The digital payments landscape is undergoing a fundamental transformation fueled by innovative technologies like blockchain. While often associated with cryptocurrencies, blockchain's potential extends far beyond mere transactional efficiency. This distributed ledger technology offers a unique blend of security, transparency, and immutability, making it a compelling avenue for reshaping the very nature of digital payments. Purpose: The research paper will offer insights into the theoretical foundations, practical use cases, and potential challenges of integrating blockchain technology with existing systems in India. Objective: This research paper aims to deliver a comprehensive conceptual understanding of blockchain technology, its potential applications in digital payments, and its relevance within the context of India Stack. Research outcome: The study concluded that rising internet users and adoption of technology by the financial institutions and banking sector transformed the India as fastest growing economy in terms of digital payment systems.
Sabam Parjuangan, Suhardi -, I Gusti Bagus Baskara Nugraha
Small and medium enterprises (SMEs) require secure, efficient, and low-cost digital transaction systems. However, many blockchain-based platforms are designed for large-scale applications and impose significant computational overhead, making them unsuitable for resource-constrained SMEs. This study proposes a lightweight smart contract blockchain platform tailored for SME-scale service environments. The system implements a modular smart contract architecture integrated with a lightweight blockchain and automates key transactional processes, including balance top-ups, service ordering, order confirmation, and payment execution, while ensuring data integrity through a simplified Proof-of-Work mechanism. System performance is evaluated using a Design of Experiment (DOE) framework with a full factorial design and analyzed through Analysis of Variance (ANOVA). The results show that execution time remains below 5 seconds under workloads of up to 20 concurrent transactions, with CPU utilization below 55%. ANOVA results indicate that transaction concurrency and smart contract complexity significantly affect performance, while block size has a limited impact. Security evaluation confirms resistance to unauthorized access, double-spending, and reentrancy attacks.
Shihab Sarar, Ali Imran Mehedi, Fabbiha Tahsin Prova, Saha Reno
The modern metropolis essentially demands the use of state‐of‐the‐art, real‐time surveillance systems, which should be reliable, scalable, and respectful of privacy at the same time. Critical shortcomings in traditional architectures are single points of failure, poor scalability, frequent data breaches, and inadequately managed privacy. These aspects of themselves make it inept for the demands of dynamic, fast‐paced city environments, without which reliability, security, and adaptability cannot be compromised at any cost. This brings to light the critical need for innovative and decentralized solutions that can overcome these challenges comprehensively. In our proposed approach, a decentralized framework integrates private blockchain technology via Ethereum, a hybrid cryptography model combining advanced encryption standard (AES) and Rivest–Shamir–Adleman (RSA) encryption, and state‐of‐the‐art deep learning techniques such as YOLOv8, DeepSort, and ArcFace. Blockchain technology ensures metadata is immutable and transparent, thus saving metadata from unauthorized access and tampering. The hybrid cryptography model encrypts sensitive data through AES and securely shares the key of AES through RSA encryption, while decryption is efficiently done in a key management system (KMS). Furthermore, YOLOv8 and DeepSort can be used for high‐precision object detection and real‐time tracking, and ArcFace can be used for facial recognition, meeting the split‐second decision‐making required in urban surveillance. Extensive experiments are performed, and the results indicate that the proposed framework enhances detection precision, tracking accuracy, real‐time responsiveness (60 FPS), and resistance to tampering (>99% chain quality per quorum Byzantine fault tolerance [QBFT]) without compromising efficiency. The adaptive and reliable solution meets modern urban surveillance demands that are evolving at an ever‐increasing pace. The scalability of the operation further ensures enhanced public safety. This paper discusses a decentralized urban surveillance system that is both tamper‐proof and secure using current blockchain technologies, InterPlanetary file system (IPFS), hybrid AES–RSA, and deep learning technologies to mitigate the risks of a traditional centralized system, such as data tampering and privacy violations. The system uses the Ethereum blockchain to provide immutable metadata, the IPFS protocol to create a fully distributed storage system of video and image frames, and an off‐chain KMS service to distribute the keys to the authorized edge devices. The system utilizes real‐time object detection (YOLOv8), tracking (DeepSort), and face recognition (ArcFace) to perform inference locally on the edge devices. We have performed experiments that demonstrate the tamper‐proof and secure scalability with low latency and secure tamper‐proof data integrity of this urban surveillance system in ever‐changing urban environments.
This thesis presents a comprehensive predictive maintenance system and application interface that integrates deep learning and blockchain technologies in order to enhance maintenance strategies in industrial systems. Traditional predictive maintenance systems have significant issues regarding data security and decentralization. This study aims to address these limitations by leveraging blockchain technology, with a specific focus on improving the reliability and verifiability of predictive maintenance processes. In this study, an LSTM-CNN hybrid model was developed to evaluate complex patterns in both time and features, thereby enabling high-accuracy fault prediction. The proposed model is designed to perform binary classification for fault prediction in industrial equipment. During the implementation phase of the study, an open-source dataset was used to train and test the developed model. The Randomized Search method was used in the hyperparameter optimization process to increase the prediction success of the proposed model. The hybrid model was trained with 5-fold cross-validation, and class weighting and threshold value optimization methods were applied to eliminate the class imbalance problem. In the threshold optimization phase, F1-score-based methods are applied to maximize recall at three predefined minimum precision levels (0.05, 0.2, and 0.85), while identifying the most balanced trade-off between precision and recall. In the proposed system, sensor data are stored in a database (SQLite3), and cryptographic proofs generated using zero-knowledge techniques are transmitted to the Ethereum network. The Poseidon hash function is used to ensure data integrity, and the Groth16 protocol is used for Zk-Snark proof generation. This approach enables secure verification of data validity without publicly disclosing sensor data and simultaneously addresses scalability concerns. The system architecture is designed to include manager, operator, and engineer nodes, and all smart contracts are implemented using Solidity. In addition, a graphical user interface is developed using the Tkinter library in Python. The experimental results demonstrate that the proposed LSTM–CNN hybrid model produces successful outcomes in terms of fault prediction performance. According to scenario where the decision threshold is optimized based on the F1-score, the model achieves an accuracy of 0.987, an AUC value of 0.979, and an F1-score of 0.794. In future studies, the proposed system is planned to be implemented on the Ethereum mainnet instead of a test network, with a comprehensive evaluation of on-chain operational costs. However, instead of Zk-Snark proofs, which have a centralized structure, the use of Zk-Stark proofs, which are transparent and do not violate the principle of decentralization, is planned.
Background: The inherent immutability of traditional blockchain technology fundamentally conflicts with the need for dynamic updates and secure sharing of medical data. Existing editable blockchain solutions also face limitations in update efficiency, key management security, and cross-institutional privacy protection. Objective: This paper aims to design a novel architecture that integrates chameleon hash with a permissioned blockchain to achieve secure, efficient, and auditable incremental updates and controlled sharing of medical data. Methods: We propose a hybrid architecture comprising: (1) a lightweight off-chain update protocol based on chameleon hash, enabling authorized institutions to swiftly modify off-chain data using a trapdoor key while only recording lightweight credentials on the blockchain; (2) a distributed trapdoor key management mechanism based on threshold signatures, which disperses critical authority across multiple trusted medical nodes to eliminate single points of failure; and (3) cross-institutional data sharing smart contracts with privacy protection, featuring an integrated Zero-Knowledge Proof (ZKP) verification interface that allows third parties to verify data validity without accessing the original sensitive information. Results: Compared to traditional schemes, our method improves update throughput by 3.2× and reduces on-chain storage by 76%. Authorized updates and verification complete within 5 seconds in simulated cross-hospital scenarios, while distributed key management prevents unauthorized modifications. Conclusion: The proposed scheme balances dynamic updates with trustworthy auditing in medical data management. By addressing efficiency, security, and privacy limitations of existing solutions, it supports the development of a trusted, privacy-secure medical data ecosystem.
We present Processual Memory Architecture (PMA), a computational framework that unifies data storage and computation by representing all information as transformation functions rather than static state, rendering the traditional ontological distinction between them architecturally unnecessary. In PMA, storing information means encoding it as a mathematical transformation that produces the data when applied to a standardized canonical input; reading means applying the transformation; and computing means composing transformations. This inversion of the conventional von Neumann paradigm yields five emergent architectural properties—structural auditability, transparent reasoning, enforced constraints, tamper evidence, and reversibility—that collectively enable verifiable computation: systems that can mathematically verify the integrity and correctness of their own reasoning chains. We provide a complete mathematical specification of PMA over Galois fields GF(2k) with roundtrip exactness guarantees, constructive algorithms for both invertible and non-invertible encoding modes, and a reference permutation-based embodiment with explicit bit-level storage formats. We analyze thermodynamic properties under reversible logic implementation, demonstrating that PMA operations on adiabatic substrates can approach within 10× of the Landauer limit at the localnode level. We then present the integration architecture for PMA with artificial general intelligence (AGI) safety frameworks, showing how transformation-based reasoning enables safety constraints that are structural rather than advisory—creating systems where unsafe behavior is computationally undefined rather than merely prohibited. We discuss applications to financial auditing, medical AI verification, and autonomous systems governance, and compare PMA's approach to verifiable computation with existing paradigms including blockchain, zero-knowledge proofs, and mechanistic interpretability.
With the rapid increase of terminal devices in the Internet of Things (IoT), it has become a significant challenge to achieve real-time and privacy-preserving data aggregation. To address this challenge, edge computing has emerged as an effective paradigm to reduce latency, where a privacy-preserving data aggregation scheme is exploited to preserve data privacy. However, most existing privacy-preserving data aggregation schemes are limited by fixed data dimensions, low scalability, and high communication or computational overhead. To address these shortcomings, this paper proposes a multidimensional privacy-preserving data aggregation scheme that supports flexible dimension expansion and privacy protection in edge computing systems. The scheme integrates the Chinese Remainder Theorem (CRT) with an elastic modulus set to efficiently pack multidimensional data. This design enables terminal devices to add new data dimensions without interrupting current operations or modifying historical data. Furthermore, by exploiting Bulletproofs-based zero-knowledge proofs and Bellare-Neven (BN) signatures with half-aggregation, the proposed scheme enables lightweight and scalable batch verification of data integrity and authenticity. These mechanisms effectively reduce the verification workload and communication bandwidth in large-scale deployments. In addition, an optimized Paillier homomorphic encryption algorithm is used to enable efficient aggregation of encrypted multidimensional data. Experimental results and theoretical analysis show that the proposed scheme significantly reduces computational and communication costs compared with existing methods.
With its decentralized, tamper proof, transparent and traceable characteristics, blockchain technology has shown great potential in fields such as finance, supply chain, and the Internet of Things. However, the public transparency of its ledger poses a serious challenge to user transaction privacy. Traditional privacy protection schemes such as homomorphic encryption and zero knowledge proofs can enhance privacy, but often struggle to balance computational overhead, communication costs, and data availability. This article explores the innovative application of neural networks in blockchain privacy protection and proposes a transaction obfuscation model based on generative adversarial networks. This model utilizes a generator to learn the statistical features of raw transactions and generate difficult to track obfuscated transactions, while ensuring the validity and compliance of obfuscated transactions through discriminators and blockchain verification contracts. The experimental results show that compared with traditional obfuscation methods and differential privacy methods, the proposed model significantly reduces the consumption of privacy budget and computation delay while ensuring high transaction utility (such as reducing address correlation by more than 85%), achieving a better balance between privacy and utility. This study provides new ideas for building efficient and practical blockchain privacy enhancement solutions.
The concept of blockchain has transformed the trust concept by decentralizing, non-modifiable, and transparent, but there is a certain conflict between the principle of public verifiability and data privacy. As DeFi and cross-institutional data collaboration should grow, it has become a fundamental concern to have the confidentiality of this data without losing verifiability on-chain. The following paper will be a review of blockchain privacy technologies developed in 2020-2025, which will involve the history of zero-knowledge proofs and homomorphic encryption development at the cryptographic primitive level, as well as share new developments such as secure multi-party computation. It points out advances in recursive proof systems, distributed proof generation architectures and scalable multi-party computing systems to overcome bottlenecks in performance. There is a trade-off between privacy, system performance, regulatory compliance, and decentralization in a comparative analysis of technology integration in both public and permissioned chains. Lastly, research directions in the future are suggested in order to overcome issues associated with low proof efficiency, regulatory compliance problems, and migration of post-quantum cryptography. The review offers both theoretical and technical sources on how to develop trusted blockchain infrastructure that would strike the right balance between compliance, high-performance, and data sovereignty.
Advancements in networking applications increase the requirement for secure data storage and an efficient data access mechanism with robust networking characteristics. Consequently, the huge volume of data generated from the het-erogeneous networks, such as smart cities, healthcare, and smart energy trading systems, suffers from scalability issues and generates insights for secure data storage and effective data management. Therefore, the research proposes a secure data storage and access scheme named Multimodal Biometric-enabled Zero-Knowledge Proof of Stake(MBZKPS). The Multimodal Biometric Data Access(MBDA) ensures secure and robust access to the heterogeneous data with reduced computational overhead. The Distributed Storage System and the Zero Knowledge Protocol with Proof of Stake alleviate the storage pressure on the blockchain and regulate the heterogeneous data storage and access in the blockchain. The Message Digest 5(MD5) with Homomorphic Encryption enables computations on the encrypted data with better data confidentiality preserva-tion. The introduction of the blockchain eliminates the scalability issues with improved privacy preservation and data integrity. Simulation results validate the superiority of the MD5 with Homomorphic Encryption (HE) used in research by achieving 0.95ms decryption time, and 0.97 encryption time with 0.73 Genuine User Rate occupying 363.76KiloBytes of memory for 250 nodes. In addition, the proposed research performs secure data storage with a 1025.85 ms response time and 1.01ms transaction time using blockchain.
[Depreciated and replaced by V3] The application-specific clean rebuild has not yet been published; its authoritative theoretical boundary is now the governing V3 branch: After Turing: The Fold Machine - An Exact, Parameter-Free and Machine-Closed Derivation of Classical Computational Science from Smithian Fold Theory; From Fold to Consciousness: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Consciousness and Cognitive Science from Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transparent historical provenance; this record must not be presented or cited as current V3 work. v4.0 — the word-scale gap closes within the fold. Rung 5e (pre-registered): the fold-factor mixing law — every context level that holds contributes, weighted 2^level, the engine's own forced halving constant — carries the pure counted engine, with no twin, no prose flood, zero training and zero parameters, past the gradient-trained transformer at word scale: cross-entropy 3.1907 vs the same-day twin's 3.4292 (replicated across two independent anchorings; stacked with the Rung 5d extraction: 3.1344). Both scales of the task gate now belong to the counted engine. Rung 5d's transfer-in verdict is SUPPORTED across three independent arena anchorings in one day. New in the architecture: tool graduation (acts held, values never — a question territory that a tool answered once runs the tool itself thereafter, fresh), recall as regeneration across every memory tier, and judge-independent graduation scoring. End-to-end verification: 36/36. v3.4: Rung 5d, the transfer-in — pre-registered verdict SUPPORTED: the trained twin's dyadically-loud fold content is extracted and installed INTO the counted engine as a counted prior with zero new parameters, closing 55.6/87.9/101.4% of the available gap at k=16/32/64 while the random-truncated null closes 10.1/24.5/56.5%; at half budget the loud shape beats the full twin's own. The word-scale rematch is recorded in full (twin retrained on today's text; decomposition included). Also: judge-independent graduation scoring (boot-discovered pool, cycle-parity alternation), multi-orbit binding (XI-4 in full), recall-is-regeneration (a held experience re-walks its own orbit, never reprinted), the public SOTA table beside the local giants with cited published figures, and one-command replication kits (GPT-2 weights auto-fetch; 13/13, 39/39 proven on a fresh clone). End-to-end verification: 36/36. Full paper v1.1 — supersedes the pre-paper (From One Axiom to Master-Level Chess — and the Law Inside Neural Networks). Built from scratch by one woman, working alone, in under twenty-four accumulated hours: where a score falls short it marks an implementation gap at measurement time, never a limit of the mathematics — the gains between releases are the finding. v1.4 adds the fold eye (vision as exact integer Walsh spectra, self-certified by integer Parseval per image, recognition of seen images with no image model in the loop) and the graduation score (blind head-to-head vs the teacher, tallied per question-territory; the teacher retires as wins cross the majority lock) -- and documents the 2026 convergence: DeepSeek Engram arrives at deterministically-addressed exact memory from the gradient side, and two independent results place the optimal curriculum at p = 1/2, the fold lock. v1.6: the full omnimodal engine (the voice via Kokoro, the fold ear -- sound as Parseval-certified integer Walsh spectra, video composed from frames + sound), speaker-transparent reasoning threads, and 32/32 end-to-end empirical verification of the entire architecture including persistence across process death. v1.7: removal-proof omnimodality, measured -- every supporting model is a teacher with an exit: a sound taught once by the synthesis teacher is re-spoken from the engine's own exact counted record in 0.00s with no model; a sound heard once is recognized natively with no transcriber; 34/34 end-to-end verification. v1.9: zero-model perceptual learning (the human observer -- a novel image learned and re-recognized at share 1.00 with no model in the loop); agentic self-knowledge (the observer reads the engine's own source, measured); the hourly progress instrument with a committed pre-boot birth line; one-tap y/n closure. v2.0 (flight-ready): the full modern-agent toolkit (live web search/fetch, paginated reading, in-file grep -- every call held as a training trace), the 43-domain everything-curriculum under the fold-only law, SOTA 1-1 benching on the public MMLU test split with the newborn baseline committed, generation closure (the Learning Law reaches generate() itself), and 36/36 end-to-end verification. v2.1: the ReAct law (reason-act-observe enforced in-turn; narrated intent without an act is detected and forced), reasoning trained on the observer's NATIVE thinking tokens (STaR-gated) with both minds' full thinking streamed to the user, and document intake (a sent file is reading -- inboxed, counted, persistent). v2.2: the identity stated correctly -- UnisonAI is an OMNI MODEL (language, sight, hearing, speech, and video on one held memory), not a language model; LLMs remain the contrast class only. v3.0: the full-altitude rewrite -- the complete omni model documented at the same depth as the spectral science: thirteen sections, the architecture organ by organ with every measurement, Rung 5c as its own section, the empirical record and its committed birth line, 36/36 end-to-end verification, and the 2026 convergence. This paper is a PROOF of The Smithian Fold Theory of Everything, not the main event: the theory (one axiom, zero free parameters, 1,844 machine-verified forced checks) is at DOI 10.5281/zenodo.21182469 and github.com/MettaMazza/Smithian-Fold-Theory-Of-Everything -- run the prover yourself. The engine: github.com/MettaMazza/UnisonAI. v3.3: the LLM-native presence suite -- the exact registered protocol applied to GPT-2's entire knowledge-storage class: 13/13 tensors, 39/39 checks, unanimous (margins 3.4-79.3x); the flagship claim now rests on the flagship objects, with diffusion/speech models recast as cross-domain breadth. Three connected results and the architecture they force. First, a pre-registered, self-certifying spectral instrument shows trained neural-network weights carry placement-law in the dyadic (Walsh) basis: 18/18 unanimous on validated released models; the law concentrated in transformer expansion projections and token embeddings across three unrelated architectures (up to 230x chance in GPT-2), attention at chance; strictly training-caused (He-initialised controls at 1.0x); surviving 4-bit deployment quantization. A recipe map from 124M to one trillion parameters shows the law tracks training recipe, not scale or architecture — strongest carrier DeepSeek-R1-671B at 43–47x — and loud-recipe weights transform under the fold's transformation group exactly as solved game-theoretic value fields do. Second, the "learned similarity space" is a counted object: word kinship as exact co-occurrence shares reproduces semantic family structure (quark → lepton, neutrino, proton) with zero parameters and zero gradients. Third, UnisonAI: a complete language architecture in which every LLM mechanism — memory, attention, similarity, learning, prediction, generation — is replaced by a machine-verified law of the Smithian Fold Theory, zero trained parameters end to end. On identical held-out text the fold-native engine outperformed its trained transformer twin (cross-entropy 1.289 vs 1.888) after reading the corpus once (26 seconds) against 48,000 gradient readings (21 minutes per seed). Deployed as a live, continuously-learning agent whose teaching loop also runs autonomously: a teacher model asks, judges, and closes the learning law itself, and the engine self-plays against its own held lessons. Negative results reported in full with their scopes. Companion to The Smithian Fold Theory of Everything (DOI: 10.5281/zenodo.21182469; 307 suites, 1,844 forced checks, 0 failures). Engine and records: github.com/MettaMazza/UnisonAI and github.com/MettaMazza/Smithian-Fold-Theory-Of-Everything.
Open access
8 source records
Explainable Artificial Intelligence (XAI)
Generative Adversarial Networks and Image Synthesis
Zero-knowledge proof is a special cryptographic technique that allows a prover to convince a verifier about the correctness of a claim without explicitly disclosing the claim itself. With the advancements of blockchain technologies, zero-knowledge proof has been successfully integrated into many decentralized applications over the years. ZoKrates, with its ease-of-use and direct integration to blockchain platforms, has emerged as a leading framework for developing, generating and verifying zero-knowledge proofs. This survey compiles a corpus of 347 documents that cite the original research work of ZoKrates by considering the period ranging from 2018 to 2025. Out of this corpus, this survey systematically selects and analyzes a total of 87 different documents including only peer-reviewed publications and excluding the gray literature. To the best of our knowledge, this is the first survey in the literature to follow a systematic approach to analyze the privacy- preserving applications in blockchain from the perspective of ZoKrates. This survey presents three different classifications over the documents with respect to (i) the applications they develop, (ii) the challenges they frequently encounter and (iii) the metrics they often use to measure performance of their techniques. Based on the challenges identified, this survey finally discusses numerous future research directions to promote potential advancements in the field and attract the attention of scientific and industrial communities. Feedback from readers regarding any inaccuracies or misinformation in this survey is welcome.
Hong Min, Yousef Ibrahim Daradkeh, Jung Taek Seo, Mohd Anjum · 5 authors
This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things (IoT)-enabled smart contract systems. The integration of IoT, edge computing, and blockchain introduces significant challenges, including limited device capacity, high verification cost, and scalability constraints. Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices, resulting in increased latency, energy consumption, and transaction costs. To address these issues, this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup (Z-FLOR) framework, an adaptive and energy-efficient model designed to enable secure and verifiable computation in IoT-based smart contract systems. The proposed framework integrates three key components. First, a zero-knowledge proof-based verification model using the Groth16 zkSNARK module generates compact and privacy-preserving proofs that enable fast and reliable verification. Second, a Fuzzy Logic–Driven Energy-Aware Offloading module dynamically allocates computational tasks between IoT devices, edge servers, and cloud platforms based on energy availability, network delay, and device reliability. Third, an Optimistic Rollup Verification module aggregates proofs off-chain and submits them in batches to reduce gas costs and enhance scalability. Extensive simulation and experimental evaluation across diverse IoT scenarios demonstrate the effectiveness of the proposed computational framework. Results indicate that Z-FLOR achieves 99.7% verification accuracy and 98.9% proof compression efficiency, while gas cost analysis indicates gas cost reductions in the range of 80%–98%. Z-FLOR additionally achieves a 44.0% reduction in latency, 51.0% savings in gas costs, and 38.0% energy consumption compared to baseline approaches. These findings highlight the capability of the proposed approach to serve as a scalable and energy-efficient modeling solution for secure IoT smart contract execution in decentralized environments.
Over the past decade, decentralized digital currencies have gained prominence in finance and technology, but their growth has also drawn adversaries exploiting security vulnerabilities. This paper reviews the literature on cryptocurrency and security using bibliometric analysis of Web of Science and Scopus articles published between 2013 and May 2025. Tools such as Biblioshiny and VOSviewer were employed to explore key trends, influential contributors, collaborative networks, and emerging themes. A novel contribution of this study is the use of Large Language Models (LLMs) to address inconsistent affiliation formats, enabling accurate identification of leading academic organizations. The results demonstrate that LLM-based harmonization effectively prevents misrepresentation in bibliometric datasets. Overall, this study not only summarizes evolving research trends in cryptocurrency and security but also highlights the potential of LLMs to enhance bibliometric methods, suggesting broader applications for improving the accuracy and reliability of future scholarly analyses.
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.
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.