Resource allocation in blockchain networks is an urgent issue due to changes in transaction load, networkcongestion, and the computational challenges associated with smart contract execution. Suboptimal resource utilization leads to high operational costs and reduced network performance. In this context, this paper presents a new hybrid algorithm for resource management in blockchain networks based on the integration of Q-learning reinforcement learning and the Ship Rescue Optimization (SRO) algorithm. The SRO algorithm is used to optimize the hyperparameters and the initial Q-learning policy, enabling the learning process to converge more effectively to an optimal solution and make better resource allocation decisions. We formulate the resource allocation problem as a Markov Decision Process (MDP), in which the agent learns optimal scaling policies for CPU, memory, and bandwidth resources. Comprehensive testing on an implemented blockchain network with 100 nodes and 245,782 transactions across 1,000 blocks shows significant improvements, including an average reduction of 38.76 % in CPU usage, 35.99% in RAM usage, and 38.51% in transaction latency, along with a 58.21% increase in throughput compared to baseline methods. All improvements are statistically significant (p 3.0) according to the statistical analysis. The ablation study shows that each component plays a significant role in the overall system performance. In particular, the proposed hybrid algorithm provides an additional 13.88% performance improvement compared to Q-learning alone and . Furthermore, the suggested framework outperforms existing methods, such as Deep Q-Networks, Genetic Algorithms, and Particle Swarm Optimization, establishing a new benchmark for blockchain resource allocation.
<b>RESUMO</b>O presente artigo analisa as estratégias de otimização de gas na rede Ethereum, focando na interação técnica entre os opcodes da Ethereum Virtual Machine (EVM) e a eficiência das estruturas de dados. Com a evolução da rede e a implementação de atualizações críticas como o EIP-1559 e o upgrade Dencun, a economia de recursos computacionais tornou-se um imperativo não apenas para a viabilidade financeira das transações, mas também para a escalabilidade e segurança de contratos inteligentes. O estudo detalha os custos associados às operações de armazenamento (Storage), memória volátil (Memory) e calldata, explorando o impacto de novas funcionalidades como o armazenamento transitório (EIP-1153). Através de uma revisão sistemática de literatura técnica e benchmarks algorítmicos, demonstra-se que a escolha criteriosa de tipos de dados, o empacotamento de variáveis (variable packing) e a substituição de padrões de iteração por mapeamentos podem reduzir significativamente o consumo de gas. Conclui-se que a otimização de alto nível deve ser acompanhada por uma compreensão profunda da arquitetura de baixo nível da EVM, assegurando que a redução de custos não comprometa a integridade lógica do sistema.<br>
Ethereum is a decentralized blockchain platform that allows developers to deploy and run smart contracts, which are self-executing programs responsible for handling digital transactions without intermediaries. ERC standards define how these smart contracts are expected to behave in the Ethereum ecosystem. When these rules are not implemented correctly, contracts may contain security weaknesses that can lead to financial loss or unexpected behavior. For this reason, verifying whether a contract complies with ERC requirements is an important task during the development process. However, compliance verification is still often performed manually, which makes the process slow and dependent on expert knowledge. Most existing static analysis tools mainly detect predefined vulnerability patterns, but they may miss behavioral deviations from Ethereum Request for Comments (ERC) specifications that are not explicitly encoded as patterns. In this study, we present a multi-agent LLM framework designed to automate ERC compliance auditing. The system extracts contract-specific code fragments and evaluates them using multiple independent Large Language Model agents. Their outputs are aggregated through a confidence-weighted mechanism that aims to stabilize the final decision. Experiments on ERC-20, ERC721, and ERC-1155 contracts show that the multi-agent configuration improves recall and reduces false negatives compared to single-agent auditing.
The growing demand for digital access to precious metals has created opportunities for ownership systems that are transparent, secure, and globally accessible. This article presents a blockchain-based silver tokenization framework enabling customers to buy, hold, and redeem physical silver through verifiable on-chain records. A hybrid token model combines fungible tokens for value representation and nonfungible tokens for bar certificates. After building the core smart contract architecture, the next step is deployment. Given the growing number of platforms, identifying the most cost-efficient option is essential. This article compares six Ethereum virtual machine-compatible blockchains and evaluates them based on gas fees and operational cost.
Bezpečnostné zlyhania blockchainových systémov zriedkavo vyplývajú z prelomenia kryptografie. Častejšie vznikajú z ekonomických stimulových nekompatibilít, chybných protokolových predpokladov a implementačných nedostatkov, ktoré môžu byť naprieč architektonickými vrstvami. Doterajší výskum prevažne analyzuje jednotlivé vrstvy blockchainov izolovane. Avšak takýto prístup nie vždy dokáže detegovať zraniteľnosti, ktoré sa môžu objavovať na hraniciach medzi jednotlivými vrstvami. Predkladaný výskum rieši túto poukázanú bezpečnostnú medzeru prostredníctvom systematickej medzivrstvovej analýzy založenej na bezpečnostnej referenčnej architektúre pre blockchainy. Použitá metodológia kombinuje modelovanie protokolov pomocou teórie hier, rozsiahle diskrétne a stochastické sieťové simulácie, návrh a prototypovanie protokolov, systematickú analýzu 36 kryptomenových peňaženiek a dôkazy s nulovou znalosťou typu zk-SNARK. Na vrstve konsenzu je formálne dokázané, že náhodný výber transakcií v protokoloch Proof-of-Work založených na orientovanom acyklickom grafe netvorí Nashovou rovnováhou. Simulácie s viac ako 8000 uzlami potvrdzujú, že táto zraniteľnosť pretrváva pri realistickom sieťovom rozsahu. Vyhodnotenie sebeckej ťažby s viacerými útočníkmi ukazuje, že prah ziskovosti v Nakamotovom konsenze klesá z 33% pri jednom útočníkovi na približne 14% až 17% pri piatich súčasných útočníkoch, a ukazuje, že Strongchain je konzistentne najodolnejší protokol vo všetkých testovaných konfiguráciách. Na rozhraní medzi konsenzom a sieťovou vrstvou dosahuje protokol Proof-of-Stake s integrovaným cibuľovým smerovaním anonymizáciu navrhovateľa blokov pri priepustnosti približne 110 transakcií za sekundu. Na aplikačnej vrstve formálna taxonómia autentifikácie odhaľuje, že väčšina analyzovaných kryptomenových peňaženiek poskytuje iba jednofaktorovú autorizáciu overovanú blockchainom napriek zdanlivej zložitosti ich rozhrania. Architektúra synchronizácie založená na zk-SNARK znižuje nároky mobilného klienta na úložisko zo 71MB na 3,5MB a nahrádza dôveru v servery tretích strán kryptografickou verifikáciou. Tieto závery a vykonaný výskum spoločne preukazujú, že bezpečnosť blockchainov je vo svojej podstate medzivrstvová vlastnosť, a zároveň poskytujú analytické nástroje, empirické referenčné hodnoty, protokolové mechanizmy a kryptografické návrhy pre budovanie odolnejších decentralizovaných systémov.
With the continuous development of blockchain technology, its applications have expanded into a wide range of fields. The consensus algorithm serves as the core of blockchain, with its performance directly influencing the overall efficacy of the blockchain system. Delegated Proof of Stake (DPoS) selects block producers through elections and offers advantages such as high performance, low energy consumption, and strong scalability. However, the election mechanism also brings several challenges, including vote bribery, low voter participation, and high degree of centralisation. To address these issues, we propose an improved DPoS algorithm based on the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) decision-making method, named BKT-DPoS, which enhances the consensus mechanism from a new perspective of multi-attribute decision-making. Specifically, a dynamic balanced clustering algorithm is introduced to constrain the voting range of certain nodes; the voting results are transformed into node influence scores using complex network theory; and the historical performance of nodes is dynamically assessed based on block production outcomes. A TOPSIS model is constructed to select the final block producers by considering node influence and historical behaviour values as decision attributes. After each round, the behavioural values of nodes are updated to incentivise honest nodes and penalise malicious ones. We conducted extensive simulations on networks ranging from 200 to 5,000 nodes over 1,00 to 10,000 rounds, and performed a comparative analysis against other improved algorithms.Experimental results demonstrate that the proposed algorithm significantly improves decentralisation, enhances resistance to vote bribery, and effectively mitigates the impact of malicious nodes.
The exponential growth of cloud computing has enabled large-scale data outsourcing but has simultaneously introduced critical challenges related to data confidentiality, integrity, and trust. Traditional cryptographic and blockchain-based cloud security solutions often suffer from high computational overhead, latency, and scalability limitations, which hinder their practical adoption. To address these issues, this study proposes a robust and lightweight blockchain-based security framework for secure cloud data storage. The framework integrates hybrid AES–ECC encryption, smart contract–driven access control, and a lightweight consensus mechanism combining Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) to achieve efficient and tamper-resistant data management. The proposed system employs an on-chain/off-chain hybrid architecture that stores only essential metadata and cryptographic proofs on the blockchain while maintaining the actual data in distributed cloud storage. This design minimizes computational burden and blockchain bloat while ensuring end-to-end transparency and verifiability. A Merkle tree–based Proof of Storage (PoS) mechanism enables rapid integrity verification without requiring full data retrieval. Comprehensive experiments were conducted using a simulated multi-node cloud environment to evaluate encryption efficiency, transaction latency, throughput, storage overhead, and energy consumption. Results show that the proposed framework outperforms existing blockchain-based models, achieving a 37.7% reduction in encryption/decryption time, a 51.3% decrease in transaction latency, and a 54.5% improvement in energy efficiency. Additionally, the system attained a 99.3% security success rate under various attack scenarios, demonstrating its resilience against unauthorized access, replay, and tampering attempts. These findings confirm that the proposed approach provides a practical balance between security assurance and performance optimization.
Blockchain-based financial systems increasingly intersect with regulated domains, including stablecoins, real-world asset (RWA) tokenization, decentralized finance (DeFi), decentralized autonomous organizations (DAOs), and ESG-linked financial instruments. Existing blockchain insurance and underwriting models rely predominantly on probabilistic risk pricing derived from historical data, oracle-fed inputs, and machine learning inference. While sufficient for limited-scale applications, these approaches exhibit structural limitations when applied to high-volume, regulation-intensive systems. This paper demonstrates that probabilistic risk pricing alone imposes a fundamental scalability ceiling, as residual risk grows unbounded with system volume. We introduce a control-oriented risk mitigation framework based on the Crystal Validator (CV), which enforces execution-level compliance constraints prior to transaction finalization. By reducing compliance entropy through deterministic validation, CV bounds residual risk independently of transaction volume. We formalize this distinction using control theory, information theory, and cyber-physical systems (CPS) principles, and show why improved machine learning alone cannot resolve these limitations. The results establish control-oriented validation as a necessary architectural primitive for sustainable blockchain insurance and regulated on-chain finance.
In this paper, a scalable interoperable hybrid blockchain systems based on a novel seven-layer architecture is proposed. The model thereby solves the three problems that have restricted the development of traditional blockchains, i.e., low transaction throughput, inability of cross-chain communication, architectural rigidity, by clearly dividing responsibilities into separate layers dedicated to core infrastructure, operation systems, and application ecosystems. It applies a hybrid consensus approach where Proof of Stake (PoS) is adopted for global finality and Practical Byzantine Fault Tolerance (PBFT) is employed for shard-level consensus, offering energy efficiency as well as fault tolerance. With rollups, sharding, and interoperability protocols e.g. Polkadot, and IBC, it boasts high performance, modular extensibility and app-ability for real-world use-cases such as finance, IoT and healthcare. The proposed architecture would act as a basis for development in AI-driven smart contracts, privacy-preserving computation and quantum-secured consensus.
Digital systems increasingly rely on user location data, raising significant privacy concerns. This study proposes a privacy-preserving location data utilization system that eliminates the need for dedicated base stations by integrating blockchain technology with zero-knowledge proof scheme. Our system converts data from smartphone trajectory data into zero-knowledge proof values and records only these proof values on the blockchain. Thus, the system enables verification of user movement without revealing sensitive information. By integrating the entire process with smart contracts on the blockchain, our system automates transaction processing and monetary transfers without relying on any specific organization. We conduct an experimental evaluation on the blockchain using trajectory data collected from a smartphone application.
In the digital economy era, the contradiction between data sharing and privacy protection is increasingly prominent. Traditional privacy protection technologies such as anonymization, differential privacy, and homomorphic encryption are difficult to meet the diverse privacy demands in multiple fields due to their flaws such as vulnerability to cracking, sacrificing data accuracy, and high computational complexity. Zero-knowledge proof (ZKP), with its core advantage of "data available but not visible", has become the key technical support to solve this contradiction. However, the current protocol types are complex and the demands in various fields vary significantly, leading to confusion in protocol selection and imbalance in resource allocation in practice. This paper systematically reviews the theoretical basis, technical system and mainstream protocol characteristics of zero-knowledge proof, integrates the advantages of demand-driven decision tree and protocol three-dimensional classification model, constructs a protocol selection framework of "demand-driven - feature matching - dynamic optimization", and clarifies the multi-stage decision-making process; Four typical fields, namely education, machine learning, finance, and healthcare, were selected to verify the effectiveness and universality of the framework, providing standardized tools for the large-scale application of zero-knowledge proof technology in various fields and direction guidance for the development of privacy protection technology in the post-quantum era.
Steps 18 and 19 explained how blockchain uses hash puzzles (proof of work) and rewards, together with randomness, to reach consensus. However, proof of work consumes a noticeable amount of electrical energy, which in turn may have negative side effects (e.g., higher energy prices or the environmental impact of energy production). For this reason, this step will introduce you to one of the main alternatives to proof of work, that is, proof of stake. Additionally, the general idea of alternative ways to achieve consensus in blockchain systems is presented.