systems, and critical information systems. The system design is tightly integrated with features such as identity management, threat intelligence sharing, behavior audit, and distributed access control, all of which are achieved via smart contracts. To build trust in transactions and operations in the environment, consensus algorithms and cryptography are employed. The paper also provides simulations and case studies illustrating the viability of the proposed method in important domains e.g., healthcare, finance and industrial control. It is found that such optimization results in significant enhancement on accuracy for compliance enforcement, reliability of the system, as well as response time to security incidents. The distributed architecture of the framework also reduces the possibility of single point failures and provides robust protection against insider attacks. This work adds to the increasing nexus of security and blockchain technology, providing a secure, autonomous trust and compliance protocol for dynamic cyber battlefields. It provides the groundwork for a future where intelligent cybersecurity contracts are enforcing regulatory mandates, automating the audit process and hardening infrastructure resiliency in a way that requires very little human intervention. The model effectively changes the narrative around compliance, because it is no longer static as a obligation but rather a dynamic, enforceable and self-governing trait woven into the composition of digital infrastructure.
Traditional Internet of Things (IoT) architectures suffer from centralization-induced vulnerabilities like single points of failure and data tampering. This paper proposes BITS, a lightweight blockchain-based framework designed for resource-constrained IoT environments. Its core is a novel Reputation-based Proof of Stake (RPoS) consensus algorithm that combines staking with behavioral reputation to elect reliable validators, significantly reducing energy consumption and computational overhead. BITS also employs smart contracts for automated device authentication and access control, alongside lightweight security techniques inspired by Advanced Metering Infrastructure (AMI). Theoretical and simulation-based analyses demonstrate that BITS achieves a throughput and latency profile compatible with typical IoT application requirements, while exhibiting superior resilience against DDoS attacks and malicious nodes compared to both Proof of Authority and centralized models. BITS effectively balances security, decentralization, and efficiency, offering a viable solution for enhancing trust in IoT ecosystems.
Smart contracts suffer from critical vulnerabilities such as reentrancy attacks, delegate call misuse, and timestamp dependencies, posing significant risks when deployed in smart grids. This paper proposes a solution that integrates deep learning techniques based on the Graph Isomorphism Network (GIN) to analyze Solidity code and detect vulnerabilities before deployment. This model was trained on a dataset of vulnerable contracts, leveraging automated hyperparameter optimization via Optuna to fine-tune the model’s performance. Performance evaluations supported by visual analysis of the model’s pre- and post-training weights provide essential insights into the behavior of the proposed approach. The experimental results demonstrated the effectiveness of GIN in identifying vulnerabilities with an accuracy of 95.26%, outperforming previous studies by more than 2%. These new findings highlight the potential of deep learning to secure and develop resilient smart grid infrastructures.
Gas optimization is a critical concern in the development of Ethereum smart contracts, with substantial implications for both cost-efficiency and security. This review systematically examines the latest peer-reviewed research on gas consumption in Solidity contracts, focusing on how micro-level decisions such as function implementation, data member usage, and storage patterns as well as macro-level architectural choices, including object-oriented structures like aggregation and inheritance, influence gas usage. Empirical findings reveal that persistent storage operations and cross-contract calls represent the highest gas expenditures, while optimization techniques such as struct and variable packing, use of immutables, and minimized storage access can yield significant savings. Object-oriented features, although beneficial for modularity, tend to increase gas costs if not carefully managed. The adoption of formal verification frameworks ensures the correctness of automated optimizations and prevents the introduction of subtle bugs. Furthermore, network-level gas price volatility underlines the need for continuous benchmarking and adaptive strategies. Overall, the review demonstrates that effective gas optimization requires an integrated approach, combining empirical measurement, codelevel best practices, formal guarantees, and awareness of evolving network conditions.
The growth of distributed energy resources and local energy markets heightens the need for price formation that is transparent, privacy preserving, and compatible with network constraints. Blockchain provides a trust-minimized substrate for auditable clearing and settlement through consensus, tamperevident ledgers, and smart contracts. This survey organizes blockchain-enabled pricing into three families, namely auction-based, game-theoretic, and optimization-based, and links them to enabling techniques such as metering oracles, secure multiparty computation, zero-knowledge proofs, and verifiable optimality certificates. Applications span wholesale electricity, carbon and green certificates, distributed energy trading, ancillary services, and electric vehicles. Evidence indicates gains in auditability, privacy, network awareness, and automated settlement, alongside challenges in scalability, data protection, grid integration, and regulation. The survey distills design patterns and research directions toward verifiable, interoperable, and governable pricing modules that complement system-operator markets.