Distributed ledgers are common in the industry. Some of them can use blockchains as their underlying infrastructure. A blockchain requires participants to agree on its contents. This can be achieved via a consensus protocol. How do these protocols differ in performance, and how are the differences affected by the communication network? Moreover, such a protocol would need a timer to ensure progress, but how should the timer be set? This article presents an analytical model to address these and related issues when there are crash faults. Specifically, it focuses on two consensus protocols (Istanbul BFT and HotStuff) and two network topologies (Folded-Clos and Dragonfly). The model provides formulas that express the consensus time in terms of protocol and topology parameters. No other model in the literature provides such a global view of the parameter space. Analysis of the closed-form expressions yield new insights into how the timers should be set, how faults affect the consensus time, when one protocol is faster than the other, and how the two topologies differ in their impact. The formulas and analyses are validated with simulations. The conclusion also offers some tips for the analytical modeling of similar protocols.
Cloud computing is popular in modern businesses because it is scalable, versatile, and cost-effective. Due to the spread and complexity of cloud infrastructures, fault tolerance is crucial. This research focuses on improved cloud failure tolerance using Distributed Ledger Technology (DLT). Decentralised and immutable DLT offers verifiable and transparent transaction history, data consistency, and decentralised recovery, promising to improve cloud infrastructure stability and robustness. This study examines how DLT improves cloud-based detection, recovery, and fault tolerance. Using blockchain and cloud services to create a fault-tolerant system is novel. Comparison of fault tolerance solutions in existing cloud settings with DLT integration is the plan. Data was obtained during failure testing simulations using genuine cloud infrastructure platforms and blockchain networks. Recovery time, defect detection, energy utilisation, and uptime were measured. Graphviz was used to create flowcharts, and Matplotlib was utilised for graphs and plots. Test results show improved fault tolerance due to lower fault impact and faster recovery from DLT. DLT can make cloud systems more fault-tolerant and resilient, setting the pace for distributed computing innovation, the research concludes.
In recent years, sharding technology has been frequently applied in blockchain systems to increase scalability. However, when new shards are added, the system may result in significant overhead in terms of computing and networking since the data allocation approach is incompatible with dynamic changes in shards. Currently, S-Store, the state-of-the-art sharding solution built on the account model, has a high re-computing latency when growing shard numbers and an unbalanced sharded data distribution after growth. To address these issues, this paper presents Tangram, an efficient and balanced dynamic storage extension approach for sharding blockchain systems. Tangram reduces system extension overhead and latency while ensuring a balanced shard distribution. In implementing Tangram, we tackle three main technical challenges as follows. (1) Designing a novel state tree structure for the storage and maintenance of sharding state data. We introduce the Jump Merkle Tree (JMT) based on the Merkle Tree, which integrates node migration and orderliness. (2) Presenting a protocol to be compatible with dynamic shard scenarios. We devise a shard addition protocol to improve system extension availability and decrease shard extension delay. (3) Proposing an approach to guarantee system longevity after extension. We first devise algorithms for the state tree to eradicate invalid states after system expansion. Furthermore, we introduce a shard reduction protocol to enhance system storage extension support in complex scenarios, such as cleaning up inactive states to avoid bloating the state tree. We conduct extensive experiments to evaluate the performance of Tangram. Experiment results demonstrate that Tangram outperforms existing solutions, showing reduced latency and superior data balance. When compared to the state-of-the-art sharding storage solution, Tangram decreases the transaction execute time by up to 87.84%, the state data migration by more than approximately 74%, and achieves up to 7.63x improvement in the standard deviation of sharding data balance.
Blockchain-empowered end-edge collaborative computing is a promising technology for enhancing the timeliness and trustworthiness of Industrial Internet of Things (IIoT). However, integrating task offloading with blockchain consensus inevitably escalates resource consumption across communication, computation, and energy domains. Thus, the joint optimization of task offloading, resource allocation and blockchain consensus is very important for IIoT. This paper studies a general end-edge collaborative computing scenario with multiple end devices and multiple edge servers. We first propose a novel dynamic blockchain (DBC) scheme by developing a dynamic leader election mechanism and designing a dynamic consensus waiting time window. Then, by fully considering the constraints of multi-task size and deadline, communication bandwidth, computing frequency, battery capacity, Byzantine fault tolerant and trustworthiness, we formulate the trustworthy processing efficiency (TPE) maximization problem with respect to end-edge task division, communication and computation resource allocation, leader election and consensus waiting window. To address this problem, we transform it into a Markov decision process and design a compound reward by fully considering the penalty for computing timeout and consensus failure. After that, we propose a rotating multi-agent deep reinforcement learning (R-MADRL) algorithm tailored to the proposed DBC scheme, where an entropy-based dual-critic DRL algorithm is proposed for rotating multi-agent training and decentralized execution. Extensive experiments validate the effectiveness and superiority of the proposed DBC with R-MADRL, where three benchmark DRL algorithms and three blockchain consensus schemes are compared. The results demonstrate that R-MADRL achieves stable convergence with more than 60.32% TPE reward than other algorithms while the task timeout ratio of DBC is reduced by more than 66.49% compared with other schemes.
Distributed ledger technology (DLT), commonly referred to as blockchain, is emerging as a transformative technology solution for managing Virtual Power Plants (VPPs), which aggregate distributed energy resources (DERs). Smart contracts are a key feature of DLT that may be leveraged to streamline complex tasks, from resource onboarding to real-time market transactions, ensuring data reliability, interoperability, and transparency. Through adoption of DLT, utilities may enhance operational efficiency and stakeholder trust, creating secure, transparent ledgers of energy information exchanges, controls and transactions accessible to energy providers, consumers, regulators and other system stakeholders.The article provides an overview of the technology features, and an accessible and practical roadmap for integrating DLT into energy systems, outlining key steps from initial planning and transaction selection to deployment. Emphasizing real-world applicability, it illustrates DLT’s pivotal role in modernizing energy grids to meet evolving demands and sustainability goals.
This publication presents a novel cryptographic commit scheme named DFTWS which is used to enable deterministic, fair, and transparent winner selection in an open source Proof-of-Useful-Work blockchain for High Energy Physics (HEP) called gophy. In gophy, instead of spamming hashing operations to mine blocks, miners are running computationally expensive Monte Carlo simulations to support a real-world HEP experiment with necessary data required to conduct the experiment. To preserve the usefulness property, block problems are defined over time by a Root Authority which is coordinated by a representative of a real-world HEP experiment. In order to be able to provide a transparent mechanism that allows for fair block winner selection from a list of eligible miners that solved a block problem, DFTWS is employed to achieve consensus between nodes. A strength of this approach is that every node is able to verify the fairness of the winner selection process. This publication provides an in-depth description and theoretical fairness analysis of DFTWS, a practical evaluation of its performance under real-world conditions and considerations of potential bottlenecks that can potentially occur as the node network scales. It also discusses a deployment strategy for using DFTWS on top of existing blockchain infrastructure like the Ethereum network. Additionally, theoretical performance aspects of DFTWS are compared with various state-of-the-art cryptographic commitment schemes and Zero-Knowledge Proof systems.
H.C. Zhang, Shike Li, Shike Li, Hang Bao · 6 authors
The rapid development of blockchain technology has driven the widespread application of decentralized applications (DApps) across various fields. However, DApps cannot directly access external data and rely on oracles to interact with off-chain data. As a bridge between blockchain and external data sources, oracles pose potential risks of malicious behavior, which may inject incorrect or harmful data, leading to trust and security issues. Additionally, with the surge in data requests, the disparity in oracle trustworthiness and costs has increased, making the dynamic selection of the most suitable oracle for each request a critical challenge. To address these issues, this paper proposes a Trust-Aware and Cost-Optimized Blockchain Oracle Selection Model with Deep Reinforcement Learning (TCO-DRL). The model incorporates a comprehensive trust management mechanism to evaluate oracle reputation from multiple dimensions and employs an improved sliding time window to monitor reputation changes in real time, enhancing resistance to malicious attacks. Moreover, TCO-DRL uses deep reinforcement learning algorithms to dynamically adapt to fluctuations in oracle reputation, ensuring the selection of high-reputation oracles while optimizing node selection, thereby reducing costs without compromising data quality. We implemented and validated TCO- DRL on Ethereum. Experimental results show that, compared to existing methods, TCO-DRL reduces the allocation rate to malicious oracles by more than 39.10% and saves over 12.00% in costs. Furthermore, simulated experiments on various malicious attacks further validate the robustness and effectiveness of TCO-DRL
To solve the problem of blockchain interoperability, we propose an efficient decentralization oracle solution. We design a subgroup Schnorr multisignature for the off-chain aggregation mechanism of our system, which is proven to be secure and robust. Compared with threshold signature-based schemes, the subgroup multisignature-based scheme does not require the execution of the distributed key generation protocol, which greatly reduces off-chain costs. The on-chain verification of the Schnorr signature is also more efficient than the pairing-based signatures. Then, we design a fair incentive mechanism to encourage the oracle nodes to work hard to validate the external data, which is based on a repeated dynamic game. Compared with the existing incentive mechanisms, our mechanism considers the real actions of each node to determine whether it should be rewarded or penalized. We implement our system over the Ethereum blockchain, and the experiments show its good performance.
The rapid growth of IoT has increased the demand for large-scale data processing. However, traditional centralized methods struggle with real-time requirements and data security . This paper introduces VCD-TSNet, a novel real-time IoT data processing framework that combines blockchain and edge computing . By integrating deep learning models like VGG, ConvLSTM , and DNN , VCD-TSNet effectively performs spatial feature extraction, temporal modeling , and decision-making, while using blockchain to ensure data integrity and privacy. Experimental results demonstrate that VCD-TSNet outperforms baseline models in classification accuracy , prediction precision, and real-time performance. For instance, on the BoT-IoT dataset, the classification accuracy reaches 97.5%, throughput increases to 920 TPS, and response time stays below 85 ms. This study validates the model’s effectiveness and highlights its potential in large-scale IoT environments, offering efficient, secure solutions for real-time data processing. It also provides insights for future improvements in frameworks that combine edge computing with blockchain.
This article presents a comprehensive framework for integrating blockchain technology with Oracle SOA Suite to facilitate secure and efficient real-time financial transactions. The proposed architecture leverages distributed ledger technology's inherent security features alongside Oracle's robust service orchestration capabilities to address prevalent challenges in traditional payment systems. Through systematic implementation and rigorous testing, the article demonstrates significant improvements in transaction processing speed, security, and regulatory compliance. The integration framework incorporates smart contracts for automated transaction validation, enhanced payment messaging protocols, and optimized data processing pipelines. The article indicates that this hybrid architecture effectively reduces transaction settlement times while maintaining data integrity and meeting industry regulatory requirements. The article contributes to the growing body of knowledge in enterprise integration architecture and provides valuable insights for financial institutions seeking to modernize their transaction processing systems.
This article presents a novel framework for implementing decentralized identity management in microservices architecture using blockchain technology. The proposed solution addresses the inherent challenges of traditional centralized identity management systems by leveraging distributed ledger technology and smart contracts to create a secure, transparent, and user-centric authentication mechanism. The framework incorporates comprehensive privacy controls and consent management features while ensuring compliance with regulatory requirements across various industries. Through extensive evaluation across multiple use cases in healthcare, financial services, and government sectors, the results demonstrate enhanced security, improved scalability, and better user privacy control compared to conventional approaches. The article suggests that blockchain-based identity management can significantly reduce the risk of security breaches while providing a more robust and flexible authentication mechanism for modern distributed systems. This article contributes to the growing body of knowledge in distributed systems security and provides practical insights for organizations looking to implement decentralized identity management solutions.
Nabil A. Ismail, Shaimaa Abu Khadra, Gamal Attiya, Salah Eldin S. E. Abdulrahman
Abstract Blockchain technology offers a robust framework for integration with the Internet of Things (IoT), enhancing interoperability, security, privacy, and scalability in modern technological ecosystems. However, traditional cryptographic protocols used in blockchain systems are increasingly vulnerable to quantum attacks due to advancements in quantum computing. In response, the National Institute of Standards and Technology (NIST) has prioritized research in post-quantum cryptography, presenting challenges and opportunities for developing blockchain-based applications tailored to IoT devices. Among the post-quantum cryptographic schemes evaluated in NIST's third standardization round, the Supersingular Isogeny Key Encapsulation (SIKE) protocol stands out for its relatively small public and private key sizes. Despite this advantage, SIKE faces challenges related to high latency, necessitating efficient implementations to make it viable for real-world applications. This research focuses on optimizing the cryptographic foundations of blockchain networks to securely and efficiently integrate resource-constrained IoT ecosystems. By enhancing the SIKE protocol, which exhibits strong resistance to brute-force and whitewashing attacks, the study achieves significant performance improvements. Our FPGA-based implementation on the VIRTEX-6 XC6VLX760 demonstrates reduced latency, achieving a key generation time of 24 ms, encapsulation time of 72 ms, and decapsulation time of 73 ms for SIKEp434. These results highlight the feasibility of deploying SIKE-optimized blockchain networks in IoT environments with stringent resource constraints.
The increasing adoption of multi-cloud database systems has transformed enterprise data management, enabling enhanced scalability, reliability, and cost efficiency.However, managing databases across multiple cloud providers introduces significant challenges, including data fragmentation, latency, security vulnerabilities, and inconsistencies in synchronization.Traditional approaches to database management struggle to provide seamless interoperability, fault tolerance, and resilience against failures, necessitating innovative architectural solutions.This paper explores the design and implementation of resilient multicloud database systems, integrating Distributed Ledger Technology (DLT) for enhanced data integrity, fault tolerance mechanisms to ensure high availability, and cross-platform synchronization techniques for maintaining consistency across heterogeneous cloud environments.DLT, particularly blockchain, offers a decentralized approach to data validation, reducing the risk of tampering and unauthorized modifications while enabling transparent and auditable transactions.Fault tolerance strategies, including redundancy, self-healing systems, and predictive analytics, play a crucial role in mitigating system failures and ensuring business continuity.Additionally, cross-platform synchronization mechanisms, such as conflict-free replicated data types (CRDTs) and real-time consistency protocols, are explored to address latency and data consistency challenges across cloud infrastructures.By integrating these technologies, organizations can enhance the resilience, security, and operational efficiency of multi-cloud database architectures.This paper provides a comprehensive framework for implementing adaptive database management solutions, leveraging AI-driven automation, blockchain-based security, and advanced fault recovery models.The findings highlight best practices for enterprises aiming to achieve scalable, reliable, and fault-tolerant multi-cloud database environments.Future research directions include the role of edge computing in multi-cloud synchronization, quantum-safe cryptographic techniques for DLT security, and AI-driven predictive failure management in cloud-native databases.
Aiming to address the shortcomings of traditional blockchain technologies, characterized by high storage redundancy and low transaction query efficiency, we propose a lightweight sender-based blockchain architecture (LSB). In this architecture, the linkage between blocks is associated with the user initiating the transaction, and the hash of the newly generated block is recorded in the user’s wallet, thereby facilitating transaction retrieval. Each user node must store only the blocks that pertain to it, significantly reducing storage costs. To ensure the normal operation of the system, the Delegated Proof of Stake based on Reputation and PBFT (RP-DPoS) consensus algorithm is employed, establishing a reputation model to select honest and reliable nodes for consensus participation while utilizing the Practical Byzantine Fault Tolerance (PBFT) algorithm to verify blocks. The experimental results demonstrate that LSB reduces storage overhead while enhancing the efficiency of querying and verifying transactions. Moreover, in terms of security, it decreases the likelihood of malicious nodes being designated as agent nodes, thereby increasing the chances of honest nodes being selected for consensus participation.
This paper proposes a method of emulation of \verb|OP_RAND| opcode on Bitcoin through a trustless interactive game between transaction counterparties. The game result is probabilistic and doesn't allow any party to cheat, increasing their chance of winning on any protocol step. The protocol can be organized in a way unrecognizable to any external party and doesn't require some specific scripts or Bitcoin protocol updates. We will show how the protocol works on the simple \textbf{Thimbles Game} and provide some initial thoughts about approaches and applications that can use the mentioned approach.
Blockchain-enabled Policy Decision Point (PDP) has been a promising solution to the centralization concern in practical deployment of Attribute-Based Access Control (ABAC). However, existing blockchain systems cannot support PDP adequately since PDP functionalities introduce extra latency to blockchain’s execution process and limits system throughput. This paper proposes an efficient PDP Blockchain (PDPB) by exploiting a minimum-redundancy execution paradigm. Concretely, we design a novel Echo-Based Execution Conclude (EBEC) mechanism to enable minimum redundancy request evaluation while ensure blockchain safety and liveness. Two optimization techniques, Echo Compacting (EC) and Load Balancing (LB), are proposed to reduce the communication and computation overhead of PDPB and further enhance its performance. We implement a prototype of PDPB and evaluate it on Amazon Web Services (AWS) servers. The results show that PDPB achieves more than 35.6% performance improvement over existing methods.
Bitcoin constructs temporal order internally rather than synchronizing to any external clock. Empirical evidence shows that its time evolution is non-continuous, probabilistic, and self-regulated. Block discovery follows a stochastic process in which uncertainty accumulates during the search phase and collapses abruptly when a valid proof-of-work solution appears. Difficulty adjustment maintains the system near the entropy-maximizing regime and allows the network to infer the underlying global hash rate. Building on these observations, we present a unified framework in which Bitcoin time emerges from four interacting mechanisms: proof of work as a distributed entropy source, difficulty adjustment as temporal feedback, entropy collapse as discrete temporal updates, and recursive sealing through hash pointers. Together these mechanisms form a self-regulating temporal architecture that transforms distributed randomness into a coherent and irreversible global timeline, offering a generalizable foundation for autonomous timekeeping in permissionless systems.
The InterPlanetary File System (IPFS) offers decentralized storage and data sharing, which are critical for the functionality of Blockchain of Things (BCoT) systems. Despite its advantages, IPFS faces challenges such as scalability, latency, and resource management issues that hinder its effective integration into existing blockchain infrastructures. This study explores the implementation of Docker containerization to enhance IPFS performance within BCoT environments. An experimental testbed was established, comprising an IPFS node and an IPFS Cluster peer deployed as Docker containers, to evaluate the latency of file operations across various sizes and analyze containerization’s impact on data storage and retrieval efficiency. The proposed Dockerized IPFS implementation demonstrates substantial performance improvements over traditional systems, achieving latency reductions of up to 75% for small files (1–256 KB) and a three-fold decrease for larger files (64 MB). Specifically, write operations were reduced from 1000 ms to 300 ms, while read operations improved by 40%, decreasing from 2500 ms to 1500 ms. Additionally, the containerized approach yielded lower latency than previous standalone IPFS deployments. The study emphasizes the significance of dynamic resource allocation in optimizing resource utilization, thereby enhancing the overall performance of IPFS Clusters within BCoT frameworks. By leveraging Dockerized IPFS, BCoT systems can achieve more efficient off-chain storage solutions, facilitating improved data management and interoperability in decentralized applications.
Blockchain that is a decentralized data management platform is attracting much attention recently. Recent advancement of blockchain technology increases the demand and supply of Non-Fungible Tokens (NFTs) that enable management of ownership concepts for various digital contents. Especially in the metaverse, NFT contents such as images and videos are actively used to facilitate communication within the virtual space. However, systems and technologies that link NFT content in the metaverse with metaverse platforms and generate metadata (e.g., popularity of each content) from data created through user's activities in the metaverse (e.g., user attention, conversation audio) are not realized. Therefore, our research aims to provide highly reliable data regarding the popularity and evaluation function of NFT content. To achieve the objective, we focus on extending existing oracle mechanisms and developing a system that integrates NFT content and various data generated on the metaverse platforms. The oracle technology to handle data provision and verification processes allows users within the metaverse to mutually verify reliability of the data generated through interactions with NFT content. Additionally, the proposed platform enables the use of common devices (e.g., smartphones) as wallets for the blockchain. These common devices can be used to sign transactions through short-range wireless communication when sending data to the blockchain through devices constituting the metaverse platform. This approach ensures user's involvement in data transmission procedure while simultaneously enabling mutual verification of the transmitted data through the blockchain to achieve both wallet security and data reliability.
Muhammad Medhat Kamal, Saad M. Darwish, Adel A. El-Zoghabi
The rapid growth of blockchain-based applications (BoS) has transformed multiple sectors but also brought significant challenges in software testing, particularly for decentralized applications (DApps) and smart contracts. Current development tools primarily support unit testing and do not address the full range of testing needs for blockchain software. Given the complexity of DApps and the immutability of smart contracts, more advanced methods are required to ensure security, performance, and functional accuracy. This study reviews the current state of blockchain software testing, identifying major gaps and limitations in conventional testing frameworks. To address these challenges, we propose an innovative software testing framework that integrates machine learning to offer real-time, customized testing recommendations for blockchain applications. By utilizing key blockchain features, including distributed ledgers, cryptographic hashing, and decentralized consensus, our model enhances testing accuracy by identifying potential vulnerabilities, performance limitations, and functional discrepancies, reducing the risk of undetected defects in the immutable blockchain environment. Experimental assessments show substantial improvements in testing coverage when compared to established tools such as Truffle and Remix, particularly in the validation of smart contracts and identification of security vulnerabilities. Our framework accelerates the testing process and improves the reliability of blockchain applications by providing developers with comprehensive tools to address the unique challenges of decentralized systems. As blockchain continues to advance in sectors like finance, healthcare, and supply chain management, this study highlights the urgent need for sophisticated testing methods and establishes a foundation for future advancements. By combining machine learning with blockchain testing, we introduce a scalable and adaptable approach that can progress alongside developments in blockchain technology. The conclusion explores broader implications and suggests further improvements, including integration into active deployment pipelines and real-time testing in operational settings. This framework marks a significant advancement in ensuring the dependability, security, and scalability of decentralized blockchain applications, supporting the sustainable growth of these systems in the digital ecosystem.