Blockchain Papers

Seyed Mahdi Hosseini, Ali Broumandnia, Ramin Karimi

No abstract is available for this record.

Rutuja Chirwatkar, Beemkumar Nagappan, L. P. Singh, Anoop Dev · 6 authors

Another recent paradigm for enabling ubiquitous smart cities to be efficient, resilient, and innovative is Distributed Resource Management (DRM), in which heterogeneous devices, infrastructures, and services operate autonomously and continuously. This paper discusses more advanced concepts of edge-cloud synergy, decentralized coordination, cyber-physical integration, and context-aware optimization to address the increasing burden on urban energy, transportation, communication, and environmental systems. These are the main objectives: (1) to create scalable DRM frameworks with the features of real-time decision, (2) to enhance the interoperability of distributed heterogeneous resources, and (3) to enhance sustainability and service quality as a result of flexible allocation schemes. The proposed solutions will be the multi-agent systems, distributed ledger technologies (DLT), machine-learning-based prediction systems, and dynamic resource-orchestration algorithms. A hybrid simulation-prototype was applied to test the performance based on the metrics of latency, reliability, load balancing, and energy efficiency. Results suggest that significant improvements (up to a 35 percent reduction in resource contention, a 28 percent reduction in response time, and a 20 percent increase in system robustness under high-density urban workloads) have been achieved. A qualitative measure also fosters greater transparency and trust in cross-domain operations. In totality, the paper identifies that the disruptive potential of decentralized management systems can make smart-city ecologies adaptive, secure, and sustainable.

Monika Malik

The artificial intelligence (AI) infrastructures have been centralized leading to limited accessibility, monopoly of computational resources, and an uneven distribution of services. CloudChain is a decentralized AI compute market that is made out of blockchain and can solve these challenges with a transparent, trustless, and fair system. It brings together decentralized storage, smart contracts, as well as token incentives to allow fairness, privacy, and auditing. Privacy is ensured through encryption and zero-knowledge proofs, task allocation, distribution of rewards and enforcement of SLA is automated through smart contracts. The performance metrics measured in a 30-day simulation of the major cloud providers (AWS, Google Cloud, Azure, Hetzner, Lambda Labs) and the community nodes included the performance measures of latency, throughput, and SLA compliance, as well as token allocation and resource utilization. Findings indicate that CloudChain does provide the necessary balance in the workloads, high quality in the service delivery, and equitable rewards among heterogeneous members. The suggested framework envisioned will create a democratized, secure, and sustainable platform of decentralized AI, enabling innovation, openness, and diversity of global AI ecosystems.

Priya D. Banerjee

The increasing demand for real-time decision analytics in modern enterprises has accelerated the development of edge-to-cloud data pipelines, which integrate distributed computing resources to enable instantaneous insights. Traditional centralized cloud architectures struggle with latency and bandwidth limitations, making them unsuitable for applications requiring immediate decision-making. Edge-to-cloud pipelines overcome these barriers by combining localized data processing with cloud-based intelligence, creating a continuous, adaptive flow of analytical information. This review examines the architectural principles, technological enablers, and analytical impacts of edge-to-cloud data pipelines on real-time decision-making. It explores how distributed processing, stream analytics, and AI-driven orchestration enhance responsiveness, reliability, and scalability across diverse environments. Technologies such as 5G, machine learning, and containerized orchestration platforms are discussed as key drivers of this transformation. The study also identifies challenges including data synchronization, security, interoperability, and energy efficiency at the edge. Addressing these issues is essential for realizing seamless, end-to-end analytics across hybrid ecosystems. Future directions highlight the potential of autonomous, decentralized, and quantum-enhanced data pipelines to deliver self-optimizing intelligence at global scale.Ultimately, this review concludes that edge-to-cloud data pipelines are foundational to achieving context-aware, predictive, and autonomous analytics, enabling organizations to transition from reactive operations to real-time, intelligent decision ecosystems.

Chavan, Atharv, Parande, Nikhil

This research paper presents the design, architecture, and evaluation of a Hybrid Blockchain Database System, a decentralized–distributed model that integrates blockchain’s immutability with the high-performance data processing capabilities of traditional database systems. The proposed hybrid architecture addresses key limitations of fully on-chain storage—such as scalability constraints, high transaction costs, and limited query efficiency—by combining on-chain verification layers with off-chain distributed storage engines. The system introduces a multi-layered architectural model consisting of: (1) a blockchain consensus and validation layer for ensuring trust and tamper-resistant auditability; (2) a distributed database layer for high-speed read/write operations; (3) an interoperability layer that synchronizes state between on-chain and off-chain data; and (4) an IPFS/Filecoin-based decentralized storage framework for storing large datasets and metadata. This design enables secure state anchoring, cryptographic data integrity, and scalable data indexing across heterogeneous storage environments. A detailed performance evaluation is conducted using a combination of Ethereum-compatible smart contracts, IPFS storage, and distributed NoSQL/SQL databases. Key metrics such as transaction throughput, read/write latency, storage overhead, and consistency verification time are analyzed under varying workloads. Experimental results demonstrate that the hybrid system improves data scalability by 70–85%, reduces storage costs by over 90%, and maintains strong auditability through periodic blockchain state commitments. Security analysis is performed using static and dynamic testing tools to identify common vulnerabilities such as broken access control, inconsistent state updates, or missing verification boundaries, ensuring robustness of the hybrid synchronization logic. Additionally, the paper discusses design trade-offs involving decentralization vs. performance, on-chain transparency vs. gas efficiency, and consistency vs. latency across distributed components. This work provides a comprehensive blueprint for the development of real-world hybrid blockchain database systems suitable for supply chain management, enterprise data systems, government registries, financial applications, and Web3 infrastructure. The results highlight how hybrid architectures can bridge the gap between decentralized trust and scalable data management in next-generation digital ecosystems.

Zirui Wu, Sheng Cao, Shuyin Xia, Butian Huang · 6 authors

Blockchain scalability remains constrained by cross-shard transaction overhead and workload imbalance in conventional sharding architectures. To address these limitations, we propose GBShard, an adaptive sharding method grounded in granular-ball computing theory. The method partitions blockchain transactions to shards through iterative splitting and merging of coarse-grained granular-ball structures, enabling adaptive shard formation with minimized cross-shard transactions. A multi-granularity dynamic sharding algorithm further optimizes topology by redistributing granular-ball groups in dynamic scenarios, achieving incremental sharding adjustment while reducing the overhead of global-repartitioning. Experimental validation using real-world Ethereum transaction traces demonstrates GBShard's superiority over Monoxide-based sharding schemes: it achieves 1.3–1.8× higher throughput, 34–42% lower transaction latency, and reduces cross-shard transactions by 23–50%. These results suggest granular-ball partitioning as a viable strategy for adaptive blockchain scaling.

Madhav Ajwalia, Parth Shah

Blockchain technology has fueled exponential growth across various industries, including finance, supply chain management, and healthcare, enabling greater transparency in transaction management and supporting decentralized implementations. This paper presents a comprehensive performance analysis of permissioned and permissionless blockchain platforms, specifically Hyperledger Fabric and Ethereum. The study evaluates these platforms with varying transaction workloads (100 to 1000 transactions) with a consistent network. Our objective is to measure key performance metrics such as send rate, throughput, latency, resource utilization, and transaction success rate using established benchmarking tools and methodologies. The findings offer valuable insights into the comparative strengths, limitations, and optimal use cases of these blockchain platforms across different performance parameters. The results indicate that Hyperledger Fabric achieves, on average, 3.5–4.5 times higher throughput and 10–12 times lower latency than Ethereum, while consuming 2.5–3 times less memory across tested workloads. In contrast, Ethereum demonstrates a higher send rate and lower CPU demand in some operations. Overall, the study suggests that Hyperledger Fabric is better suited for enterprise applications that demand high scalability and performance.

Abdel Badeeh M Salem

Transaction fees play a crucial role in determining the efficiency and scalability of blockchain networks, particularly in Ethereum, where gas fees fluctuate significantly due to network congestion and competitive bidding. This study analyzes transaction fee patterns in the Ethereum blockchain and their impact on network efficiency by examining key blockchain metrics such as block density, transaction size, and transaction fee variability. The findings indicate that the mean transaction fee is 0.0342 ETH, with a median of 0.0008 ETH, demonstrating significant fee variability. The study also finds a strong positive correlation (r ≈ 0.75, p < 0.01) between transaction fees and block density, as well as a moderate correlation with transaction size (r ≈ 0.58, p < 0.01), highlighting the direct impact of network congestion on fee structures. Time series forecasting with Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models reveals cyclical trends in transaction fees, often influenced by major network activities such as NFT releases, DeFi protocol surges, and high-frequency trading. The LSTM model achieves a lower RMSE (0.09) compared to ARIMA (0.15), demonstrating its superior predictive capability for fee trends. Additionally, anomaly detection techniques identify outlier transactions with fees exceeding 2.5 ETH, often associated with front-running strategies, priority gas auctions (PGA), and inefficient smart contract executions. Despite improvements introduced by EIP-1559, the findings indicate that Ethereum’s transaction fee market remains highly volatile, with block density fluctuating between 512.0% and 3896.0%, causing extreme fee spikes during congestion periods. The presence of large transactions (maximum size: 250 bytes) further amplifies fee inefficiencies, reinforcing the need for improved scalability solutions. This study underscores the necessity of Layer-2 rollups, dynamic block size adjustments, and more adaptive fee mechanisms to enhance blockchain efficiency. Future research should explore comparative studies across blockchain networks, advanced predictive modeling techniques, and the role of miner extractable value (MEV) in transaction ordering fairness. The study’s insights provide valuable guidance for developers, users, and policymakers aiming to optimize Ethereum’s transaction fee structure and enhance overall blockchain performance.

Aytekin Işman

This study examines the relationship between gas prices and transaction values on the Ethereum blockchain, providing a detailed analysis of transaction dynamics and the factors influencing gas price determination. The correlation coefficient between gas prices and transaction values is -0.0273, indicating a very weak and negative relationship. Instead, gas prices are driven by factors such as computational intensity, network congestion, and user prioritization. Functions with higher computational demands, such as mint, recorded the highest mean gas price of 120.45 Gwei, with a standard deviation of 15.30 Gwei, while functions like approve and transfer exhibited mean gas prices of 98.30 Gwei and 110.80 Gwei, respectively. Recipient address analysis reveals a strong concentration of transaction values, with the top recipient address receiving 49.95 ETH consistently, indicating high-value operations directed toward specific accounts. High-gas transactions, defined as those above the 90th percentile, displayed a mean gas price of 191.96 Gwei with minimal variability, while their corresponding transaction values varied widely, with a mean of 23.91 ETH and a standard deviation of 13.66 ETH. These findings provide critical insights into Ethereum transaction behavior, emphasizing the role of function type and user prioritization in shaping gas price decisions. Future research should investigate the impact of network upgrades such as EIP-1559, the adoption of Layer-2 scaling solutions, and temporal trends in transaction behavior to enhance network scalability and cost efficiency as Ethereum continues to evolve.

Yuqin Wang, Jiannong Cao, Shaocheng Chu, Hongbo Liu · 6 authors

Web3, referring to the next-generation decentralized web, has gained extensive attention from industry and academia. Its popularity drives considerable demand for continuous development and delivery of web3 applications and services. Blockchain is an essential technology that enables web3 but comes with considerable operational cost. Blockchain-as-a-service (BaaS) is considered as a promising solution for supporting web3 applications; however, existing BaaS platforms are conceptual, built for specific applications, or not optimized for emerging web3 applications. This work analyzes the primary features and fundamental requirements of decentralized web3 applications and articulates the critical role of blockchain for web3. To fill the gap, we introduce DAPPaaS, the first customizable BaaS platform specializing in web3 applications, with carefully designed goals and principles to meet the goals. Our key technical contributions lie on the approaches enabling component modularization, communication efficiency, automated scaling and performance monitoring, distributed deployment, and resource optimization. We implement a real-world application based on DAPPaaS and conduct extensive performance evaluations to validate its utility and efficiency.

Davide Patti, Salvatore Monteleone, Enrico Russo, Maurizio Palesi

No abstract is available for this record.

Matteo Bjornsson, Taylor Hardin, Taylor Heinecke, Marcin Furtak · 6 authors

Distributed ledger technologies (DLTs) rely on distributed consensus mechanisms to reach agreement over the order of transactions and to provide immutability and availability of transaction data. Distributed consensus suffers from performance limitations of network communication between participating nodes. BLOCKY ZipperChain guarantees immutability, agreement, and availability of transaction data, but without relying on distributed consensus. Instead, its construction process transfers trust from widely-used, third-party services onto ZipperChains's correctness guarantees. ZipperChain blocks are built by a pipeline of specialized services deployed on a small number of nodes connected by a fast data center network. As a result, ZipperChain transaction throughput approaches network line speeds and block finality is on the order of 500 ms. Finally, ZipperChain infrastructure creates blocks centrally and so does not need a native token to incentivize a community of verifiers.

Chenggang Mu, Tao Ding, Zhuopu Han, Shanying Zhu · 5 authors

Distributed computing faces a persistent multi agent trust dilemma. In the computation process, participants may maliciously attack the system for personal gain by providing false data. Blockchain provides a possible solution for this problem with its immutability and multi-party consensus. However, existing blockchain data throughput has long been queried owing to its exorbitant time and energy costs by consensus mechanisms. This paper proposes a light blockchain structure in distributed computing scenarios. A No-Proof consensus (NPC) mechanism is designed for distributed computing problems with no extra proving process such as Proof-of-Work or Proof-of-Stake. This consensus mechanism notices that the distributed computing result has proven to be valid in the computation process automatically, which does not need to be verified again in the consensus mechanism. Further, the single-threaded data processing ability of the blockchain structure certainly leads to low efficiency when applied to distributed computation problems. An NPC-based blockchain is constructed in this paper to solve this problem. In this structure, the distributed computing is done off chain, and an oracle is designed to upload the computing results to the blockchain asynchronously. Upon the contribution in this paper, a distributed energy trading model is provided as a case study to verify the superiority of the designed blockchain in contrast with other similar structures.

Neeraj Purushotham, Mallepula Likhitha, Y Sai Pragathi, C RaviKiran · 5 authors

Secure and verifiable cloud storage auditing is a significant issue of concern with identity-based systems, especially against malicious auditors and forged proof attacks. In this paper, the enhanced identity-based public audit protocol has been enhanced with blockchain-supported federated trust and quantum resilient cryptographic priming. The suggested Quantum-Resistant Federated Identity-Based Auditing (QFIBA) scheme does not have a single-point dependency on the PKG and proposes a lattice-based identity encryption that ensures post-quantum security. The proposed system implements privacy-preserving audits via zero-knowledge verification made using zk-SNARK without exposing user metadata. The results of experiments carried out on the JPBC and CRYSTALS-Dilithium schemes show that the tag generation cost is reduced by 19.6 per cent, the proof generation overhead is reduced by 14.2 per cent and the verification rate is 12.8 times faster than the better IBPA scheme with the same audit soundness and forgery resistance. Security guarantees: It has been shown that QFIBA is secure to the CDH and LWE assumptions. Such findings confirm the practical effectiveness of the scheme and its resilience to the future cloud ecosystems where federated trust and quantum resilience is needed.

Mingrui Cao, Bin Cao, Mugen Peng

As the application fields of permissioned blockchains broaden and the integration of related industries accelerates, there is a rising demand for permissioned blockchains to support scalable networks. This paper proposes a Partitioned, Parallel and Practicable permissioned blockchain, called as P3-Chain, which builds upon a multi-shard two-tier architecture. Its key design insight is to extend scalability in terms of consensus algorithm protocol, architecture, and scheduling. In particular, P3-Chain employs a dual-consensus algorithm with decoupled intra- and inter-shard operations, allowing them to run in parallel and asynchronously under practical scenarios. To resolve the conflicting transaction problem brought by this decoupled dual-consensus algorithm, P3-Chain incorporates a state-access locking mechanism. P3-Chain is implemented in Golang across multiple OSs, and it is evaluated on Hyperledger Caliper testbed, ensuring standardized and fair benchmarking. Through extensive experiments, the results indicate that P3-Chain can achieve TPS$3.3\times $that of FISCO,$3.3\times $that of partitioned FISCO,$2.7\times $that of Fabric,$2.3\times $that of AHL+,$2.2\times $that of SharPer and$7.4\times $that of Ethereum when system contains 32 nodes. Meanwhile, within the same experimental settings, P3-chain is scalable to 1024 nodes successfully, while Fabric and FISCO run with 64 nodes only. Furthermore, P3-Chain only sacrifice less than a 10% performance when the system scale expands$256\times $from 4 to 1024.

Eunchan Park, Taeung Yoon, Hocheol Nam, Deepak Maram · 5 authors

In timing-sensitive blockchain applications, such as decentralized finance (DeFi), achieving first-come-first-served (FCFS) transaction ordering among decentralized nodes is critical to prevent frontrunning attacks. Themis [CCS'23], a state-of-the-art decentralized FCFS ordering system, has become a key reference point for high-throughput fair ordering systems for real-world blockchain applications, such as rollup chains and decentralized sequencing, and has influenced the design of several subsequent proposals. In this paper, we critically analyze its core system property of practical batch-order fairness and evaluate the frontrunning resistance claim of Themis. We present the Ambush attack, a new frontrunning technique that achieves nearly 100% success against the practical batch-order fair system with only a single malicious node and negligible attack costs. This attack causes a subtle temporary information asymmetry among nodes, which is allowed due to the heavily optimized communication model of the system. A fundamental trade-off we identify is a challenge in balancing security and performance in these systems; namely, enforcing timely dissemination of transaction information among nodes (to mitigate frontrunning) can easily lead to non-negligible network overheads (thus, degrading overall throughput performance). We show that it is yet possible to balance these two by delaying transaction dissemination to a certain tolerable level for frontrunning mitigation while maintaining high throughput. Our evaluation demonstrates that the proposed delayed gossiping mechanism can be seamlessly integrated into existing systems with only minimal changes.

R. K. Shyamasundar

No abstract is available for this record.

Wed Kadhim Oleiwi, Alharith A. Abdullah

Abstract Distributed Software Define Network (DSDN) offers to eliminate the single point of failure present in the centralized SDN controller. The main concept is to have numerous controllers that can share the burden on the network, and one controller can take over another controller when it breaks. In this study, we work on an Opendaylight-based completely distributed system to deal with issues of the controller's central processing unit (CPU) consumption being uneven. Every controller administers its own domain therefore sharing the burden across the network's controllers. To update the flow rules and broadcast new rules to all the controllers at once, maintaining the consistency of the controller's rule set, we create an application ledger that maintains a distributed ledger. Results demonstrate that, in addition to ensuring the uniformity of inventory shards across all controllers in the system, our work presented an increase with throughput reaching 40% and the bandwidth of controller's communication is less with almost 31%.

Arman Petrosyan

The paradigm of enterprise analytics is undergoing a fundamental shift from centralized, reactive reporting to distributed, proactive intelligence. This review article evaluates the integration of machine learning models within SAP business intelligence frameworks operating across multi-cloud and hybrid environments. We analyze how the transition toward a federated data architecture, facilitated by SAP Datasphere, enables the deployment of high-performance neural networks without the traditional constraints of data replication. The study specifically examines the efficacy of Long Short-Term Memory units for temporal forecasting in SAP Integrated Business Planning and the role of unsupervised learning models in real-time financial anomaly detection. Furthermore, we explore the rise of augmented analytics and natural language processing in democratizing data access, alongside the operational necessity of MLOps to mitigate model drift in volatile global markets. The review also addresses critical technical and strategic barriers, including data latency across distributed cloud nodes, the harmonization of structured and unstructured data, and the evolving landscape of global data sovereignty. By synthesizing current performance benchmarks with future directions such as agentic intelligence and the integration of carbon accounting through the green ledger, this research provides a roadmap for architecting autonomous analytical ecosystems. We conclude that the convergence of machine learning and distributed cloud infrastructure is the primary catalyst for transforming raw enterprise data into a strategic, self-optimizing asset.

Keyhan Mohammadi, Reza Ebrahimi Atani

This paper presents TEOM (The Evolutionary Open Machine), a decentralized operating protocol designed for smart enterprise architecture. TEOM enables secure, scalable, and flexible integration across diverse organizational functions, from AI and IoT to finance and governance. By applying a combination of Hashgraph and blockchain technologies, TEOM provides a robust framework for decentralized service delivery, real time data sharing, and multi organization collaboration. It supports the creation of autonomous, fault tolerant ecosystems that scale effortlessly, ensuring high availability and fault resistance across distributed networks. TEOM’s extensibility allows seamless integration of legacy systems and third-party platforms, promoting interoperability within smart enterprise environments. The protocol also integrates advanced AI capabilities for intelligent decision making, predictive analytics, and federated learning. Through automated service provisioning and decentralized transaction management, TEOM fosters an agile, transparent, and adaptive framework for modern enterprise architecture, empowering organizations to operate efficiently in a zero-trust environment.

Tirthankar Sengupta, Bishakh Chandra Ghosh, Sandip Chakraborty, Shamik Sural

Blockchain interoperability is increasingly recognized as the centerpiece for robust interactions among decentralized services. Blockchain ledgers are generally tamper-proof and thus enforce non-repudiation for transactions recorded within the same network. However, such a guarantee does not hold for cross blockchain transactions. When disruptions occur due to malicious activities or system failures within one blockchain network, foreign networks can take advantage by denying legitimate claims or mounting fraudulent liabilities against the defenseless network. In response, this paper introduces InterSnap, a novel blockchain snapshot archival methodology, for enabling auditability of cross blockchain transactions, enforcing non-repudiation. InterSnap introduces cross-chain transaction receipts that ensure their irrefutability. Snapshots of ledger data along with these receipts are utilized as non-repudiable proof of bilateral agreements among different networks. InterSnap enhances system resilience through a distributed snapshot generation process, need-based snapshot scheduling process, and archival storage and sharing via decentralized platforms. Through a prototype implementation based on Hyperledger Fabric, we conducted experiments using on-premise machines, AWS public cloud instances, as well as a private cloud infrastructure. We establish that InterSnap can recover from malicious attacks while preserving cross chain transaction receipts. Additionally, our proposed solution demonstrates adaptability to increasing loads while securely transferring snapshot archives with minimal overhead.

Ling Cao, Wenlong Shen

As blockchain technology deepens its integration into various fields, the challenges of blockchain as an isolated distributed ledger are becoming increasingly prominent. Effectively breaking the isolation of various blockchain ledgers and enabling information flow and value transfer between blockchains is a key area of current blockchain research. Hash timelocks are a key technology for achieving this cross-chain nature, offering advantages such as decentralization, ease of implementation, and high cross-chain efficiency. However, the hash timelock mechanism still suffers from issues such as the inability to consistently match cross-chain transaction partners and the inability to transfer assets. This severely impacts the user experience and hinders the usability of hash timelocks in cross-chain transactions. Therefore, building on existing hash timelock technology, this paper proposes a mechanism that utilizes an intermediate user pool to enable instant cross-chain asset transfers. This mechanism also incorporates a dynamic transaction matching algorithm to achieve efficient transaction matching. This mechanism not only enables decentralized cross-chain asset transfers, but also eliminates the indeterminate waiting time required for matching by traditional hash timelock mechanisms. Finally, experiments demonstrate the feasibility of this new mechanism, demonstrating significant efficiency advantages for small and medium-sized cross-chain transactions, eliminating the need for additional transaction matching time.

University of Salamanca

In complex environments such as those incorporating distributed and edge computing, middleware plays a critical role in meeting the communication and performance requirements of distributed systems by providing communication flow and integration capabilities. Its inherent advantages, such as abstraction of complexities, enhanced interoperability and scalability, make it ideal for managing tasks such as federated learning in edge AI environments. In addition, by supporting secure and energy-efficient operations, the middleware fosters sustainability, enabling green blockchain solutions and low-power distributed ledger technologies (DLTs) to thrive for managing dynamic ecosystems such as dAIEDGE. This deliverable D5.3, "Middleware prototype" presents the first version of dAIEDGE middleware. This work has been developed during the first year of dAIEDGE project from M4 to M16. In general, the document outlines the first version of the middleware developed collaboratively with task partners, by the University of Salamanca (USAL) as part of Task T5.2, "Middleware and Networks for Edge AI," within the dAIEDGE project. This task reflects a joint effort involving multiple participants, including BCA, BTH, CETIC, KUL, VICOM, and UEDIN.

Jing Shi, Xiaoying Bai, Wenzheng Zhang, Pei-Lun Li · 7 authors

No abstract is available for this record.