Blockchain consensus algorithms face trade-offs between performance, security, decentralization, and energy efficiency. Proof-of-Work (PoW) ensures strong security but is energy-intensive. Proof-of-Stake (PoS) is efficient but may risk centralization. Byzantine Fault Tolerance (BFT) offers low latency but lacks scalability. This paper proposes an Adaptive Hybrid Consensus (AHC) algorithm that dynamically integrates PoW, PoS, and BFT elements. The AHC mechanism is designed for general-purpose blockchain environments and can adapt to real-time network conditions. AHC is a promising solution for next-generation blockchain systems as it has the potential to significantly improve latency, throughput, and energy consumption [13][14].
As Web3 technology rapidly evolves, data management efficiency has become a critical challenge because of the decentralized nature of blockchain technology, which requires extensive computational and storage resources. Existing research highlights that decentralized consensus mechanisms in blockchain, such as proof-of-work (PoW) and proof-of-stake (PoS), lead to significant data redundancy, storage limitations, and latency issues, making data management efficiency complex. Moreover, the need for security and immutability in blockchain data storage exacerbates these challenges as the system scales and unique challenges unfold in maintaining efficient data management. Using Monte Carlo simulation, we analyzed the model’s behavior under various scenario by manipulating key variables such as network latency, node distribution, data block size, consensus mechanism efficiency, and storage redundancy levels. Our findings indicate that consensus mechanism efficiency has the most significant impact on overall data management performance, followed by network latency and node distribution. Interestingly, although data block size shows a moderate impact, storage redundancy levels have a relatively minor effect on efficiency. These simulation studies provide insights into optimizing consensus protocols and improving network infrastructure in enhancing the efficiency of Web3 data management.
In the era of web3, blockchain based technologies that leverage the essence of data security and content distribution are transforming the data storage mechanism by eliminating the reliance on centralized servers. Interplanetary File System (IPFS) is one such technology which provides the mechanism to distribute the data without centralization. Many critical applications are being constructed on the principles of blockchain technology to provide data security, integrity and privacy. They are facing storage challenges owing to the data volume growth of transactions. In this research paper, we propose the paradigm shift towards moving forward a more decentralized, secure and censorship-resistant internet, paving way for the next generation of Web3 applications. Our proposed framework works on the Integrated principles of blockchain and IPFS to ensure data security and eliminate the storage cost and challenges. The blockchain sharing mechanism is build using the concept of real time connection for web (WebRTC) where the connection is established between trusted peers. The block consists of essential information required for file retrieval from the IPFS nodes and the password to decrypt the files. As the files are encrypted before uploading to the IPFS nodes using a strong encryption mechanism AES-256, it makes the framework secure from man in the middle attacks which try to fetch the files from the network while being shared.
S M Mostaq Hossain, Amani Altarawneh, Maanak Gupta
As blockchain technologies are increasingly adopted in enterprise and research domains, the need for secure, scalable, and performance-transparent node infrastructure has become critical. While self-hosted Ethereum nodes offer operational control, they often lack elasticity and require complex maintenance. This paper presents a hybrid, service-oriented architecture for deploying and monitoring Ethereum full nodes using Amazon Managed Blockchain (AMB), integrated with EC2-based observability, IAM-enforced security policies, and reproducible automation via the AWS Cloud Development Kit. Our architecture supports end-to-end observability through custom EC2 scripts leveraging Web3.py and JSON-RPC, collecting over 1,000 real-time data points-including gas utilization, transaction inclusion latency, and mempool dynamics. These metrics are visualized and monitored through AWS CloudWatch, enabling service-level performance tracking and anomaly detection. This cloud-native framework restores low-level observability lost in managed environments while maintaining the operational simplicity of managed services. By bridging the simplicity of AMB with the transparency required for protocol research and enterprise monitoring, this work delivers one of the first reproducible, performance-instrumented Ethereum deployments on AMB. The proposed hybrid architecture enables secure, observable, and reproducible Ethereum node operations in cloud environments, suitable for both research and production use.
Traditional systems for checking credentials are prone to forgery and inefficiencies, which demand the prompt need for secure, transparent, and tamper-proof solutions in professional and academic fields. This research introduces a decentralized system of managing certificates based on blockchain technology that ensures the eradication of forgery, data breaches, and prolonged processes of verification through tamper-proof solutions. With the features of blockchain technology combined, the system protects and verifies certificates using stored data which guarantees both integrity and authenticity. Other features include the implementation of smart contracts for automated certificate issuance, QR codes for real time verification, and a user-friendly design to enable easy engagement by students, institutions, and employers. In contrast to conventional systems, Ethereum blockchain deployment improves user empowerment, security, and transparency through the removal of intermediaries, Verification time minimization, and prevention of data tampering. This research provides a stable foundation for blockchain credential management, paving the way for its applications in various fields like education, health, and finance and creating an environment of trust and transparency. Keywords: Blockchain Technology, Certificate Verification, Tamper-proof, Smart Contracts, Transparency, Authentication
Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks. Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements. Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures. To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method. Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure. Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16 KB or more, outperforming state-of-the-art methods.
This study presents a comparative evaluation of five widely adopted blockchain consensus algorithms—Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Practical Byzantine Fault Tolerance (PBFT), and Proof of Authority (PoA)—based on energy efficiency and performance metrics. As blockchain systems scale into broader applications, key indicators such as transaction throughput (TPS), latency, security, centralization risk, and scalability become increasingly critical. Each algorithm is assessed through theoretical analysis, empirical data, and simulation-based studies found in the literature. The findings indicate that Proof of Work is the most secure, but it does not environmentally sustainable due to its energy needs. Proof of Stake and Delegated Proof of Stake are more energy-efficient and have a high throughput, but it can lead to centralization. A Practical Byzantine Fault Tolerant consensus algorithm is great in permissioned networks, but it doesn't scale well. Furthermore, those networks aren't really decentralized. Proof of Authority is the most environmentally sustainable option, but that energy efficiency comes at the cost of decentralization. This research highlights the lack of sustainable consensus mechanisms, that is, consensus protocols that are both secure and scalable and environmentally sustainable—as a gap that future research can address.
With the integrating development of Internet of Things (IoT) and edge computing, data sharing among various IoT devices has become the trend for extensive applications. However, data sharing in IoT environments is challenged by limited terminal resources and distributed data storage, which places higher demands on security and effectiveness. Even though existing searchable encryption technologies provide feasible solutions, there remain challenges in terms of trustworthy retrieval and execution efficiency. To address these issues, this paper proposes an efficient supply-demand-aligned and trustworthy multi-keyword (ESTM) search scheme in edge-assisted IoT environments, where encrypted documents are stored in edge servers. Furthermore, blockchain-based smart contracts are employed so that search results are consensus on the Fabric ledger and data users can verify whether the returned encrypted documents are reliable using encrypted hashes. To achieve the supply-demand-aligned requirement, the RoBERTa (Robustly optimized BERT approach) model is introduced for text classification and data users can judge which edge server stores data best suits their demands. Meanwhile, coordinate (COO) format is adopted into index vectors and search vectors, which can decrease the time required for constructing an index tree to about 2.7% and the time required for generating trapdoors to about 4.6%. Finally, we conducted an in-depth security analysis and performance comparison with existing works, results show that the proposed scheme is effective and feasible.
The emergence of blockchain technology has revolutionized decentralized data management by offering robust alternatives to traditional centralized database systems. This paper provides a systematic and comprehensive review of blockchain-based distributed databases, highlighting key architectural transformations, core enabling technologies such as Merkle Trees, PBFT, and Zero-Knowledge Proofs, and comparing them with conventional distributed databases. Real-world implementations including Hyperledger Fabric, BigchainDB, and OrbitDB are analyzed to assess their scalability, interoperability, and security capabilities. The paper also explores intrinsic security mechanisms, performance bottlenecks, and regulatory challenges that affect adoption. Finally, it identifies open research questions and future directions necessary for building scalable, privacy-aware, and interoperable decentralized database ecosystems suitable for enterprise and multi-stakeholder environments. Keywords— Blockchain databases, consensus mechanisms, data integrity, decentralized systems, distributed ledger, Merkle trees, Zero-Knowledge Proofs
Zhikang Guo, Heng Pan, Ang He, Yueyue Dai · 8 authors
Blockchain technology presents significant security challenges despite its transformative impact on digital transactions and decentralized data management. Key vulnerabilities include insecure smart contract execution, data privacy risks on transparent ledgers, and susceptibility of certain consensus mechanisms to attacks. Trusted Execution Environments (TEEs) offer a robust hardware-based solution to these critical issues. By providing isolated execution spaces, TEEs safeguard code and data confidentiality and integrity, thereby fundamentally strengthening blockchain security. This paper presents a comprehensive analysis of TEEs in blockchain technology. First, we analyze the challenges inherent in blockchain systems and demonstrate the advantages of TEEs over current methods. A detailed analysis of TEE properties, variants, and evolution in the blockchain field is provided. Additionally, we explore innovative TEE-based solutions across three key application domains: consensus mechanism optimization, confidential computation and execution, and payment networks and financial applications. Furthermore, we propose a research agenda addressing current challenges such as vulnerabilities to side-channel attacks and dependencies on hardware trust assumptions. Finally, we propose five critical directions for future TEE-blockchain integration: enhancement of security and privacy protection with particular attention to the Trusted Computing Base (TCB) minimization, performance optimization through hardware architecture advancement, trust model refinement to reduce centralization, expansion of application scenarios through interdisciplinary collaboration, and development of cross-chain interoperability standards. Our work contributes to blockchain security knowledge and provides a roadmap for researchers and practitioners in this rapidly evolving field.
LLMs have brought new, amazing abilities for understanding language, generating it and making decisions. Yet, there are serious concerns about data privacy, the ability to scale LLMs and how different components of a cloud-native system interact. The paper outlines a new Federated Data Modelling (FDM) framework specifically for making use of LLMs in secure and efficient distributed cloud settings. The framework achieves decentralized training, prevents data being leaked and meets the requirements of data residency laws by using federated learning and dynamic schema harmonization with container orchestration. Moreover, the proposed FDM technique relies on zero-trust security, confidential computing and Kubernetes-native operations to provide isolation, watching and traceability among the various tenants. On typical benchmark datasets, the approach shown here performs better in terms of privacy, how quickly the model learns and how quickly it may be used in practice compared to centralized training. By using this study, AI service providers can ensure their LLM service is trustworthy and safe for IAP use in healthcare, finance and government.
Nowadays, checkpoints have been proven to be an effective solution to ensure slashability in proof-of-stake (PoS) consensus, and Tas et al.'s cutting-edge solution in S&P 2023 is a typical example. Unfortunately, despite progress, hour-level transaction delays and annually around 10 K dollar checkpoint costs make existing related solutions still unacceptable in realworld PoS applications. In this paper, we propose SecPoS, a slashable PoS consensus with second-level transaction delays and one-time checkpoint costs. To achieve these design goals, we draw inspiration from Pixel+ signatures and chameleon hash functions to design a novel bilateral blockchain structure, achieving twoblock transaction finalization via only uploading the first block of our chain as checkpoints. Next, considering practical application requirements, we address a series of following challenges, such as bilateral immutability, blockchain forks, determination of the main chain, and malicious attacks from PoS members. In detail, we propose two constructions of SecPoS, i.e., SecPoS – A and SecPoS – B. SecPoS – A and SecPoS – B have a tradeoff between transaction delays and block numbers packed in an epoch. Compatible with most existing one-way blockchains, we implement and outsource a prototype SecPoS to facilitate research11https://github.com/Academic-Paper-Codes/SecPoS-Consensus, and prove the security of SecPoS. Experiments on this prototype show that SecPoS – A and SecPoS – B require around 5s and 100s transaction delays, respectively, and both require 2 dollars one-time checkpoint costs.
Sunil P. Chinte, Prof. S. A. Thakare, Aarti R. Jaiswal, Nikunj Hasmukhrai Raja · 5 authors
The rapid expansion of Internet of Things (IoT) ecosystems has resulted in an unprecedented surge in data generation, necessitating reliable, scalable, and secure storage mechanisms. Traditional centralized storage systems suffer from inherent limitations such as single points of failure, limited scalability, and vulnerability to cyberattacks, which compromise the confidentiality and availability of critical IoT data. This study introduces a blockchain- based decentralized storage framework aimed at addressing these critical issues. By leveraging the distributed and immutable characteristics of blockchain technology, the proposed system enhances data integrity, ensures transparency, and facilitates trustless data exchange among heterogeneous IoT devices. The methodology includes mathematical modeling of key performance parameters such as latency, throughput, storage efficiency, and consensus delay. Smart contracts are integrated to automate validation and enforce rules among interconnected devices, while redundancy mechanisms like replication and erasure coding improve storage reliability and efficiency. The framework’s effectiveness is evaluated using simulation tools including Hyperledger Caliper and Ethereum Testnets for blockchain behavior, and NS-3 and OMNeT++ for modeling dynamic IoT network environments. Experimental results reveal a 30% improvement in data retrieval time, 25% gain in storage efficiency, 40% enhancement in system resilience, and a 50% increase in transaction throughput over conventional approaches. These metrics highlight the suitability of the proposed model for real-world applications requiring scalable and secure IoT data management, such as healthcare monitoring, smart cities, and industrial automation. The model’s reproducibility and modularity make it a robust solution for future research and deployment. Overall, this work demonstrates that blockchain-integrated decentralized storage frameworks present a transformative step toward resilient and scalable IoT infrastructures.
Rohan Menon, C.V.S Anirudh, K Pradeep, K. P. Vijayakumar
This paper presents a serverless blockchain transaction model built entirely on Amazon Web Services (AWS), combining AWS Managed Blockchain (Hyperledger Fabric) with AWS Lambda for smart-contract execution. By eliminating traditional server infrastructure, the proposed architecture reduces operational overhead, automatically scales with demand, and optimizes costs through pay-as-you-go billing. Integration with AWS Identity and Access Management (IAM) enforces fine-grained access control, while AWS CloudWatch delivers real-time monitoring and logging. Smart contracts run as stateless Lambda functions triggered by blockchain events, enabling a fully decoupled, event-driven workflow. Infrastructure provisioning is automated via AWS CloudFormation, and AWS Key Management Service (KMS) secures cryptographic keys. Comparative benchmarking shows significant improvements in scalability, fault tolerance, and total cost of ownership versus server-based deployments, making this model ideal for enterprise-grade distributed ledger applications.
Bo-Sian Liao, Jung‐Shian Li, I‐Hsien Liu, Chuan-Kang Liu
Federated Learning (FL) has emerged as an innovative paradigm that enables heterogeneous and geographically distributed clients to collaboratively train models in a decentralized and privacy-preserving manner. However, FL systems face numerous challenges in real-world deployments, particularly passive participation caused by malicious attacks, where clients drop out due to attacks. This issue, though not intentionally designed by the system, significantly impacts training stability. In this study, we propose BAHA-FL (Blockchain-based Adaptive Historical Averaging Federated Learning. Our approach integrates adaptive historical averaging with exponential decay weighting to effectively compensate for missing parameters due to client dropouts. Our blockchainbased solution ensures the immutability and traceability of model update records, leveraging Distributed Ledger Technology (DLT) to maintain model integrity.
Abstract: The rapid proliferation of blockchain technology has intensified concerns over the energy inefficiency of traditional consensus mechanisms, particularly Proof of Work (PoW). This study investigates alternative, scalable consensus mechanisms with a focus on enhancing energy efficiency while maintaining performance and decentralization. The evaluation centers on Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Practical Byzantine Fault Tolerance (PBFT), using empirical simulations and statistical modeling to assess key performance indicators—energy consumption per transaction, transaction latency, and throughput. The results indicate that PoS is the most energy-efficient, consuming only 0.04 kWh per transaction, whereas DPoS offers the highest throughput at 1400 transactions per second with moderate energy requirements. PBFT demonstrates the lowest latency but at the cost of increased energy usage. Predictive regression analysis further reinforces the trade-offs between energy use and scalability across consensus mechanisms. These findings highlight the importance of selecting consensus algorithms based on specific application requirements and environmental considerations. The study provides a data-driven framework for guiding the design and adoption of energy-conscious blockchain infrastructures suitable for sustainable and large-scale deployment. Keywords: Blockchain, Consensus Mechanism, Energy Efficiency, Scalability, Proof of Stake, Delegated Proof of Stake, PBFT, Distributed Systems, Transaction Throughput, Green Blockchain Technologies
Blockchain has been widely recognized as a trusted computing paradigm underpinning Decentralized Applications (DApps). However, low performance and poor scalability of the existing mainstream blockchain designs and implementations render their prospects unattainable, so-called trilemma . Directed Acyclic Graph (DAG) emerged as an alternative blockchain architecture to address the performance and scalability issue. However, the capacity and feasibility of DAG-based blockchain systems still remain of concern, due to a lack of open source implementations and convincing published experimental results. In this article, we propose a DAG-based blockchain, named 3D-DAG , to greatly improve the scalability and performance without compromising security. The system addresses the trilemma of decentralization, scalability, and security. Technically, our 3D-DAG consists of two layers of chains: DAGchain and mainchain . The DAGchain organizes the transactions in DAG topology, without having to solve the forks. They select a small group of validators from a large number of miners to make up the committee for consensus procedures. The design of DAGchain improves scalability by enabling parallel transaction processing. Based on that, the mainchain plays the role of checkpoint, periodically packaging and recording received transactions from DAGchain for finality. We also provide a prototype implementation with detailed evaluations. Experimental results show our 3D-DAG can meet the performance (a peak value of \(9.82\times 10^{4}\) TPS) that is expected by DApps in the context of Web3.
As financial institutions increasingly migrate their core platforms to microservices-based architectures, the challenge of managing distributed transactions has gained critical importance. Banking APIs typically require atomicity and consistency across multiple services—such as account management, fraud detection, notifications, and audit trails all of which operate independently with isolated data stores. In such an ecosystem, ensuring consistency, performance, and fault tolerance becomes a balancing act that traditional and modern transaction patterns attempt to resolve differently. This paper explores and contrasts two dominant approaches to distributed transaction management: the Two-Phase Commit (2PC) protocol and the Saga Pattern, particularly in the context of mission-critical banking applications. 2PC has long been considered the gold standard for ensuring atomicity and strong consistency in distributed systems. However, its blocking nature, reliance on a centralized coordinator, and vulnerability to network partitions make it less suitable for high-throughput, globally distributed systems common in modern fintech platforms. On the other hand, the Saga Pattern, an eventual consistency model that orchestrates a sequence of local transactions with compensating rollback operations—offers better fault tolerance and non-blocking behavior. Yet, its trade-offs include the complexity of compensating logic, lack of strict ACID guarantees, and potential for data anomalies if not carefully implemented. To ground the discussion in real-world reliability needs, I introduce a chaos engineering-based simulation that demonstrates the behavior of both 2PC and Saga under controlled failure scenarios, such as inter-service latency spikes and partial service outages. We benchmark recovery times, resource locking, system availability, and data reconciliation behavior using a representative banking microservice architecture deployed in a containerized environment. My findings reveal that Saga outperforms 2PC in terms of availability and fault recovery, making it suitable for user-facing, latency-sensitive operations. However, 2PC remains superior for operations demanding immediate consistency and compliance with strict audit requirements, such as core ledger updates. Based on this analysis, we propose a hybrid transaction strategy that applies 2PC to core financial operations and Saga to surrounding auxiliary services, striking a balance between performance and correctness. This study offers practical design insights for architects building resilient, scalable, and regulation-compliant financial systems. It also highlights the need for adaptive orchestration platforms capable of dynamically selecting transaction models based on context and SLA requirements.
Web3 technologies have experienced unprecedented growth in the last decade, achieving widespread adoption. As various blockchain networks continue to evolve, we are on the cusp of a paradigm shift in which they could provide services traditionally offered by the Internet, but in a decentralized manner, marking the emergence of the Internet of Blockchains. While significant progress has been achieved in enabling interoperability between blockchain networks, existing solutions often assume that networks are already mutually aware. This reveals a critical gap: the initial discovery of blockchain networks remains largely unaddressed. This paper proposes a decentralized architecture for blockchain network discovery that operates independently of any centralized authority. We also introduce a mechanism for discovering assets and services within a blockchain from external networks. Given the decentralized nature of the proposed discovery architecture, we design an incentive mechanism to encourage nodes to actively participate in maintaining the discovery network. The proposed architecture implemented and evaluated, using the Substrate framework, demonstrates its resilience and scalability, effectively handling up to 130,000 concurrent requests under the tested network configurations, with a median response time of 5.5 milliseconds, demonstrating the ability to scale its processing capacity further by increasing its network size.
The rapid evolution of data management in decentralized environments requires innovative solutions to ensure security, accessibility, and efficiency. This paper presents a scientific approach for secure data management in swarmedge computing. The approach leverages DAO, smart contracts, and IPFS to enhance data availability, security, and privacy. It addresses key challenges in integrating off-chain data with blockchain ecosystems. The exploitability of the oracles for data tokenization, smart contracts for metadata mapping and data files based on metadata, and access control are significant contributions from the scientific approach. The proposed approach presents an advanced decentralized, smart contract-driven data management system, integrating non-fungible tokens (NFTs) for data ownership and access rights.
Bitcoin mining is highly energy-intensive, and improving its efficiency is critical for both economic and environmental sustainability. This project presents a web-based simulation tool that models key aspects of Bitcoin mining, including the SHA-256 hashing algorithm, nonce iteration, and target difficulty checks. The computational backend is integrated with real-time power and thermal models, enabling the simulator to reflect how hash rate influences energy consumption and temperature. Interactive controls for frequency, voltage, and resistance, along with graphical visualizations of power usage over time, allow users to explore trade-offs between energy efficiency and mining performance. The simulation also includes a financial trade-off analysis feature and supports extended runtime testing to evaluate long-term behavior under varying operational conditions.