Hassan Cessi Ibrahim, Damilare Timothy Ogunjobi, Philip Mensah
The networks that run operational technology (OT) substations, water treatment plants, oil and gas pipelines, and manufacturing lines are moving from a centralized control to a federated, multi-stakeholder architecture coordinated by permissioned distributed ledgers. Protection and control loops in the electrical grid and other critical infrastructure have protection-relay tripping times, IEC 61850 GOOSE message classes, and SCADA/PMU polling cycles that impose multi-millisecond to sub-second deadlines on protection and control operations, while Byzantine fault-tolerant (BFT) consensus protocols like PBFT, Tendermint, HotStuff, and HoneyBadgerBFT were designed for settlement workloads that can tolerate hundreds of milliseconds to seconds of latency. In this paper, we survey four representative BFT families, discuss their structural latency and scalability constraints for OT deployment, and introduce a hybrid consensus algorithm called IsoBFT (Isochronous Byzantine Fault Tolerance), which combines an optimistic single-round-trip fast path with a PBFT-style fallback mechanism based on a network-stability monitor, and elects a small rotating committee using a verifiable random function (VRF). A formal system model, safety/liveness/termination proof, and security analysis for eight attack classes are provided, with a proposition quantifying the degradation of the practical availability of the safety guarantee when the global Byzantine fraction is approaching one-third. Using realistic Modbus/DNP3/IEC 61850 OT traffic, the discrete-event simulation of the design IsoBFT managed to execute realistic workloads with median consensus latency ranging from 4.90ms at n = 10-50 to 9.17-11.26ms at n = 100 and n = 500, remaining competitive with or better than PBFT and Tendermint across this range. Committee-bounded communication overhead stayed essentially flat with respect to the number of validators from n = 10 to n = 50, but newly completed runs at n = 100 and n = 500 (n = 200 still outstanding) show overhead growing faster than the quadratic scaling of PBFT and Tendermint over that range, together with a heavy P95/P99 latency tail not present at smaller scale; this discrepancy with the theoretical scale-independence result is reported and discussed rather than resolved. IsoBFT could reduce the median latency by approximately 81% and 56% under up to 33% Byzantine faults compared to HotStuff and HoneyBadgerBFT, respectively, at n = 10-50, while maintaining the safety of the system; a Byzantine-resilience sweep at n = 100 shows a narrower advantage over PBFT/Tendermint than at smaller scale.
Ravindra Janardan Lawande, Sudhir Bapurao Lande, Manisha Lande
Internet of Vehicle (IoV) uses heterogeneous access technologies to link automobiles and their surroundings. Effective methods are essential for safeguarding data confidentiality and privacy during communication among the roadside unit (RSU), the control room, and vehicles. Many vehicle-to-infrastructure authentication-based approaches have been developed to secure the IoV environment. However, efficiency and security are challenged by instability, decentralization, and transaction-tracking features. To resolve this, a secure, lightweight, and scalable communication protocol was developed for a 5G-enabled SDN-IoV environment. Efficient block verification is achieved through the Joint-Graph Delegated Practical Byzantine Fault Tolerance (JtGr-DPBFT) mechanism, in which validators create subgraphs to reduce communication overhead. JtGr-DPBFT is combined with an Improved Gossip Algorithm (IGA) to minimize message redundancy and optimize bandwidth utilization. Moreover, a lightweight hierarchical authentication mechanism, assisted by a Merkle Tree with Boneh-Lynn-Shacham (HAMT-BLS) signatures, enables compact block verification and minimizes computational and communication costs. The proposed model achieves tamper-proof, efficient, and scalable block verification by incorporating hierarchical authentication with consensus optimization. This approach is simulated in the NS3 tool, and performance is evaluated in terms of propagation delay, transaction confirmation latency, throughput, communication cost, and network delay. Thus, secure and tamper-proof communication is developed to ensure integrity, trust, and dependability in the SDN-enabled IoV environment.
As the digital landscape expands, centralised cybersecurity frameworks grow increasingly vulnerable to sophisticated threats, creating single points of failure and targets for adversarial data manipulation. While AI enables real-time threat detection and big data analytics, its centralised deployment limits efficacy and exposes training data to poisoning and evasion attacks. To address this, the AICyber-Chain model proposes a distributed framework combining parallel AI and blockchain architectures. It leverages a hybrid Proof-of-Stake (PoS) and Byzantine Fault Tolerance (BFT) mechanism with IPFS and Private Data Centres (PDCs) for secure decentralised storage and processing. Generative Adversarial Networks (GANs) refine security rules, while Ethereum-based smart contracts enable automated responses and trustless data sharing. Results on the Rinkeby test network show 1.8× faster authentication, 25% lower gas consumption, F1 score of 0.92, and 1.2 s response time, with a medical data sharing use case ensuring data provenance and tamper-proof control.
In today's era, securing data related to environmental and forest department has become a crucial aspect. Integrating blockchain based technologies with the forest data management is an effective solution. Blockchain technology such as Hyperledger Fabric is a permissioned blockchain. It can be helpful in storing the forest data in a more accurate, secure and tamper-proof manner. In this work a Hyperledger Fabric based framework has been developed to store the forest data in a secure and cost effective manner. The developed system can be used in the forest department to improve security and to monitor forest activities like illegal logging. In the developed system all the records are stored in an immutable manner. Its decentralized and permissioned nature helps to prevent any unauthorized participants, which means it only allows the participants that are authorized or permitted to perform within the network. The proposed technique has been implemented on Fabric 2.5 Test-Network. Performance analysis revels that the proposed system can be implemented in real time.
The mushrooming digitalization of industries has heightened the need to have secure and resilient system design as well as smart system design that is prone to address intricate cyber threats and data breaches. Artificial Intelligence (AI) and Blockchain have become the new influential technological innovations that could contribute greatly to the security, visibility, and reassurances of any digital ecosystem. AI can be used to provide smart threat detection and predictive analytics, intelligent decision-making, and blockchain can be employed to provide decentralization trust, immutability, and secure data sharing. The chapter discusses the prospects of AI and blockchain in the design of secure digital architectures and how the two can be used concurrently to enhance cybersecurity, keep data intact, and ensure operational resilience. It talks about architectural structures, practical implementation in industries, and ethical or regulatory implications as well as the future opportunities to create a solid and reliable digital systems in a more globalized world.
The growing interconnectivity of industrial systems has intensified the need for secure, intelligent, and scalable data transfer mechanisms within Industrial Internet of Things (IIoT) environments. Despite rapid IIoT adoption, industrial data transfer remains vulnerable to high-volume, dynamic cyber anomalies and consensus-level attacks, while existing security mechanisms struggle to jointly deliver low-latency, scalable, and trustworthy communication under large-scale adversarial deployments. This study introduces a Secure Dual-Consensus Blockchain-Enabled Deep Learning Framework (SD-BDL) that unifies blockchain security and adaptive anomaly detection to ensure trustworthy and efficient IIoT communication. The framework employs a hybrid consensus mechanism, integrating Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) to achieve enhanced fault tolerance, reduced latency, and protection against collusion and Sybil attacks. To address the dynamic and high-volume nature of IIoT data streams, a CNN–LSTM model is deployed for real-time anomaly detection, with hyperparameters optimized using the Adaptive Aquila Optimization (AAO) algorithm—identified as the most effective technique for achieving rapid convergence, high detection accuracy, and balanced exploration–exploitation. The proposed SD-BDL framework is evaluated on an IIoT dataset, incorporating preprocessing steps to mitigate class imbalance, missing values, and noise interference. Experimental outcomes demonstrate a significant improvement in performance metrics, achieving an R² score of 0.985, throughput enhancement of 25.2%, and latency reduction of 19.4% compared with benchmark models using PSO, GA, and Bayesian optimization. The hybrid consensus blockchain further ensures transaction integrity, tamper resistance, and low-energy overhead, validating its robustness under adversarial and large-scale deployment scenarios involving over 1,000 nodes. This research contributes a novel, energy-efficient, and scalable architecture for industrial data protection, setting a foundation for future integration with 6G-enabled IIoT systems, federated trust networks, and lightweight transformer-based threat detection frameworks.
Permissioned ledgers are commonly treated as centralised because admission is restricted. This paper separates permissioning from control distribution and proposes identity-staked consensus as a trust model for accountable settlement ledgers operated by chartered validators. The model treats validator identity as externally costly collateral: public legal identity, charter state, institutional reputation, liability, attributable audit exposure, a phase-indexed conditional identity-loss floor, and loss-realisation channels outside the protocol. It distinguishes this construct from proof-of-authority by formalising validator acts as actor constellations, public validator anchoring, affiliation-aware voting caps, threshold class coverage, per-member collusion margins, observer-supported detectability, bootstrap claim discipline, and a consensus/application enforcement boundary. The paper connects the model to a broader identity-infrastructure series: the actor-assurance paper supplies capability-gate evidence, the trust-anchor paper supplies public validator anchoring and assurance-at-time, and the delegated-authority paper can consume the ledger evidence record for mandate and model-attribution records.
The convergence of Smart Grids and the Internet of Medical Things (IoMT), termed Grid-IoMT, represents an emerging paradigm where healthcare facilities dynamically interact with energy grids to optimize both clinical operations and power consumption.Real-time medical data streams (e.g., continuous vital signs from wearable monitors, infusion pump logs, ventilatory parameters) traverse network infrastructure shared with grid telemetry, creating unprecedented attack surfaces where energy-demand manipulation can indirectly compromise patient safety, and conversely, medical data injection can destabilize grid frequency regulation.This paper presents BlockAuth-GridMed, a novel blockchain-anchored adaptive authentication framework specifically designed for real-time medical data streams in AI-driven Smart Grid-IoMT converged networks.The framework integrates three synergistic innovations:(1) A hierarchical blockchain architecture (local permissioned chains for clinical domains interconnected via a main chain for cross-domain trust) that anchors authentication proofs without introducing latency prohibitive for real-time medical applications (median latency 187ms),(2) An adaptive authentication engine powered by deep reinforcement learning (DRL) that dynamically adjusts authentication strength based on real-time risk assessment-escalating to multi-factor requirements during grid instability events or cyber-threat alerts while maintaining low-friction single-factor authentication during quiescent periods,(3) A zero-knowledge proof (ZKP) layer enabling mutual authentication between medical devices and grid nodes without revealing sensitive patient identifiers or clinical data patterns to energy system operators.We evaluate BlockAuth-GridMed using a realistic testbed emulating a 200-bed smart hospital integrated with an IEEE 13-bus distribution grid model, processing 15,000 real-time medical data streams per second across 6,500 IoMT devices and 12 grid sensors.The framework achieves 99.97% authentication success rate for latency-sensitive medical alerts (critical events requiring <100ms end-to-end latency) and maintains an average authentication overhead of 28ms, well within clinical requirements.Under adversarial conditions (simulated man-in-the-middle, replay, and grid-state injection attacks), BlockAuth-GridMed demonstrates 96.8% attack detection and 99.1% attack prevention rates, outperforming baseline certificate-based (83.4%/87.2%)and token-based (71.3%/74.6%)schemes.The DRL-driven adaptive authentication reduces unnecessary multi-factor challenges by 73% compared to static high-security policies, significantly improving clinical workflow efficiency.We also analyze blockchain gas costs (approx.\$0.012 per authentication), scalability under IoMT device churn (up to 15% daily device joins/leaves), and regulatory alignment with HIPAA, NERC CIP, and FDA pre-market guidance for medical device security.This work provides the first integrated authentication framework specifically tailored to the Grid-IoMT convergence, enabling secure, real-time, and adaptive protection for medical data streams in energy-aware healthcare infrastructures.
This chapter explores the critical role of data polishing and anomaly detection in enabling decentralized finance (DeFi)-driven digital transformation within the energy and utilities industry, with broader implications for dataintensive environments such as cryptocurrency markets and metaverse ecosystems. In an ideal digital infrastructure, decision-making systems operate on transparent, consistent, and high-quality data that support reliable automation, decentralized governance, and predictive analytics. Such an ecosystem presumes seamless data integrity, adaptive risk monitoring, and trustworthy financial and operational exchanges. In practice, however, industrial and financial platforms remain vulnerable to noisy datasets, measurement errors, systemic inconsistencies, and undetected anomalies, which undermine analytical accuracy and institutional confidence. Prior studies on machine learning, data cleaning, and blockchain-based energy systems emphasize preprocessing, normalization, and outlier detection as 78 prerequisites for intelligent operations. Parallel research on crypto-market anomalies and metaverse security highlights the relevance of statistical and learning-based surveillance models. Yet, these strands often remain methodologically fragmented, rarely examining their integrated function within DeFi-enabled infrastructures. This chapter addresses this gap by advancing a unified analytical framework grounded in data reliability theory and decentralized analytics. Focusing on the comparative evaluation of IQR, MAD, and LOF models applied to XRP/USD datasets since 2018, the paper assesses robustness, sensitivity, and computational efficiency. The findings demonstrate how systematic data polishing strengthens trustless financial architectures, enhances operational resilience, and supports sustainable digital transformation in energy and utility ecosystems.
V.I. Petrenko, M. Kh. Najajra, F. B. Tebueva, V. I. Pronin
The article presents an innovative method for distributed access control of robotic agents in a decentralized cyber-physical system (CPS), which combines an advanced architecture of graph attention neural networks (CAT-GNN) with blockchain technologies. The proposed approach aims to enhance the security, reliability, and fault tolerance of interactions between agents through dynamic behavioral anomaly analysis using CAT-GNN, capable of detecting complex spatio-temporal dependencies in agent behavior. The calculated anomaly score is used for adaptive adjustment of the trust level in agents, directly influencing access decisions to critical resources within the distributed system. Simulation experiments have demonstrated that the CAT-GNN detector outperforms the baseline STAD-GNN model in key metrics such as Accuracy, Fl-score, and ROC-AUC, showing high stability and precision in detecting malicious behavior while varying the number of agents and the proportion of malicious participants. The introduction of a dynamic trust mechanism significantly increased the proportion of successfully completed tasks from 63 to 82 %, while simultaneously reducing errors from over 18 to 8 %. The method relies on the integration of machine learning and distributed ledger protocols, ensuring transparency, immutability, and flexibility in access management. This comprehensive mechanism effectively counters internal and external threats, meeting modern security requirements of industrial and IoT systems. The proposed method is capable of effective scalability and adaptation to changing conditions of cyber-physical systems, confirming its high practical value and promising potential for broad application in critical infrastructures, industry, and transportation networks.
Traditional distributed consensus mechanisms rely on probabilistic assumptions, economic weighting (Proof-of-Stake), or arbitrary computational work (Proof-of-Work) to secure ledger state transitions. These models leave the application layer inherently vulnerable to Man-in-the-Middle (MITM) attacks, Maximal Extractable Value (MEV) extraction, and semantic exploits against critical infrastructure (SCADA/PLC). This manuscript introduces Proof-of-Rigidity (PoR), a deterministic state-validation framework that locks the consensus machine within a continuous 150-decimal-place geometric manifold ($G_{24}$ volume space). The paper formalizes three core components: The Brittle Acceptance Predicate: A Coq-verified mathematical boundary that enforces an absolute $10^{-80}$ validation tolerance, structurally denying unauthorized state mutations. Mantissa Tail Parity (The MEV Sieve): A mechanism utilizing Canonical Decimal Arithmetic ($\mathbb{D}_{150}$) to mathematically neutralize routing interception and front-running. Capability-Constrained Semantic Policies: A bipartite matrix that structurally subordinates LLM-based ontological analysis to strict cryptographic Role-Based Access Control (RBAC), preventing adversarial paraphrasing against industrial endpoints. By enforcing strict geometric determinism, PoR transforms network security from probabilistic difficulty into mathematical brittleness. Included in this deposit are the Coq formal verification proofs, a Python reference implementation of the Layer-1 substrate, and a computational benchmarking harness demonstrating throughput scalability. LEGAL, ETHICAL, AND SAFE HARBOR DISCLAIMER The mathematical models, formal Coq proofs, and Python reference implementations contained within this deposit are published strictly for academic research, cryptographic peer review, and educational purposes. The architectures described herein represent a theoretical substrate and an experimental prototype. They have not undergone formal, independent security auditing for production deployment. No Warranty (As-Is): The mathematical models and reference code are provided "AS IS", without warranty of any kind, express or implied. The continuous geometric bounds and mechanisms detailed herein are theoretical thresholds; physical hardware limitations, truncation errors, or implementation flaws may affect real-world execution. Limitation of Liability: Under no circumstances shall the author, contributors, or affiliated research entities be held liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, loss of use, data, stablecoin assets, or profits; business interruption; or industrial infrastructure failure) arising in any way out of the use, deployment, or misconfiguration of this protocol. Assumption of Risk: Any entity choosing to implement the $G_{24}$ volume space boundaries, the Topological Shatter mechanics, or any variant of the PoR consensus layer within a live environment does so entirely at their own risk, and is solely responsible for ensuring compliance with all applicable cybersecurity and financial regulations.
Peer-to-peer (P2P) energy trading requires network-aware coordination because transactions are physically realized through distribution networks. However, sensitivity-based coordination causes a confidentiality-verifiability tradeoff, as network sensitivities may reveal vulnerable components while undisclosed sensitivities prevent participants from verifying utility-provided transaction guides. This paper proposes a zero-knowledge-proof-based method for verifying the computational integrity of network-constrained transaction guides with respect to committed private network data, without exposing network-sensitivity information. The guide defines admissible injection and withdrawal volumes derived from sign-decomposed sensitivity matrices while satisfying balance, voltage, line-flow, and optimality conditions. These conditions are encoded in an arithmetic circuit, represented as R1CS constraints and a quadratic arithmetic program, and verified using a bilinear pairing. Blockchain commitments bind the approved circuit, public inputs, statement identifiers, proof, and verification result for tamper-evident auditability. The proposed proof certifies correct guide computation from committed network data; the authenticity of the committed network data is handled through an explicit registration and attestation assumption. Case studies on a modified IEEE 33-bus system show satisfaction of network constraints after clearing, rejection of public-input and witness-inconsistency attacks, and practical on-chain overhead, with an 806-byte proof.
In response to the difficulty of balancing privacy protection and system efficiency in energy data trading, this article analyzes the limitations of existing methods: static pseudonym mechanisms can easily lead to long-term identity link risks, traditional zk-SNARKs schemes have high computational overhead, and Raft consensus mechanisms lack robustness in adversarial environments. To address the above challenges, an integrated privacy protection scheme based on dynamic pseudonyms and lightweight zk-SNARKs is proposed. This scheme breaks the temporal correlation of transactions through a dynamic pseudonym generation mechanism, uses blockchain level batch processing proofs to reduce the computational and storage overhead of zero knowledge proofs, and introduces an LSTM based node health assessment model and incremental log synchronization mechanism to enhance the error tolerance and synchronization efficiency of the Raft consensus algorithm. The experimental results show that the proposed scheme outperforms traditional methods in terms of privacy, transaction processing performance, and system availability, effectively achieving a balance between privacy protection and operational efficiency, and providing a feasible technical path for energy data trading.
K Vigneshkumar, A R JayaSudha, P Nandini, Jana Murugesan · 7 authors
The system architecture presented in this work uses blockchain technology in conjunction with cryptographic authentication methods and real-time anomaly detection to validate the integrity of artificial intelligence models. By integrating tokenization-based model tracking, zero-knowledge proof verification, and machine learningbased integrity monitoring, the suggested solution fills important holes in current methods. We use Ethereum smart contracts to construct a prototype system and assess it using several AI model designs. In comparison to signature-based methods alone, experimental findings show 96.7% fewer false positives, sub-second verification latency for the real-time component, and 99.4% detection accuracy for model tampering attempts. Every day, the system effectively processes 10,000 model inference records while upholding cryptographic security requirements.
A tokenised energy market settles payment against metered dispatch, but the meter reading is the prosumer's private information: a self-interested prosumer can report more energy than it supplied and be paid for the difference. The companion papers in this programme assume meter integrity — truthful reporting — and build settlement, participation, and delivery contracts on top of it. This paper derives the verification contract that makes the assumption hold. A prosumer dispatches a quantity it observes privately and reports a possibly inflated figure to the settlement layer; the grid-telemetry layer can audit a report at a cost, detecting a discrepancy with a probability that reflects sensor accuracy, and a detected misreport forfeits a posted verification stake. We treat the audit probability, the stake, and the sensor accuracy as the designer's instruments and characterise the verification that makes truthful reporting weakly dominant at minimum cost. The baseline assumes a margin-independent detection probability and one-sided audit error (false negatives possible, false positives excluded); both are stated and the general margin-dependent condition is given. First, truthful reporting is weakly dominant if and only if the expected forfeiture covers the largest gain from admissible over-reporting, αφB ≥ Pm̄ (strict under strict inequality), where α is the audit probability, φ the per-audit detection probability, B the stake, and m̄ the largest admissible over-report; with a one-unit maximum this is αφB ≥ P (Proposition 1). Second, along this deterrence frontier the audit probability is α = Pm̄/(φB), and once the stake is itself chosen against its capital carry the least-cost interior contract is B* = √(κPm̄/(ρφ)), α* = √(ρPm̄/(κφ)), total cost 2√(ρκPm̄/φ), all decreasing in detection accuracy, so accurate telemetry drives the audit rate, the stake, and the cost down together (Theorem 1). Third, sensor accuracy is itself a procurable instrument with a convex capital cost, and the cost-minimising accuracy equates marginal sensor capital cost to the marginal audit-opex saving, a capex–opex frontier between better meters and more auditing (Proposition 2). Fourth, the per-report enforcement αφB is exactly the meter-integrity guarantee the companion papers assume; truthful reporting is weakly dominant on the binding frontier and strict under an arbitrarily small slack, so the reported quantity equals the dispatched quantity, discharging that assumption from primitives and closing the stack at its base (Proposition 3). Full proofs are in the online appendix.
Prosumer communities, aggregations of residential and commercial entities equipped with distributed energy resources (DER), including photovoltaic systems, battery storage, and flexible loads, are emerging as critical organizational units in decarbonising smart grid architectures. Managing these communities effectively requires balancing economic efficiency with equity, autonomy, and environmental sustainability, objectives that conventional centralized control methods and existing multi-agent reinforcement learning (MARL) implementations fail to address simultaneously. This article proposes a value-aligned hierarchical multi-agent reinforcement learning (VA-HMARL) framework as a formally unified architecture that embeds equity (Jain’s Fairness Index J ≥ 0.90), individual autonomy, and carbon sustainability as hard constraints within the MARL reward structure. The framework integrates: a multi-objective Value Alignment Module (VAM) combining economic, fairness, sustainability, and comfort objectives; attention-based implicit coordination for scalable agent interaction; and differentially private federated policy aggregation (ε = 1.0, δ = 10−5) for GDPR-compliant collaborative learning. Simulation on a 20-prosumer community modelled on the IEEE 33-bus feeder over 10 Monte Carlo runs (300 episodes each) demonstrates: a 6.2% energy cost reduction versus the Rule-Based baseline (p = 0.0004); a Jain’s Fairness Index of 0.912 ± 0.031 at policy convergence (final 50 episodes), satisfying the J ≥ 0.90 community equity floor; and an 18.0% reduction in CO2 emissions. The economic efficiency trade-off relative to performance-optimized MARL baselines is limited to 2.4%, within the 5% design target. These results establish VA-HMARL as a technically feasible and ethically grounded paradigm for autonomous decentralized energy governance.
This chapter proposes a novel multi-factor authentication (MFA) with six schemes, namely password salting/hashing, non-interactive zero-knowledge (NIZK) proofs, GPS-based validation, time-based one-time passwords (TOTP), DNA cryptography, and lightweight SPECK ciphers. Taken together, these elements address the deficiencies in prior authentication and achieve a tradeoff between security and computational efficiency. The system is verified by theoretical and experimental methods. Furthermore, it is theoretically examined under the Real-or-Random model (RoR) with generative/explainable Artificial Intelligence (AI)-driven cybersecurity and provides strong security guarantees in terms of unpredictability (even if reduced in certain security parameters) and defends against replay, insider misuse, brute-force key search attacks, as well as spoofing ones. The solution is developed in Java, and the system is empirically evaluated by performing 100 runs to examine essential performance features: randomness, determinism, stability, and scalability.
Manuel Lagos Rodríguez, Hilda Romero Velo, Álvaro Leitao Rodríguez, Javier Pereira Loureiro · 5 authors
Ethereum use as a decentralized platform for executing smart contracts has driven the adoption of standards that optimize interoperability in industrial environments. Ethereum Requests for Comments (ERC) establish uniform patterns for smart contracts, facilitating their integration and operation in network nodes, which are essential for industrial applications such as supply chain management or process automation. However, this standardization can propagate vulnerabilities or inefficiencies in critical systems if the contracts are not optimized, affecting the reliability of industrial processes. Since operations in Ethereum consume computational resources (measured in gas) with associated economic costs, poor design can lead to significant losses in industrial settings. This paper evaluates the efficiency and security of ERC standards by examining their functional diversity and technical complexity. Thus, it analyzes existing implementations to identify common errors and proposes improvements to enhance the robustness and optimization of three of the most popular standards: ERC-20, ERC-1400 and ERC-3643. The ultimate goal is to support the effective adoption of ERCs in industrial applications. Considering the Ethereum network incentives on lower complexity logic and the obtained results, it is advisable to use simple standards, which also reduce error risks and ease maintainability.
Smart cities require the efficient and secure integration of key infrastructure domains such as water, energy, transportation, smart lighting and waste management deploying a myriad of data-generating IoT sensors and devices. Current management systems feature single points of failure, lack of auditability, insufficient privacy protection and lack of scalability as IoT nodes increase in density. In this paper, we present SmartChain, a three-tier multilayered blockchain based architecture that incorporates a permissioned distributed ledger, an AIbased anomaly detection module (ADM) and a dual-layered privacy preservation approach that combines Zero-Knowledge Proofs (ZKP) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE). SmartChain is tested on a large dataset - 2000 timestamped transactions involving Smart Cities’ five infrastructure types across several zones of a city. The results show a mean throughput of 3,421 transactions per second (TPS), a mean transaction latency of 3,847 milliseconds and a mean privacy score of 82.4 out of 100. The machine learning based anomaly module produces an F1-Score of over 97% and an AUROC score of 0.991 with Random Forest as the classifier. Benchmarking against Hyperledger Fabric 2.5, Ethereum 2.0 and the IOTA Tangle demonstrate the scalability, security, privacy and efficiency of SmartChain. This research renders SmartChain a practical production-level platform for management of new smart city infrastructure.
Mark C. Ballandies, Florian Spychiger, Uwe Serdült, Claudio J. Tessone
We propose DAO-enabled decentralized physical AI (DePAI), a democratic architecture for coordinating humans and autonomous machines in the operation and governance of physical-digital systems. We (1) synthesize foundations in blockchains, decentralized autonomous organizations (DAOs), and cryptoeconomics; (2) connect DAO design with digital-democracy research on deliberation and voting, showing how each can advance the other; (3) position DAO-governed decentralized physical infrastructure networks (DePIN) within a vertically integrated stack that links energy and sensing to connectivity, storage/compute, models, and robots; (4) show how these elements specify workflows that couple machine execution with human oversight, enabling enhanced self-organization of techno-socio-economic systems, which we call DePAI; and (5) analyze risks, including security, centralization, incentive failure, legal exposure, and the crowding-out of intrinsic motivation, and argue for value-sensitive design and continuously adaptive governance. DePAI offers a path to scalable, resilient self-organization that integrates physical infrastructure, AI, and community ownership under transparent rules, on-chain incentives, and permissionless participation, aiming to preserve human autonomy.
Abstract The use of blockchain technology and smart contracts is progressively spreading to real-world applications, especially to those that deal with critical services, such as water management systems. In fact, these applications can exploit the immutability, enforceability, and trustworthiness of such technology while promoting a more reliable, tamper-resistant way to collect and store data. At the same time, the development of decentralized applications poses challenges with respect to scalability, efficiency, management, and security. To address these problems, some design patterns, such as the factory pattern, have been proposed in the literature, to deal with modularity and scalability; at the same time, these proposed solutions apply some common concepts of role-based access control (RBAC), by adapting them to the smart contract context. This paper extends prior work by extending the definition of a hierarchical factory pattern, enhanced with multirole authentication and authorization capabilities, and applying it in the context of a water management system. It provides an extensive description of both advantages and disadvantages of this solution, discussing why the ability to instantiate a hierarchical family of contracts is essential in some application domains, and how a finer management of dynamic roles and permissions can be achieved in this kind of design. This paper also performs an extensive analysis of the performance and scalability capabilities of the proposed solution, and discusses some security aspects by considering its ability to overcome certain security attacks.