V. Hemamalini, M. Jeyaselvi, Amit Kumar Tyagi, Shabnam Kumari
Decentralized Applications (dApps) and Distributed Ledger Frameworks (DLFs) represent critical advancements in the realm of blockchain technology, promising enhanced security, transparency, and efficiency. Decentralized Applications, leveraging the inherent properties of blockchain, operate without central control, offering users greater autonomy and reducing reliance on intermediaries. These applications span various domains, from finance and supply chain management to gaming and social networks, demonstrating the versatility and transformative potential of dApps. Distributed Ledger Frameworks, encompassing both blockchain and non-blockchain-based systems, provide the underlying architecture for these applications. DLFs ensure data integrity, immutability, and consensus across distributed nodes, fostering a trustless environment where participants can transact securely. Key frameworks like Ethereum, Hyperledger, and Corda offer diverse features tailored to different use cases, ranging from public, permissionless networks to private, permissioned systems.
Network Systems, AT&T, United States, Amar Gurajapu, Vardhan Garimella, United States Intellibus
ABSTRACT: Distributed cloud services across multiple providers demand consistent identity verification and policy enforcement. Centralized IAM and policy managers introduce single points of failure, limited auditability, and latency. We propose BC-IdPol, a blockchain-based framework that stores identities and governance policies on a permissioned ledger. Smart contracts enforce registration, authentication, and policy retrieval. Off-chain cloud agents query the chain for runtime decisions. In experiments over a three-cloud prototype (Azure, AWS, GCP) on Hyperledger Fabric, BC- IdPol achieved:  99.9 % tamper-resistance (vs. 0 % baseline)  45 ms median identity lookup latency (vs. 18 ms centralized API)  60 ms median policy fetch time (vs. 20 ms baseline)  1,800 req/sec enforcement throughput (vs. 2,200 req/sec) We detail architecture, smart-contract design, mermaid diagrams, evaluation results, limitations, and future work.
The increasing demand for cloud computing services has led to the rapid expansion of cloud data centers, which consume significant amounts of energy and contribute substantially to global CO2 emissions. As the IT industry grows, the environmental impact of these data centers becomes an urgent concern. Green Cloud Computing (GCC) has emerged as a solution to mitigate this impact by focusing on energy efficiency and reducing carbon footprints while maintaining the necessary functionality and performance of cloud infrastructures. However, traditional blockchain consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) face limitations regarding energy consumption and scalability, which exacerbates the environmental burden. This study proposes a quantum-inspired blockchain consensus algorithm designed to optimize energy consumption and reduce latency in cloud data centers. By integrating quantum principles such as superposition and entanglement, the algorithm enhances task scheduling and resource utilization, enabling more energy-efficient operations without sacrificing performance. Simulations in a green cloud environment showed that the quantum-inspired algorithm resulted in up to a 30% reduction in energy usage compared to traditional consensus methods, with a 40% improvement in consensus processing time. These results suggest that quantum-inspired algorithms hold significant potential for enhancing the sustainability of cloud infrastructures by improving energy efficiency and scalability. Furthermore, this study discusses the feasibility of implementing quantum-inspired algorithms on classical hardware, addressing challenges in scalability and integration into existing blockchain frameworks. The findings provide valuable insights into the potential of quantum-inspired technologies to drive energy-efficient solutions in cloud computing.
Adam Zahir, Milan Groshev, Carlos J. Bernardos, Antonio de la Oliva
Edge computingbrings computation near end users, enabling the provisioning of novel use cases. To satisfy end-user requirements, the concept ofedge federationhas recently emerged as a key mechanism for dynamic resources and services sharing across edge systems managed by different administrative domains. However, existing federation solutions often rely on pre-established agreements and face significant limitations, including operational complexity, delays caused by manual operations, high overhead costs, and dependence on trusted third parties. In this context, Distributed Ledger Technologies (DLTs) such asblockchaincan create dynamic federation agreements that enable service providers to securely interact and share services without prior trust. This article first describes the problem of edge federation, using the standardized ETSImulti-access edge computing (MEC)framework as a reference architecture, and how it is being addressed. Then, it proposes a novel solution usingblockchainandsmart contractsto enable distributed MEC systems to dynamically negotiate and execute federation in a secure, automated, and scalable manner. We validate our framework’s feasibility through a performance evaluation using a private Ethereum blockchain, built on the open-source Hyperledger Besu platform. The testbed includes a large number of MEC systems and compares two blockchain consensus algorithms. Experimental results demonstrate that our solution automates the entire federation lifecycle-from negotiation to deployment–with a quantifiable overhead, achieving federation in approximately 18 seconds in a baseline scenario. The framework scales efficiently in concurrent request scenarios, where multiple MEC systems initiate federation requests simultaneously. This approach provides a promising direction for addressing the complexities of dynamic, multi-domain federations across the edge-to-cloud continuum.
Hancheng Gao, Mohammad S. Obaidat, Haiping Huang, Yizheng Xing · 6 authors
The adoption of blockchain technology in healthcare has significantly enhanced data integrity, transparency, and user privacy. However, high storage overhead and resource-intensive operations remain major challenges to its widespread deployment, particularly in large-scale or resource-constrained healthcare environments. To address these challenges, we propose STORChain, a storage-optimized blockchain framework designed for data services in healthcare. The framework introduces the Clustered Merkle Patricia Tree (C-MPT), a novel logical structure that aggregates similar transaction types to maximize storage efficiency while ensuring Proof of Inclusion (PoI). A Selective Transaction Pruning Strategy (STPS) is employed to prioritize and prune essential historical data, improving data access efficiency. Additionally, an incentive-based Delegated Proof-of-Stake (DPoS) consensus algorithm is utilized, integrating a probabilistic election mechanism to promote fairness and node inclusivity. Comprehensive theoretical analysis and practical experiment results indicate that STORChain significantly reduces storage overhead, optimizes data access, and outperforms existing schemes.
Non-Fungible Tokens (NFTs) have emerged as a transformative blockchain-based technology, enabling unique digital ownership and novel applications across art, gaming and Decentralized Finance (DeFi).However, the rapid evolution of NFT ecosystems has exposed critical challenges in scalability, security and interoperability, driven by the underlying blockchain architectures and software design paradigms.This article presents a systematic review of state-of-the-art blockchain architectures supporting NFTs, analyzing Layer-1 and Layer-2 solutions, consensus mechanisms and smart contract design patterns.We further explore software design best practices for NFT platforms, including gas optimization, upgradeability and anti-fraud mechanisms.Through a comparative analysis of Ethereum, Solana, Flow and Layer-2 frameworks like Polygon, we identify trade-offs in decentralization, throughput and cost.Finally, we highlight open challenges and future directions, such as cross-chain interoperability and energy-efficient NFT minting.This work serves as a comprehensive reference for researchers and practitioners aiming to advance NFT infrastructure.
Cross-chain bridges and oracle DAOs represent some of the most vulnerable components of decentralized systems, with more than 2.8 billion lost due to trust failures, opaque validation behavior, and weak incentives. Current oracle designs are based on multisigs, optimistic assumptions, or centralized aggregation, exposing them to attacks and delays. Moreover, predictable committee selection enables manipulation, which threatens data integrity across chains. We propose V-ZOR, a verifiable oracle relay that integrates zero-knowledge proofs, quantum-grade randomness, and cross-chain restaking to mitigate these risks. Each oracle packet includes a Halo 2 proof verifying that the reported data was correctly aggregated using a deterministic median. To prevent committee manipulation, VZOR reseeds its VRF using auditable quantum entropy, ensuring unpredictable and secure selection of reporters. Reporters stake once on a shared restaking hub; any connected chain can submit a fraud proof to trigger slashing, removing the need for multisigs or optimistic assumptions. A prototype in Sepolia and Scroll achieves sub-300k gas verification, one-block latency, and a $\mathbf{1 0} \times$ increase in collusion cost. V-ZOR demonstrates that combining ZK attestation with quantum-randomized restaking enables a trust-minimized, high-performance oracle layer for cross-chain DeFi.
This paper presents an enhanced blockchain-based salary certification system that leverages FISCO BCOS, Node.js, and MySQL to address critical challenges in traditional salary management systems. The system introduces more substantial advancements in blockchain topology design, performance optimization, and security mechanisms. It features a hierarchical consortium blockchain architecture that balances decentralization with operational efficiency, ensuring robust security and transparency. The system employs advanced techniques such as dynamic validator set management, batch transaction processing, and parallel validation pipelines to achieve higher transaction throughput and reduced latency. Additionally, it integrates zero-knowledge proofs and a multi-active data center architecture for enhanced data protection and disaster recovery. Performance evaluation demonstrates significant improvements in transaction throughput and latency, with the system achieving high success rates in both open and query type tests. The modular design allows for flexible deployment across various organizational structures, providing a comprehensive solution for secure, transparent, and efficient salary data management.
In recent years, blockchain consensus mechanisms have evolved significantly from the original proof-of-work design, transitioning towards more efficient and scalable alternatives. This paper presents a comprehensive review and analysis framework for blockchain consensus mechanisms based on a systematic examination of 200+ publications. We categorize consensus mechanisms into four performance-oriented groups: high throughput, strong security, low energy, and flexible scaling, each addressing specific trade-offs in the blockchain trilemma of decentralization, security, and scalability. Through quantitative metrics including transactions per second, energy consumption, fault tolerance, and communication complexity, we evaluate mainstream mechanisms. Our findings reveal that no single consensus mechanism optimally satisfies all performance requirements, with each design involving explicit trade-offs. This paper provides researchers and practitioners with a structured framework for understanding these trade-offs and selecting appropriate consensus mechanisms for specific application contexts. Finally, we discussed future development trends, as well as regulatory and ethical considerations.
Md. Rafid Haque, Sakibul Islam Munna, Sabbir Ahmed, Md. Tariqul Islam · 6 authors
Centralized version control systems (VCS) are vital for software development but pose risks of data loss and ownership disputes. While blockchain offers a decentralized alternative, existing solutions are often hindered by high latency, compromising the real-time collaboration essential for modern workflows. This study introduces a novel hybrid architecture combining the security of the Ethereum blockchain and the InterPlanetary File System (IPFS) with two key contributions: 1) Shamir's Secret Sharing (SSS) to create a trust-minimized model for key distribution, and 2) an authoritative-first, optimistic-fallback retrieval protocol utilizing a temporary middleware to decouple the user experience from blockchain confirmation delays. We implemented a full prototype and conducted a comprehensive performance evaluation on the public Sepolia testnet. Our results demonstrate that this architecture not only provides a secure, auditable, and resilient platform for source code hosting but also achieves highly competitive user-perceived performance. Our user-perceived push time reduces submission latency by up to 49% compared to a standard git push for common repository sizes, proving that a well-designed decentralized VCS can balance the core tenets of security and decentralization with the practical need for speed and efficiency.
Gas fees play a crucial role in Ethereum blockchain transactions, directly affecting the cost and efficiency of decentralized applications. This study analyzes gas fee patterns across transaction types, temporal trends, and anomalous behaviors using a dataset of 1,000 Ethereum transactions. The results reveal that the average gas price was 120.5 Gwei, with a standard deviation of 45.2 Gwei, highlighting significant variability. Smart contract functions exhibited varying gas usage, with mint operations consuming the highest average gas (1,500,000 units) compared to approve (1,200,000 units) and transfer (800,000 units). A positive correlation (r = 0.65) was observed between gas price and value transferred, suggesting that higher-value transactions often incur elevated gas fees. Temporal analysis showed predictable patterns, with peak gas prices occurring between 13:00 - 17:00 UTC during high network activity and lower prices between 02:00 - 06:00 UTC. Additionally, anomaly detection identified 15 outlier transactions, including one with an unusually high gas price of 500 Gwei, reflecting network congestion or prioritization strategies. These findings provide actionable insights for optimizing transaction costs and improving smart contract efficiency. Future research could explore layer-2 scaling solutions, alternative fee mechanisms, and machine learning approaches for gas price prediction. This study contributes to a deeper understanding of Ethereum’s gas fee dynamics, offering valuable guidance for developers, users, and researchers in the blockchain ecosystem.
One major issue associated to microservices architectures is aspects related to data consistency and management of data within such a context. To measure the effect on system integrity, this research paper compares different data consistency models which are, eventual consistency, distributed transactions through Two-Phase Commit (2PC), the Saga pattern. Employing historical data analysis, case studies, and examining the recent technological advancements, the study gives a comparative assessment of various approaches in terms of their capabilities to achieve transaction latencies and throughput and possibility to guarantee consistent results. Based on the results, it is evident that although eventual consistency models provide high scale-out and availability characteristics, the models experience transitory data inconsistency. Consistency is high since data is accessed from, and committed to, multiple databases in a distributed transaction but they have performance penalties. The Saga patterning gives a balanced view but at the same time brings together additional design difficulty. New trends like Distributed Ledger Technology (DLT) as well as new consensus algorithms that can be used to solve problematic approaches like the Raft algorithm give new possibilities for improving consistency of the data but bring their own concerns. The study implies that one must choose the right consistency model depending on the need and performance Degree and the future research can consider the use of the combination of several models along with the improvement of technological solutions to have a better deal with data consistency in microservices.
Blockchain sharding is a promising solution for scalability but struggles to reach the expected performance due to the high ratio of cross-shard transactions. Account migration has emerged as a critical approach to optimizing shard performance. However, existing migration solutions suffer from inefficient handling of queued withdrawal transactions from a migrating account and inadequate priority mechanism for migration transaction, resulting in prolonged transaction makespan and reduced system throughput. This paper proposes Caravan, a novel blockchain sharding system for optimizing account migration. First, Caravan proposes a transaction aggregation-based migration scheme to efficiently handle withdrawal congestion post-migration. It incorporates a multi-level Merkle tree and cross-shard synchronization protocol to ensure cross-shard security. Second, Caravan presents an economic incentive-driven priority mechanism that motivates miners to perform transaction aggregation and prioritize migration transactions by increasing the associated revenue. Furthermore, its gas recycling strategy enables users to finance migration costs without awareness or extra expenses. Finally, we develop the Caravan prototype, deploy it on Alibaba Cloud, and experiment with real Ethereum transactions. The results show that compared to the state-of-the-art account migration schemes, Caravan significantly mitigates the transaction surge caused by migration, achieving up to a 3.2× throughput improvement and a 65% reduction in transaction confirmation latency. And users share considerable migration costs without extra expenses, significantly reduce system costs. The code for Caravan is available on GitHub.11Caravan are available athttps://github.com/Caravan-project/Caravan.
Purpose: Enterprise Resource Planning (ERP) systems, such as SAP (Systems, Applications, and Products in Data Processing), are critical to modern enterprises, enabling the integration of core business functions and the management of essential data. Ensuring their availability, reliability, and adaptability is paramount, as disruptions can result in significant operational and financial consequences. Traditional Knowledge Management (KM) approaches emphasize the preservation of ERP-related knowledge but often lack responsiveness to emergent risks. This study introduces a novel framework grounded in the concept of antifragility—where systems grow stronger under stress—by simulating disruptions to enable continuous knowledge evolution and system adaptation. Methodology: A mixed-methods research design combines simulation-based inquiry with Design Science Research (DSR) to investigate antifragile KM within ERP environments. Artificial Intelligence (AI) tools are integrated into the KM system to analyse ERP failures, generate runbooks, and proactively manage recovery knowledge. Controlled simulations of kernel upgrades and failure scenarios—modelled on ITIL 4 incident typologies—serve as structured stressors to expose vulnerabilities. Lightweight LLMs, Retrieval-Augmented Generation (RAG) pipelines, and semantic search tools are employed to codify procedural knowledge and enhance the responsiveness of ERP operations. Findings: The results demonstrate that embedding antifragile principles into ERP KM improves organizational learning, responsiveness, and recovery capabilities. Transitioning from static knowledge repositories to dynamic, AI-enabled systems allows for autonomous decision-making, decentralized knowledge flow, and adaptive documentation. Each disruption becomes a learning event, reinforcing the resilience and self-improvement of the ERP knowledge ecosystem. Implications: Empirical insights suggest that AI-driven antifragile KM transforms ERP disruptions into opportunities for growth, rather than threats to stability. The proposed framework supports the development of systems that not only recover from failure but also become progressively more robust and adaptive through structured experimentation and continuous learning.
The rapid development of the Internet of Things (IoT) has intensified security and privacy challenges across data generation, transmission, and storage. This study introduces a blockchain-based secure edge storage model tailored for IoT environments and presents a lightweight consensus algorithm, Deep Q-Network (DQN)-Raft+, which incorporates deep reinforcement learning. By combining the decentralized features of edge computing and blockchain, the model enables automated data access control through smart contracts. Furthermore, it optimizes leader node selection in the Raft consensus process using a DQN, formulating the consensus as a Markov Decision Process to enhance responsiveness and privacy protection in dynamic network conditions. Experiments were performed in a simulated environment using TensorFlow 2.6 and a MySQL database. The performance of DQN-Raft+ was compared against traditional consensus algorithms, including Proof of Work, Proof of Stake, Practical Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. Results indicate that DQN-Raft+ significantly reduces block generation delay (175.77 ms) and achieves a high privacy protection score (0.95). It also maintains a low data loss rate of 0.01%, demonstrating enhanced robustness and real-time capability. These findings indicate that DQN-Raft+ effectively strengthens data security and privacy in IoT systems, offering a technically sound and efficient mechanism for secure data exchange. The study provides both a theoretical framework and practical direction for future research in secure IoT deployment.
S. Raghavendrachar, Gagan Shivanna, V. Aditya, H. N. Karthik · 5 authors
Financial transparency is a persistent challenge for NGOs, leading to trust issues, inefficiencies, and fund mismanagement. Trust Block is a blockchain-based web application designed to enhance accountability by enabling real-time tracking of donations and fund allocations through Ethereum blockchain, smart contracts, and decentralised ledgers. Built with React.js, Node.js, and MongoDB, and secured using Ganache and Solidity, the system automates financial processes, minimises human intervention, and ensures immutable transaction records. By providing real-time insights to donors and regulatory bodies, Trust Block fosters trust and financial integrity in the NGO sector, demonstrating the power of Web3 technologies in creating secure and transparent transactions.
This paper presents a comprehensive comparative analysis of three prominent blockchain networks: Solana (SOL), Ethereum (ETH), and SUI. The study examines transaction speeds, costs, transaction volumes, and evaluates the benefits and disadvantages of each cryptocurrency in real-world applications. Through detailed analysis of technical specifications, market performance, and ecosystem development, this research provides insights into the relative strengths and weaknesses of these blockchain platforms as they compete for market dominance in 2025.
The acceleration and widespread application of wireless networks underscore the pressing need for innovative solutions in wireless resource management, with blockchain emerging as a transformative approach due to its decentralized and secure nature. Our paper provides a panorama of blockchain-driven wireless resource management and emphasizes critical aspects such as spectrum utilization, infrastructure sharing, and interference management. We analyze representative models, including optimization, game model, auction mechanism, and machine learning, with mathematical formulations for a deeper understanding of blockchain’s impact. These methodologies underpin flexible and adaptive resource allocation strategies tailored for dynamic and high-demand environments. Recognizing the challenges of integrating blockchain with wireless networks, on the one hand, we investigate advanced blockchain solutions at both Layer 1 and Layer 2 that enhance scalability and adaptability for large-scale networks. On the other hand, we also exploit networking technologies, such as virtualization, network slicing, etc., to ensure seamless compatibility with blockchain. Furthermore, through several typical application scenarios, our paper illustrates the adaptability and practical benefits of blockchain in next-generation wireless networks. At last, this survey offers significant insights into blockchain’s role and potential in the evolution of wireless communication and outlines future research directions to facilitate a seamless transition to 6G.
This research paper is a study of creating a blockchain-based system that would make data more secure and consistent in the decentralized information computing system. By pursuing qualitative exploratory research methodology, the study combines the findings of eight adaptively designed interviews with blockchain developers, enterprise IT managers, legal practitioners, and academic researchers. Thematic analysis disclosed five major dimensions defining secure blockchain architecture: Security Mechanisms, Scalability, Governance, Regulatory Compliance and Privacy & Confidentiality. Results indicate the need to focus on strong cryptographical protection, optimization, adaptive governance, legal alignment, and privacy-preserving protocols, including zero-knowledge proofs. Technical and institutional obstacles were identified by the stake holders, and it was noted that a balanced and modular framework that has capability of satisfying various operational and regulatory requirements is sought. This research adds a conceptual model informed by stakeholders and advises a simulation and implementation trial as the next stage to examine the applicability of the model in practice. The results present both theoretical and practical advice on the design of safe, scalable, and regulatory-compliant architectures of blockchain environments in distributed systems.
With the growing demand for blockchain technology, the deployment of various applications has highlighted the critical chal- lenge of balancing scalability, security, and decentralization—termed the blockchain trilemma. To ensure blockchain’s effectiveness in real-world scenarios, resolving this trilemma with minimal trade-offs is crucial. Despite recent advancements, existing solutions, including Filecoin’s decentralized storage, have not fully addressed these challenges. While Filecoin reduces on-chain data re- dundancy through cryptographic proofs (PoRep/PoSt), its throughput remains insufficient (< 50 TPS) and vulnerable to adversarial attacks like selfish mining. In this study, we propose a novel Filecoin-based architecture that tackles the trilemma by reducing trans- action size to 50.6 bytes via Data Identification Numbers (DINs), achieving 145 TPS (a 20 × improvement over baseline Filecoin) with 1 MB blocks while maintaining 80% chain quality under 45% adversarial influence. Our approach enhances decentralization by minimizing storage requirements (292 bytes per reference block) and lowering hardware demands for mining nodes ($500/n- ode). Cost-efficiency analysis demonstrates a 99.9% reduction in energy consumption (0.001 kWh/Tx) compared to Bitcoin’s PoW (1,200 kWh/Tx) and a 3,448 × improvement in storage efficiency over baseline Filecoin. Security is preserved through PoRep/PoSt optimizations, resisting double-spending and Sybil attacks. Theoretical and empirical evaluations, including adversarial simulations and comparisons with Bitcoin, Ethereum, and Filecoin, validate unprecedented scalability-security-decentralization trade-offs. This work sets a new benchmark for blockchain systems, enabling decentralized applications to rival centralized systems in throughput, cost, and robustness.
Blockchain technology has rapidly evolved as a decentralized solution offering high security and transparency; however, several challenges still hinder the effective management of workflows within blockchain based environments. This study aims to develop an adaptive workflow management model that utilizes decentralized artificial intelligence (AI) and distributed ledger technology (DLT) to enhance the performance, security, and flexibility of processes in blockchainn networks. A mixed method approach combining simulation and experimentation on a dedicated blockchain platform was employed. The adaptive workflow model consists of a realtime process monitoring module, a decentralized AI module for adaptive decision making, and a DLT component that ensures data consistency and security. Statistical methods and system performance evaluations were used to analyze the experimental data. Results show that the proposed model can reduce workflow response times by up to 25% and increase the successful execution rate of smart contracts to 98%. Moreover, the integration of decentralized AI optimizes workload distribution across nodes, enabling network scalability improvements of up to 150% without significant performance degradation. The findings demonstrate that the adaptive workflow model combining AI and DLT enhances the flexibility and governance of blockchain networks through AI’s predictive capabilities and DLT’s security. Nevertheless, challenges such as high computational resource demands and technical complexities must be addressed. This research opens opportunities for further development to expand the scope of complex and dynamic blockchain applications and supports their integration with technologies like the Internet of Things (IoT).
With advances in the edge computing (EC) and federated learning (FL) technologies in jointcloud, the edge FL service market has emerged recently and it requires trading edge resources between model requesters and data owners to complete FL tasks, which needs to incentivize sufficient data owners to participate in model training tasks. However, the limitations of resource trading and incentive design for edge FL service market have not been well addressed. In this paper, we propose a two-level blockchain-aided resource trading mechanism for encouraging appropriate edge servers to compete for dynamic FL tasks from the market while incentivizing data owners to participate in the FL tasks. At the upper level, we apply the deep learning-based reverse auction to model the dynamics of the task server selection process, with the aim of maximizing the total social welfare of the edge FL service market, where the edge server, as a seller, considers not only the data contribution of edge devices but also the cost of using blockchain when bidding. At the lower level, the edge servers offer rewards in exchange for the data owners’ participation, while the parameter aggregation is completed through the blockchain in a decentralized manner, which improves the FL’s robustness. Then, we utilize the Stackelberg game to model the dynamic process that the data owners compete for the servers’ revenue. We conduct extensive simulation experiments and the experimental results show that the proposed mechanism is able to get maximized social welfare and provide effective insights and strategies for the resource trading in the edge FL market to complete the federated training.