The rapid proliferation of cloud storage services necessitates robust mechanisms for verifying data integrity without requiring complete data retrieval. Traditional Provable Data Possession schemes face significant challenges in achieving simultaneous decentralization, privacy preservation, and efficient dynamic data handling. We present a novel framework that integrates blockchain technology with zero-knowledge cryptography to comprehensively address these limitations. Our approach employs Ethereum smart contracts for decentralized verification orchestration, Groth16 zk-SNARKs for privacy-preserving proof generation, and IPFS for distributed metadata management. The system architecture features a hierarchical Merkle tree authentication structure combined with BLS signature aggregation, achieving logarithmic verification complexity that is independent of the dataset size. By leveraging blockchain's immutable ledger properties, we eliminate single points of failure inherent in centralized third-party auditor models while ensuring complete audit trail transparency. The protocol supports dynamic data operations, including insertions, deletions, and modifications, through efficient cryptographic re-authentication mechanisms. We implement homomorphic encryption to enable verification on encrypted data, ensuring cloud providers never access plain-text information. Experimental evaluation on realistic datasets demonstrates a 43% reduction in computational overhead, a 67% decrease in communication costs, and 99.9% verification accuracy compared to existing approaches. When processing 10,000 data blocks totalling 1 GB, our system achieves a 2.3 -second average verification time with only 1.2% storage overhead. Formal security analysis proves correctness, soundness under computational hardness assumptions, and zero-knowledge privacy guarantees.
O presente artigo investiga como o tamanho do bloco afeta a propagação em redes blockchain, recorrendo à modelagem estocástica para quantificar os trade-offs entre throughput, segurança e descentralização. Estudos teóricos e empíricos indicam que blocos maiores elevam o tempo médio de propagação e a variância desse tempo, aumentando a probabilidade de forks e de blocos órfãos em mecanismos de consenso baseados em Prova de Trabalho (PoW) e variantes de Nakamoto. Modelos analíticos e de simulação demonstram que a relação entre o intervalo médio de geração de blocos e o atraso médio de propagação pode ser tratada por meio de sistemas de filas ou de processos de Poisson, nos quais a taxa de forks cresce quando o produto “taxa de blocos × atraso de propagação” se aproxima de um limiar crítico associado a um regime congestionado. Resultados de trabalhos de otimização de tamanho de bloco em PoW sugerem a existência de um tamanho “ótimo” que maximiza a eficiência econômica da rede – isto é, transações por segundo ponderadas pelo risco de órfãos –, e que esse ótimo depende fortemente da largura de banda média da rede e do grau de heterogeneidade entre nós. Evidências empíricas da rede Bitcoin mostram ainda que melhorias de protocolo, tais como Compact Blocks e redes de relay dedicadas, reduzem significativamente o impacto negativo de blocos maiores sobre a propagação, conquanto não eliminem o viés estrutural em favor de nós com melhor conectividade. Conclui-se que a modelagem estocástica do impacto do tamanho de bloco é fundamental para parametrizar blockchains de modo a manter a rede em regime funcional, minimizando taxa de forks e força centralizadora, ao mesmo tempo em que se atende à demanda por maior capacidade transacional na Web3.Blockchain
O presente artigo analisa a dicotomia entre descentralização teórica e descentralização real em redes blockchain, com foco nas métricas de distribuição de nós validadores e de poder de voto. O objetivo é investigar em que medida os fundamentos técnicos e econômicos dos mecanismos de consenso refletem, de fato, uma distribuição ampla de controle, ou se concentram poder em poucos agentes, contrariando as promessas de infraestrutura verdadeiramente distribuída. A metodologia adotada baseia-se em revisão bibliográfica de trabalhos recentes sobre descentralização em consenso Prova de Participação (Proof-of-Stake – PoS) e Prova de Trabalho (Proof-of-Work – PoW), em estudos de caso empíricos que medem coeficiente de Nakamoto, índices de Gini e Herfindahl-Hirschman (HHI), além de relatórios sobre distribuição geográfica e por provedores de validadores em redes como a Solana. Os resultados obtidos indicam que métricas superficiais, a exemplo da simples contagem de nós, podem mascarar riscos sistêmicos: em diversas redes PoS, um conjunto relativamente pequeno de validadores, países e provedores de infraestrutura controla fração substancial do stake, de forma que poucas entidades seriam suficientes para censurar transações ou comprometer a liveness da rede. Estudos recentes sobre consenso PoS mostram ainda que modelos de ponderação de stake alternativos – como Square Root Stake Weight (SRSW) e Logarithmic Stake Weight (LSW) – podem melhorar, em média, 51% e 132% as métricas de descentralização (Nakamoto, Gini, HHI), sugerindo caminhos concretos para tornar a distribuição de poder mais equitativa. Conclui-se que a descentralização real exige métricas multidimensionais que incorporem stake, geografia, infraestrutura e diversidade de clientes, e que o desenho de protocolos e políticas de governança precisa considerar explicitamente esses indicadores para alinhar a prática ao ideal normativo de descentralização da Web3.
Carlos Melo, Glauber Gon§Alves, Francisco A. Silva, André Soares · 7 authors
Leading blockchain platforms like Ethereum face significant scalability challenges while striving to balance high transaction throughput, security, and decentralization. Layer-2 solutions, especially Zero-Knowledge (ZK) Rollups, tackle this issue by processing transactions off-chain and submitting concise validity proofs to the main network. This approach preserves security while improving throughput. Despite these technical advantages, optimizing the underlying infrastructure remains a complex task for node operators. This paper presents a formal modeling approach using Stochastic Petri Nets (SPNs) to assess the operational behavior of ZK-Rollups, particularly addressing deployment challenges in public cloud environments. The proposed model is calibrated using empirical parameters from the zkSync Era protocol to ensure realistic performance representation. Using a Design of Experiments (DoE) methodology, we analyze key infrastructure metrics systematically, including resource utilization, transaction wait times, and daily operational costs under various system configurations. Case studies demonstrate that increasing Layer-2 transaction volume can reduce service providers’ daily operational costs by up to $\mathbf{2 5 \%}$ through more efficient batch usage. However, results indicate a significant tradeoff between cost efficiency and responsiveness, as larger batch sizes may double user waiting times. Additionally, we analyze the relationship between server utilization and capacity, demonstrating that typical deployments are frequently overprovisioned and compromise system stability.
O presente artigo analisa a gestão de estado em blockchains públicas, com foco em técnicas de pruning e estratégias de arquivamento eficiente, diante do crescimento contínuo de dados e da necessidade de equilibrar segurança, disponibilidade histórica e custos de infraestrutura. Em redes baseadas em máquinas de estado, como aquelas compatíveis com a Ethereum Virtual Machine, o estado global – composto por contas, contratos e dados de armazenamento – é mantido em estruturas de dados do tipo árvore de Merkle-Patricia (MPT), que crescem monotonicamente à medida que novas transações modificam o estado, levando nós completos e de arquivo a consumirem dezenas de terabytes após alguns anos de operação em produção. Essa dinâmica impõe desafios estruturais a operadores de nós, que precisam escolher entre manter histórico completo, realizar pruning de blocos e estados antigos ou recorrer a nós especializados para consultas históricas, afetando diretamente o grau de descentralização e o custo de participação na rede. A metodologia adotada combina revisão bibliográfica de pesquisas em bancos de dados para blockchains, documentação técnica de clientes de nós – entre os quais Geth, Erigon e equivalentes em outras redes – e estudos recentes sobre statelessness, expiração de estado (state expiry), árvores de Verkle e bancos de dados forkless, discutindo as implicações dessas abordagens para a gestão de estado de longo prazo. Os resultados indicam que estratégias de pruning ao nível de blocos e de estado, combinadas a modelos de nós diferenciados (full, pruned, archive, light) e a técnicas de instantâneos (snapshotting) e arquivamento externo, permitem reduzir significativamente o volume de armazenamento exigido de nós validadores sem sacrificar a capacidade de validação e a segurança da cadeia. Por outro lado, a dependência crescente de archive nodes e de infraestruturas especializadas para consultas históricas levanta questões relevantes acerca de centralização e do custo de reconstrução do estado em cenários adversos, estimulando pesquisa em modelos de clientes sem estado (stateless clients), provas compactas de estado e bancos de dados desenhados especificamente para as cargas de trabalho de blockchains. Conclui-se que a gestão eficiente de estado constitui componente crítico da sustentabilidade de longo prazo da Web3, demandando abordagens integradas que combinem técnicas de pruning, desenho criterioso de estruturas de dados, políticas explícitas de retenção histórica e modelos econômicos que incentivem a operação de nós com diferentes perfis de armazenamento.
O presente artigo examina técnicas de compressão de dados em blocos de blockchain e sua relação com a redução de custos de armazenamento on-chain, considerando tanto o consumo de espaço em disco pelos nós da rede quanto o custo econômico de inclusão de dados em transações. Em plataformas como o Ethereum, parcela significativa do custo de transações – especialmente para rollups e aplicações que utilizam calldata de maneira extensiva – está associada ao armazenamento e à propagação de bytes de dados na camada base, o que tem motivado a adoção de estratégias de compressão de calldata, ajuste de parâmetros de gas e a introdução de novas formas de armazenamento temporário, a exemplo de blobs de dados. A metodologia empregada baseia-se em revisão bibliográfica de trabalhos sobre estratégias de armazenamento on-chain, análise de propostas de melhoria do ecossistema (Ethereum Improvement Proposals – EIPs) voltadas à redução de custos de calldata, exame de relatórios técnicos sobre compressão de dados em soluções de camada 2 e análise recente do impacto de mecanismos como o EIP-4844 (proto-danksharding) sobre a estrutura de custos de rollups. As evidências levantadas indicam que técnicas de compressão aplicadas ao payload de transações podem reduzir da ordem de cinco vezes o tamanho efetivo dos dados enviados, traduzindo-se em economias de aproximadamente 50% no gasto de gas para determinadas operações em redes de segunda camada, sem exigir mudanças disruptivas no protocolo subjacente. Paralelamente, abordagens estruturais – entre as quais a separação de dados de disponibilidade em blobs não permanentes e o emprego de rollups que publicam apenas provas e raízes de estado, em vez de dados completos – contribuem para reduzir a pressão de armazenamento permanente sobre os nós da camada 1. Conclui-se que a compressão de dados em blocos, combinada a ajustes de modelo de dados (blobs, rollups, off-chain storage) e a técnicas de compressão clássicas (run-length, delta, dicionário, entropia), constitui peça central na estratégia de escalabilidade e sustentabilidade econômica da Web3, não obstante levante desafios relevantes quanto à complexidade de implementação, compatibilidade entre clientes e preservação da verificabilidade de longo prazo.
Transaction flow networks are crucial in detecting illicit activities such as wash trading, credit card fraud, cashback arbitrage fraud, and money laundering. Our collaborator, Grab, a leader in digital payments in Southeast Asia, faces increasingly sophisticated fraud patterns in its transaction flow networks. In industry settings such as Grab's fraud detection pipeline, identifying fraudulent activities heavily relies on detecting dense flows within transaction networks. Motivated by this practical foundation, we propose theS-T densest flow(STDF) query. Given a transaction flow networkG, a source setS, a sink setT, and a size thresholdk, the query outputs subsets$S^{\prime}\subseteq S$and$T^{\prime}\subseteq T$such that the maximum flow from$S^{\prime}$to$T^{\prime}$is densest, with$\vert S^{\prime}\cup T^{\prime}\vert\geq k$. Recognizing the NP-hardness of the STDF query, we develop an efficient divide-and-conquer algorithm,$\mathsf{Conan}$. Driven by industry needs for scalable and efficient solutions, we introduce an approximate flow-peeling algorithm to optimize the performance of$\mathsf{Conan}$, enhancing its efficiency in processing large transaction networks. Our approach has been integrated into Grab's fraud detection scenario, resulting in significant improvements in identifying fraudulent activities. Experiments show that$\mathsf{Conan}$, outperforms baseline methods by up to three orders of magnitude in runtime and more effectively identifies the densest flows. We showcase$\mathsf{Conan}$'s applications in fraud detection on transaction flow networks from our industry partner, Grab, and on non-fungible tokens (NFTs).
Today, many computing workloads are executed in loosely coupled, geographically distributed environments where resources are owned by different organizations. Examples include inter-institutional research infrastructures, community-operated clusters, and edge deployments. As disconnections are frequent in such environments, ensuring reliable task execution remains a fundamental challenge. Kubernetes, the de facto standard for cluster orchestration, provides centralized control and strong consistency, but suffers from slow recovery when node failures occur frequently. At the opposite extreme, blockchain-based orchestration removes centralized control but incurs substantial latency due to global consensus, making it unsuitable for time-sensitive task scheduling. This paper presents Mutual Cloud, a decentralized orchestration framework that operates between these two extremes. Mutual Cloud adopts a hybrid architecture where task admission and queue management are handled in a centralized manner similar to conventional public clouds, whereas most scheduling functions, including execution-node selection and failure handling, are performed in a decentralized manner by autonomous agents using a distributed hash table. We implement a prototype of Mutual Cloud and evaluate its performance through large-scale simulation studies. The results show that Mutual Cloud maintains stable performance comparable to centralized baselines under normal conditions while achieving approximately five-second-level recovery latency under substantial node failures.
Cloud storage systems have become an integral part of modern data management by enabling users to store and access data remotely.However, traditional cloud storage architectures rely on centralized servers, which introduce several critical challenges such as single-point failure, redundant data storage, increased storage costs, and security vulnerabilities.In earlier systems, data was stored in centralized data centers where duplicate files were repeatedly maintained, leading to inefficient utilization of storage resources.Although basic deduplication methods were implemented, they often compromised data confidentiality and lacked transparency in metadata management.Furthermore, failure of the central server could result in permanent data loss.To overcome these issues, this research proposes the Blockchain-enabled Heuristic Optimized Deduplication Model (BHODM), which integrates blockchain technology, InterPlanetary File System (IPFS), Convergent Encryption (CE), and heuristic-based chunking techniques.In this model, files are divided into optimized chunks based on file size using a heuristic approach.Each chunk is encrypted using CE, where the encryption key is derived from the hash of the data itself, allowing secure deduplication without exposing plaintext data.Duplicate chunks are identified through hash comparison, ensuring that only unique data is stored.The encrypted chunks are stored in IPFS, a decentralized peer-to-peer storage network, while metadata such as file names, block numbers, and hash values are securely maintained in an Ethereum blockchain smart contract, ensuring immutability and transparency.The system is implemented using Django, Web3, IPFS API, and AES-CTR encryption.Experimental results based on storage utilization and computation time demonstrate improved efficiency over traditional approaches.
Yuqin Wang, Zhengkang Du, Jiannong Cao, Shan Jiang
The proliferation of web3 has underscored the need for robust and efficient decentralized data infrastructure. However, prominent decentralized storage systems like IPFS suffer from poor data availability and the risk of cold data loss, while incentive-based solutions like Filecoin and Storj employ uniform replication strategies that are often inefficient and costly. These systems fail to distinguish between frequently accessed ("hot") and infrequently accessed ("cold") data, leading to a suboptimal allocation of storage resources. This paper introduces TRep, a novel Temperature-aware Data Replication strategy designed to address these challenges. TRep implements a dynamic replication policy by classifying data based on its access temperature. The system is built on three key innovations: (1) a highly accurate data classification algorithm using double exponential smoothing to analyze access frequency and trends; (2) an intelligent data distribution strategy that selects optimal backup nodes based on storage availability, online stability, and network proximity; and (3) a dynamic backup regulation mechanism, managed by a regulator node, to globally control replica counts for hot and cold data. Our performance evaluation demonstrates that TRep's classification algorithm significantly reduces prediction errors compared to traditional methods. Furthermore, TRep achieves speeds up to four times faster than IPFS for large files while effectively managing storage overhead. TRep thus provides a flexible and performant framework for data replication, enhancing the durability and efficiency of web3 data infrastructure.
The escalating energy consumption of blockchain networks has intensified concerns regarding their environmental sustainability, particularly in consensus protocols derived from Proof of Work. Although Proof of Stake improves efficiency, existing mechanisms remain static and lack responsiveness to dynamic network and energy conditions. This paper presents an Energy-Adaptive Consensus Mechanism (EACM) that integrates real-time workload awareness with energy-sensitive validator selection to optimize power utilization without compromising security. The proposed model introduces a multi-factor adaptive control layer that adjusts validation intensity based on transaction throughput, node availability, and energy profiles. A carbon awareness incentive function is incorporated to prioritize validators operating on renewable or low-carbon energy sources. Prototype implementation is developed on a private Ethereum-based test network, and comparative experiments are conducted against conventional Proof of Stake under variable workloads. Results indicate measurable reductions in energy consumption while maintaining competitive throughput, latency, and fault tolerance. The findings demonstrate that adaptive consensus design can enhance blockchain sustainability and provide a viable pathway toward carbon-efficient distributed ledger infrastructures.
This paper presents SAYMAN, a production-architected educational blockchain framework designed to enable controlled decentralization for learning ecosystems and digital academic infrastructure. Traditional blockchain systems prioritize full decentralization, often introducing scalability, governance, and usability challenges that limit adoption in education-focused environments. SAYMAN proposes a hybrid architectural model combining permissioned governance layers with selectively decentralized components to balance transparency, institutional control, and operational efficiency. The framework introduces modular consensus orchestration, identity-anchored participation, and configurable trust boundaries, allowing institutions, developers, and learners to interact within a verifiable yet manageable distributed system. Unlike conventional public chains, SAYMAN emphasizes educational deployment readiness, low operational overhead, and adaptable governance policies suitable for academic credentialing, collaborative research environments, and decentralized learning platforms. This work outlines the architectural principles, system design considerations, and implementation roadmap of the SAYMAN blockchain, positioning it as a practical foundation for next-generation educational Web3 infrastructure. The proposed model demonstrates how hybrid blockchain architectures can bridge centralized institutional requirements with decentralized innovation, enabling scalable and secure digital education ecosystems.
The projected arrival of cryptographically relevant quantum computers (CRQCs) between 2030 and 2035 poses a structural threat to blockchain infrastructure built on classical elliptic-curve cryptography. Shor's algorithm reduces the security of ECDSA-256, the signature scheme underpinning the majority of production blockchains, from approximately $2^{128}$ classical operations to $O(2^{24})$ quantum operations, rendering it categorically broken in the post-quantum era. The "Harvest Now, Decrypt Later" attack vector compounds this risk: adversaries collecting signed transactions today can retroactively extract private keys once quantum hardware matures, exposing all assets whose public keys have been revealed on-chain. This paper presents QoreChain, a Layer~1 blockchain platform designed from first principles to operate in a post-quantum world. QoreChain integrates three foundational capabilities into a single protocol stack: (1)~full-stack post-quantum cryptography implementing NIST-standardised algorithms (ML-DSA-87 per FIPS~204, ML-KEM-1024 per FIPS~203, SLH-DSA per FIPS~205, and SHAKE-256) at FIPS Security Level~5 across every protocol layer, from transaction signing and consensus messaging to cross-chain bridge attestations; (2)~an AI-native intelligence layer (QCAI) that applies reinforcement learning to consensus parameter optimisation, graph neural networks to anomaly detection, and multi-objective optimisation to transaction routing; and (3)~a triple virtual machine execution environment supporting EVM, CosmWasm, and SVM within a unified state model with atomic cross-VM call semantics and full rollback guarantees. The consensus mechanism, Combined Proof of Stake (CPoS), merges Reputation PoS, Delegated PoS, and classical PoS with BFT finality. A five-way fee distribution (37\% validators, 30\% burned, 20\% treasury, 10\% stakers, 3\% light nodes) aligns incentives across all participant classes. Governance employs Quadratic Delegation with Reputation Weighting (QDRW), for which we present formal game-theoretic analysis demonstrating bounded resistance to plutocratic capture (voting power scales sub-linearly with stake) and flash-loan manipulation (reputation updates lag delegation by one block finality cycle). Cross-chain interoperability is provided by the QoreChain Bridge (QCB), connecting directly to 25 Layer~1 blockchains with over 120 additional networks reachable via IBC. All bridge operations are secured by ML-DSA-87 multi-attestation with QCAI anomaly detection and circuit breaker mechanisms. A multi-layer scaling architecture incorporating sidechains, paychains, and a Rollup Development Kit (RDK) enables horizontal throughput expansion while inheriting the main chain's quantum-safe settlement guarantees. The QOR token has a fixed supply of 4,500,000,000 with epoch-based emissions following a halving schedule. The architecture is designed for 5,000+ transactions per second with sub-second finality; multi-node testnet benchmarks are pending. QoreChain Association is incorporated under the Swiss DLT Act (CHE-484.963.998, Rolle) with formal FINMA utility token classification (January 2026). Testnet is operational (chain ID: \texttt{qorechain-diana}) with 47 genesis modules. Mainnet launch is targeted for Q4~2026. The full specification spans 16 chapters and 351 pages, presenting 530 formal equations, 78 data tables, and 9 architectural diagrams covering cryptographic foundations, AI integration, smart contract execution, consensus, tokenomics, governance, interoperability, and regulatory compliance.
Cloud storage systems have become an essential component of modern data management, enabling users to store and access data remotely. However, traditional cloud storage architectures rely on centralized servers, which introduce critical challenges such as single-point failure, redundant data storage, high storage costs, and security vulnerabilities. In earlier systems, data was stored in centralized data centers where duplicate files were often saved multiple times, leading to inefficient utilization of storage resources. Although basic deduplication techniques were used, they frequently compromised data confidentiality and lacked transparency in metadata management. Moreover, failure of the central server could result in permanent data loss. To overcome these limitations, this research system integrates blockchain technology, InterPlanetary File System (IPFS), Convergent Encryption (CE), and heuristic-based chunking techniques to create a secure and decentralized storage framework, hereafter named Blockchain-enabled Heuristic Optimized Deduplication Model (BHODM). In this system, files are divided into optimized chunks using a heuristic method based on file size. Each chunk undergoes CE, where the encryption key is derived from the hash of the data itself, enabling secure deduplication without exposing plaintext information. Duplicate chunks are identified using hash comparison, ensuring that only unique data is stored. The encrypted chunks are stored in IPFS, a decentralized peer-to-peer storage network that eliminates reliance on a single server. Metadata such as file names, block numbers, and hash values are securely stored in an Ethereum blockchain smart contract, ensuring immutability and transparency. The system is implemented using Django for the web application, Web3 for blockchain interaction, IPFS Application Program Interface (API) for distributed storage, and Advanced Encryption Standard in Counter Mode (AES-CTR) encryption for security. By combining decentralized storage, blockchainbased metadata management, and secure deduplication, the proposed model effectively reduces storage overhead, enhances data integrity, and mitigates single-point failures. The system is further evaluated using storage utilization and computation time analysis, demonstrating improved efficiency compared to traditional approaches
A six-part study proposing a deterministic computing architecture based on Quantum Thought Circuit OS ASI. It integrates heterogeneous self-optimizing hardware, energy-circulating communication, hardware-rooted trust, adaptive inference control, distributed infrastructure, and heterogeneous TEE confidential computing to improve efficiency, resilience, compliance, security, and scalability.
Jose Almarcha-Sanchez, Maria-Jesus Alba-Baena, Volodymyr Dubetskyy, Maria‐Dolores Cano
Abstract Open-source simulators let engineers stress-test blockchain ideas long before field deployment, yet few studies compare tools side-by-side. This tutorial article benchmarks two research-grade simulators, namely, BlockSim and Simewu, and the production-grade IOTA Hornet node under an identical traffic harness that runs on laptop-class hardware. Results show that consensus style dominates capacity. A DAG ledger that finalizes one milestone per second (≈ 6 tx s⁻¹) surpasses the 10 Transactions Per Second (TPS) ceiling of a six-node Bitcoin simulation, while Ethereum-style 12 s blocks lift the same mesh to approximately ~ 20TPS.BlockSim reproduces proof-of-work fairness within ± 3% of theoretical expectations, and a ten-fold increase in propagation delay cuts a miner’s reward roughly in half despite equal hash power. Hornet delivers protocol-truth execution, but at noticeably higher CPU, memory and bandwidth cost than the simulators. All scripts, Docker files and raw logs are released under an open license, providing a one-click baseline for future benchmarking of new distributed-ledger technologies.
Web3 has gained increasing attention in recent years as a paradigm that aims to establish a new phase of the Internet by enabling a decentralized web infrastructure without reliance on centralized authorities or intermediaries. A fundamental difference between Web3 decentralized applications (dApps) and traditional Web2 applications lies in their underlying architecture and trust model. Web3 dApps rely on blockchain networks and smart contracts to execute application logic and manage shared state. This paper presents a systematic literature review that examines the architectural components, architectural patterns, and development challenges of Web3 decentralized applications. A set of peer-reviewed research articles was analyzed to provide a structured overview of current research and practice. The results identify the core building blocks of Web3 dApps, as well as highlighting the most adopted architectural styles, with a particular emphasis on hybrid on-chain/off-chain and fully decentralized architectures. Furthermore, this study synthesizes the most frequently reported challenges in Web3 dApp development. By systematizing existing knowledge, this work contributes to a clearer understanding of Web3 dApp architectures and provides a foundation for future research and improved engineering practices in decentralized application development.
Ashkan Safari, Amir Aminzadeh Ghavifekr, Amir Rikhtegar Ghiasi
• A private Ethereum-based discrete-event blockchain is developed for P2P energy trading. • Smart contracts using Solidity automate market matching, settlement, and tokenization. • Platform integrates ERC-20 token framework to support secure energy transactions. • Gas fee modeling and minimization are implemented for cost-efficient operations. • Validated on IEEE 14-Bus multi-community system with real dynamic market behavior. Due to the fast growth in renewable energy production, which enables households to sell excess power directly and better manage its intermittent nature, the Peer-to-Peer (P2P) energy market has become considerably more established, as it’s aligned with the decentralization and digitalization of power systems and local markets. It’s a system that lets energy consumers and producers trade energy directly with one another. Furthermore, the presence of blockchain technology increases these techno-economic advantages for energy systems, particularly when integrated with P2P energy trading. Consequently, a wide range of works have considered the integration of P2P and blockchain; however, few of them have investigated the full details of this system, including its performance, Transaction (TX) gas fee in a secure and private platform. Following this, the proposed work presents an Ethereum-based discrete event Private blockchain and its integration with P2P energy trading market in a Multi-Community Energy System (MCES). Considered on an IEEE 14-Bus MCES with 3 communities and 20 participating agents (11 consumers, 5 generators, and 4 not participating in the market), the platform uses Web3 and Ethereum Virtual Machine (EVM) for execution. Smart contracts, written in Solidity, handle tokenization by Ethereum Request for Comment 20 (ERC-20) standards and market matching/settlement discrete event processes. On the secure performance, the proposed platform is based on Keccak-256 for immutability, while TX gas fees are minimized. Results show synchronized peak demands up to 60 (MW), diurnal Renewable Energy Sources (RES) outputs peaking at 40 (MW), alongside the market prices, and agents’ revenues. Finally, the reliability of the platform is evaluated based on two main metrics of Transaction Success Rate (TSR) = 1 (100%) and Transaction Per Second (TPS) = 3.29, with a primary mode centered at 1.8–2.0 TPS, a secondary peak at 4.0–4.2 TPS.
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.
The increasingly complex Web3 ecosystem and decentralized finance (DeFi) landscape demand ever higher levels of technical expertise and financial literacy from participants. The Intent-Centric paradigm in DeFi has thus emerged in response, which allows users to focus on their trading intents rather than the underlying execution details. However, existing approaches, including Typed-intent design and LLM-driven solver, trade off expressiveness, trust, privacy, and composability. We present OMNIINTENT, a language-runtime co-design that reconciles these requirements. OMNIINTENT introduces ICL, a domain-specific Intent-Centric Language for precise yet flexible specification of triggers, actions, and runtime constraints; a Trusted Execution Environment (TEE)-based compiler that compiles intents into signed, state-bound transactions inside an enclave; and an execution optimizer that constructs transaction dependency graphs for safe parallel batch submission and a mempool-aware feasibility checker that predicts execution outcomes. Our full-stack prototype processes diverse DeFi scenarios, achieving 89.6% intent coverage, up to 7.3x throughput speedup via parallel execution, and feasibility-prediction accuracy up to 99.2% with low latency.
The growing implementation of blockchain technology across various application areas has made the need for energy-efficient and scalable consensus mechanisms more pressing. Conventional consensus protocols like Proof of Work (PoW), although secure in nature, have high energy expenditures and are limited in scalability. This paper introduces a new hybrid consensus algorithm that merges Proof of Stake (PoS) and Proof of Elapsed Time (PoET) to overcome such limitations. The new method leverages the deterministic stake-based leader election of PoS and the low-energy time-based leader election facilitated by PoET’s utilization of Trusted Execution Environments (TEEs). This fusion enables an optimal balance between energy efficiency, security, and decentralization. A systematic design of the hybrid mechanism is provided, and then analytical performance comparison with standard PoW, PoS-only, and PoET-only models is presented. The results indicate that the hybrid model has significant energy consumption and latency decreases, making it a perfect candidate for implementation within resource-constrained environments such as agricultural and healthcare digital twin infrastructures. The paper ends by emphasizing the potential of the suggested consensus model to facilitate sustainable blockchain ecosystems.
ABSTRACT Deterministic execution models used in distributed systems and generative pipelines share a common weakness, fixed concurrency schedules that fail under skewed workloads, hotspots, or noisy inputs. This paper proposes Adaptive Time stamp Informed Deterministic Flow Control (ATDFC), a unified concurrency control framework that combines time stamp‐based ordering with adaptive deterministic flow execution. ATDFC dynamically adjusts segmentation, initialization, and replay based on conflict intensity or input quality, enabling deterministic correctness while eliminating unnecessary computation. Experimental evaluation shows that ATDFC reduces conflict rates by more than half, lowers bandwidth consumption significantly, and maintains higher throughput and lower latency under extreme skew when compared to leading deterministic flow and concurrency control approaches. The results demonstrate that ATDFC offers a domain‐agnostic concurrency model capable of unifying deterministic computation across distributed ledgers, databases, and multimodal generative systems.
Nach dem anfänglichen Hype um die Blockchain-Technologie, die erstmals durch Satoshi Nakamotos Bitcoin bekannt wurde, hat sich der Bereich in Richtung der Entwicklung ausgereifter Blockchain-basierter Systeme und Anwendungen weiterentwickelt. In dieser weitläufigen Landschaft fungieren die einzelnen Blockchain-Plattformen und Ökosysteme häufig als isolierte Silos, die strikt von anderen Plattformen getrennt sind und über keine inhärenten Interoperabilitätsfunktionen verfügen. Trotz der Existenz etablierter Mechanismen für den Austausch von Coins und Tokens über heterogene Blockchains hinweg müssen Entwickler von Web3-Anwendungen, die aus Smart Contracts bestehen, möglicherweise auf individuelle Anpassungen zurückgreifen, um Blockchain-übergreifende Anwendungen zu ermöglichen. In vielen Fällen sind diese Ansätze nicht ausreichend skalierbar, wenn die Anwendung auf zusätzlichen Blockchain-Plattformen verteilt werden muss. Folglich sind weitere Anpassungen erforderlich. Darüber hinaus stellt sich die Frage der Speicherung gemeinsamer Anwendungsdaten, die mit Smart Contracts kompatibel und für das dezentrale Konzept der Blockchain geeignet sein muss. Diese Arbeit präsentiert einen Vorschlag für eine Blockchain-übergreifende Datenspeicherlösung, die das InterPlanetary File System (IPFS) als dezentrale Off-Chain-Persistenzschicht und Blockchain-Oracles nutzt, um Lese- und Schreibvorgänge zu ermöglichen. Der Einsatz von incentivierten Vermittlern in Verbindung mit einem neuartigen Oracle-Verifizierungsmechanismus für Schreibzugriffe erlaubt die Formulierung eines Lösungsentwurfs für ein vollständig dezentrales System. Dieser Ansatz ermöglicht die lose gekoppelte Verbindung von Blockchain-übergreifenden Anwendungen, wobei die einzelnen Blockchain-Plattformen nicht direkt aufeinander zugreifen müssen. Wir präsentieren eine prototypische Implementierung des Lösungsentwurfs und bewerten anschließend den Prototyp hinsichtlich Kosten, Leistung und Sicherheit. Im Vergleich zu einer hypothetischen Referenzlösung, die eine zentralisierte Persistenzschicht verwendet, zeigen wir, dass vollständige Dezentralisierung die Betriebskosten und die Leistung sowie die Integrität der gemeinsam genutzten Daten erheblich negativ beeinträchtigt.
Stochastic Bit-Parallel Maximum Clique Solver (1024-bit Virtual Register) We introduce a stochastic bit-parallel solver for the Maximum Clique Problem (MCP) based on a 1024-bit virtual register architecture implemented as 16 contiguous uint64_t words in standard C++17, ensuring full portability across 64-bit platforms (x86-64, ARM, RISC-V). Core operations—candidate intersection, population count, and leading-zero detection—execute in exactly 16 instructions per 1024-bit operation. The solver integrates three key components: (i) a co-neighborhood heuristic that identifies high-coreness nodes via O(N²) pairwise popcount over 1024-bit adjacency rows; (ii) a stochastic swarm of independent worker threads; and (iii) greedy clique expansion through iterative bitwise intersection. Exact branch-and-bound solvers (MaxCliqueDyn, MCQ) become computationally intractable on dense random graphs such as G(1024, 0.5), where chromatic coloring bounds lose effectiveness and the search tree grows exponentially, requiring hours of computation on commodity hardware. The proposed method operates specifically within this hard regime, achieving 100% recovery of all 28 planted clique vertices in 153 milliseconds—a setting where exact state-of-the-art methods cannot remain competitive regardless of hardware scaling. Experimental validation was performed on a Qualcomm Snapdragon 8 Gen 2 (8-core ARM) and independently reproduced on Linux x86-64 server hardware. The solver requires no cloud infrastructure and no GPU acceleration. STATEMENT OF PRIOR ART AND LICENSE TERMS (PolyForm Noncommercial Framework) 1. Statement of Prior Art This document constitutes a public disclosure of the stochastic bit-parallel Maximum Clique methodology, including its virtual register architecture, heuristic structure, and execution model.The mathematical and algorithmic concepts are released solely to establish Prior Art and prevent third-party patent claims under 35 U.S.C. § 102 and international equivalents. 2. Software License While the conceptual methods are disclosed defensively, all source code, implementations, binaries, and hardware realizations are not in the public domain and are licensed under the PolyForm Noncommercial License 1.0.0. Permitted (Non-Commercial)• Academic research and experimentation• Peer review and independent verification• Educational and non-profit use• Non-commercial open-source research implementations Condition: Publications must cite the canonical DOI or primary reference. Prohibited (Commercial)• Integration into proprietary software or hardware• Deployment in commercial systems, services, or products• Use in paid tools, platforms, or consulting workflows• Sublicensing or sale of the code or derivatives 3. Commercial Licensing Any commercial use requires explicit written authorization from the author. 4. No Code-Size Threshold (No De Minimis) The PolyForm Noncommercial License imposes no exemptions based on code length, fragment size, or proportion of reuse. Any use—partial or complete—remains fully subject to the license. 5. Anti-Snippet Laundering and Anti-Circumvention Extraction, paraphrasing, refactoring, translation, or reimplementation of any algorithmic component—including bit-parallel structures, heuristics, or execution logic—shall be considered derivative use.Attempts to evade the license through minimal reuse, language changes, functional replication, or modular embedding do not limit its applicability.This interpretation aligns with international good-faith and anti-abuse principles. 6. Presumption of Derivation Any system exhibiting substantial functional or structural similarity, developed after exposure to this work, shall be presumed derivative.The burden of proof for independent creation rests on the alleged infringing party. 7. Knowledge Contamination Exposure to the code, documentation, or technical description constitutes knowledge contamination.Subsequent implementations by exposed parties are not considered clean-room unless supported by contemporaneous evidence of prior independent development. 8. Waiver of Jury Trial To the fullest extent permitted by law, all parties waive the right to a jury trial in disputes arising from this license or related use. 9. Severability and Survival If any provision is deemed unenforceable, the remaining provisions remain in effect.The following provisions survive termination: license scope, noncommercial restrictions, anti-circumvention, presumption of derivation, knowledge contamination, intellectual property ownership, waiver of jury trial, and remedies. 10. Academic Use and Research Freedom The author expressly encourages academic and scientific use of this work. The following activities are permitted on a non-commercial basis: • Research, benchmarking, and experimental validation• Publication of scientific analyses, comparisons, or extensions• Use in university courses, laboratories, and academic projects• Inclusion in research solver portfolios• Independent theoretical or empirical study All academic use must include proper citation to the original work.