Denis Wapukha Walumbe, Gabriel Ndugu Kamau, Jane Wanjiru Njuki
Proof of Stake (PoS) models are energy-efficient and require limited computational power. These features are critical in telemedicine environments, where resource-constrained devices must handle sensitive data securely. The growing need for auditable and privacy-preserving data storage in telemedicine underscores the importance of PoS models optimized for lightweight devices while complying with strict regulatory requirements, such as the Health Insurance Portability and Accountability Act (HIPAA).This study was guided by two research questions: (i) Which PoS models are lightweight and suitable for telemedicine? and (ii) What features make lightweight PoS models effective for privacy and efficiency in telemedicine? To address these questions, a systematic literature review (SLR) guided by the PICOC framework was conducted to investigate lightweight PoS models that can enhance privacy in telemedicine systems. Out of 2,394 papers studies screened, 55 were included in the analysis. The findings identified Algorand, Ouroboros Praos, Tendermint, Nxt, and Casper CBC as promising candidates. Key enabling features included lightweight voting mechanisms, such as Byzantine Agreement protocols and Verifiable Random Functions, as well as cryptographic techniques like symmetric encryption and multiparty computation. Performance metrics evaluated included latency, throughput, energy efficiency, and battery consumption, with Grey Relational Analysis ranking Algorand highest due to its low latency, high throughput, and minimal energy consumption.
Edgar Roberto Dulce Villarreal, Julio Ariel Hurtado, Jose Garcia-Alonso, Enrique Moguel · 5 authors
Achieving a balance between interoperability and security in blockchain systems has led to the development of various integration mechanisms. These include the use of specialized smart contracts to facilitate cross-chain interactions, enabling reliable connections and secure transfers of information and assets. While technical interoperability is effectively addressed at lower layers, achieving semantic interoperability at the application layer remains a significant challenge. This paper proposes a mechanism to address this challenge by leveraging a model-driven approach. Metamodels, models, and transformations are created, and smart contracts are defined abstractly and then semi-automatically generated for specific blockchain platforms. The proposed approach was validated by generating contracts between Ethereum and Hyperledger, enabling semantically compatible transactions. The mechanism was further evaluated using the Technology Acceptance Model with expert participants. This evaluation demonstrated its effectiveness in specifying and transforming contracts and fostering semantic interoperability across blockchains.
Vitor Emanuel Batista, Guilherme Koslovski, Maurício A. Pillon, Charles C. Miers · 6 authors
Cloud computing has become the dominant choice for hosting various systems and services, with substantial public cloud service spending growth. This trend has extended to blockchain technology, which offers decentralized solutions for diverse applications. Concurrently, there is an increasing focus on business models incorporating Environmental, Social and Governance (ESG) aspects. One such initiative is Carbono21, a platform generating tokens in response to reforestation actions and the carbon credit market. In this context, this article examines the performance aspects of generating Non-Fungible Tokens (NFTs) in a blockchain environment under stress conditions. The Hyperledger Caliper was the benchmark tool used in experiments conducted to analyze the blockchain’s resilience and stability. Linear regression models showed a strong positive correlation between memory usage and total transactions. These results highlight the need for precise resource sizing and robust monitoring mechanisms to prevent service degradation under high transaction loads.
Harsha Kumar A, Preetham Venkatram C, N. Saran, David Daniel · 5 authors
Traditional Electronic Health Record (EHR) systems suffer from critical vulnerabilities in security, interoperability, and patient data control. This paper introduces PolyMed, a novel decentralized platform designed to address these challenges. PolyMed combines blockchain, Artificial Intelligence (AI), and edge computing into a synergistic architecture. It uses the Polygon blockchain for immutable record-keeping and a Decentralized Autonomous Organization (DAO) for transparent governance. Patient identity is secured through privacy-preserving zero-knowledge proofs (ZKPs) and anchored to non-transferable Soulbound Tokens (SBTs), granting users true sovereignty over their data. The platform also includes a Decentralized Finance (DeFi) module to improve healthcare accessibility. Empirical evaluations on the Polygon Mainnet confirm the system's viability, showing sub-4-second transaction latencies and over 90% cost savings compared to legacy systems. The integrated AI model, leveraging a LightGBM classifier on a rich set of engineered features, achieves an Area Under the Curve (AUC) of 0.8543 and an accuracy of 80.33% in emergency detection, demonstrating high reliability on a clinically relevant and imbalanced dataset. By aligning with global standards like General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), PolyMed offers an integrated platform for patient-centric digital health management.
The use of distributed ledger technologies (DLTs) and blockchains is rapidly expanding across multiple sectors, including finance and governance. Although numerous blockchain frameworks and networks exist to support different use cases, a major challenge remains: enabling seamless communication between these diverse frameworks, protocols, and ledgers. As blockchain adoption accelerates, the need for effective interoperability solutions is becoming increasingly critical to deliver greater value to users. This study explores the design of current blockchain bridges and evaluates common security threats and mitigation strategies within the realm of interoperability. A threat model is introduced to examine the key components, vulnerabilities, risks, and corresponding safeguards involved in blockchain interoperability. Security concerns—such as excessive trust centralization and flaws in smart contracts—are identified and categorized based on the type of bridge component, along with recommended countermeasures. Different interoperability approaches—including relays, Hash Time-Locked Contracts (HTLCs), notary schemes, and smart contract-based solutions—are analyzed in detail. Ultimately, this research aims to help developers better understand the security challenges in blockchain interoperability and highlights the importance of establishing standardized practices in this area.
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the significant computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We show the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.
Rabia Arshad, Muhammad Milhan Afzal Khan, Saman Rasheed, Irtaza Ijaz · 5 authors
Blockchain technology has transformed decentralized data exchange and digital payments but the consistently high gas prices pose a significant challenge to its scalability and efficiency. This research explores the role of AI-driven gas price prediction and data compression methods on gas utilization in blockchain systems with special emphasis on Ethereum transactions. Using actual Ethereum transaction history, we compare the performance of compressed versus uncompressed payloads with three different compression algorithms: Zlib, Brotli, and Gzip. Beyond that, a linear regression model is also trained to forecast hourly gas Price fluctuations given past transaction history. The methodology includes thorough statistical analysis to provide accurate and reproducible results. Our results show that compressing text data over 141 bytes using the Zlib algorithm prior to making transactions on the Ethereum network decreases the amount of gas Used without altering system time. This validates the efficiency of combining data compression with gas price forecasting in minimizing transaction costs without affecting performance. Moreover, our study further encompasses investigation of actual gas Price trends and provides real-world insights for optimizing timing strategies for economic transaction execution. These results enhance the knowledge of Ethereum gas dynamics and provide valuable solutions for enhancing economic efficiency and resource utilization in applications based on blockchain. Future efforts will involve applying the framework to the Ethereum mainnet, using deep learning models for increased prediction accuracy, and adaptive compression dependent on network state and transaction size.
Aiming at the problem that smart contract security vulnerability detection faces high false positives in static analysis and low efficiency in dynamic analysis, which leads to low accuracy of vulnerability detection, a smart contract vulnerability detection method based on improved graph neural network (EGN, Event-Enhanced GNN) was proposed. Firstly, security mode features and graph features were extracted. Security mode features reduced false positives caused by blind detection, and graph features avoided the performance bottleneck of full graph traversal. Secondly, the high-risk functions were screened based on the risk probability threshold to improve the overall analysis efficiency. Thirdly, the temporal graph neural network was deployed for high-risk functions, the event temporal graph was dynamically tracked and the self-attention mechanism was used to capture vulnerabilities, so as to enhance the detection ability of complex vulnerabilities. Finally, we focus on reentrant vulnerability and timestamp dependency vulnerability detection. Through the evaluation experiments on the real contract datasets of two platforms of Ethereum and VNT chain, the experimental results show that the accuracy and F1 value of the proposed model for detecting reentries vulnerability reach 93.12% and 94.29% respectively, and the accuracy and F1 value of timestamp dependency vulnerability reach 91.71% and 91.42% respectively, which are better than the existing methods.
This study presents a comprehensive framework that integrates deep learning and blockchain security to address key challenges in cryptocurrency forecasting and privacy preservation. A state-of-the-art ensemble machine learning model, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, is proposed for Bitcoin price prediction. The model achieves 92.1% accuracy on out-of-sample data following rigorous validation, demonstrating strong forecasting performance. To address fundamental security and privacy concerns in blockchain systems, a dynamic privacy framework is proposed, which integrates Zero-Knowledge Proofs (ZKPs) and adaptable consensus methods to improve transaction confidentiality, scalability, and adherence to regulations.
Traditional information system auditing faces severe challenges in data integrity verification and audit transparency. Manual sampling methods are not only inefficient but also vulnerable to data tampering attacks. This paper proposes a blockchain-based auditing framework that integrates SHA-256 hash chains, ECDSA digital signatures, and zero-knowledge proofs to establish a cryptographically secure and tamper-proof auditing environment. The framework employs a three-layer architecture: the data integrity layer uses hash chains to ensure audit records are immutable; the authentication layer uses digital signatures to verify the non-repudiation of evidence; and the privacy layer implements zero-knowledge proofs to protect sensitive data. To verify the framework’s effectiveness, a comprehensive experiment was conducted on a private Ethereum network with five verification nodes, processing 10,000 to 100,000 audit records. The experimental results show a significant performance improvement. Efficiency is improved by $65 \%$, data processing throughput reaches 500 records/second with a response latency of less than 2 seconds, and the average time for hash calculation and digital signature verification is 0.8 milliseconds and 1.2 milliseconds, respectively. Data integrity verification efficiency is improved by $78 \%$ compared with traditional methods, and reliability reaches $99.99 \%$. Comparative experiments show that compared with traditional database-centric auditing systems, the proposed system improves processing throughput by $178 \%$, and reduces manual reconciliation time from 2.5 hours to near real-time. This solution provides a practical, efficient and scalable method for auditing next-generation information systems in enterprise environments.
Yasir Abdelgadir Mohamed, Mohamed Bashir, Akbar Khanan
The exponential growth of Internet of Things (IoT) devices and edge computing nodes has amplified security and sustainability challenges in decentralized environments. Traditional blockchain models, while providing integrity and immutability, are computationally intensive and energy-hungry, making them unsuitable for constrained IoT-edge ecosystems. This paper proposes an energy-efficient blockchain security framework designed to ensure trust, data confidentiality, and low-carbon operation in sustainable IoT deployments. The framework introduces a lightweight consensus algorithm named Proof of Trust and Energy Balance (PoTEB), which integrates device reputation scoring with energy-aware block validation. To further enhance resilience, a hybrid on-chain/off-chain encryption model ensures secure data transmission and adaptive key rotation. Experimental results obtained from Raspberry Pi-based edge nodes demonstrate a 42% reduction in energy consumption compared to Proof-of-Work (PoW) and a 25% latency improvement over Proof-of-Stake (PoS), while maintaining comparable throughput and attack resistance. The proposed framework aligns with Sustainable Development Goals (SDG 9 & 13), fostering responsible innovation and carbon-efficient digital infrastructures.
Smart contract vulnerability detection is an important task in securing the blockchain. However, existing detection methods primarily extract single view features, such as semantic or structural features, which ignores the synergistic supplementation of them to smart contract, remaining room for improvement in feature representation. To this end, this paper proposes the LLM-assisted dual-view awareness framework for smart contract vulnerability detection, which incorporates significantly different semantic features and structural features. To address the limitation of large language model (LLM) in domain-specific expertise, we design semantic awareness module based on Retrieval-Augmented Generation (RAG), construct vulnerability knowledge base, and perform semantic reasoning on smart contracts. To capture crucial structural information, we propose structural awareness module based on Graph Neural Network (GNN), construct contract graphs, and perform structural analysis on smart contracts. We evaluated four types of vulnerabilities, and the experimental results show that our approach significantly outperforms state-of-the-art approaches, achieving 4.80% improvement in accuracy for timestamp dependence detection.
Eranga Bandara, Sachin Shetty, Ravi Mukkamala, Ross Gore · 12 authors
In recent years, blockchain has experienced widespread adoption across various industries, becoming integral to numerous enterprise applications. Concurrently, the rise of generative AI and LLMs has transformed human-computer interactions, offering advanced capabilities in understanding and generating human-like text. The introduction of the MCP has further enhanced AI integration by standardizing communication between AI systems and external data sources. Despite these advancements, there is still no standardized method for seamlessly integrating LLM applications and blockchain. To address this concern, we propose "MCC: Model Context Contracts" a novel framework that enables LLMs to interact directly with blockchain smart contracts through MCP-like protocol. This integration allows AI agents to invoke blockchain smart contracts, facilitating more dynamic and context-aware interactions between users and blockchain networks. Essentially, it empowers users to interact with blockchain systems and perform transactions using queries in natural language. Within this proposed architecture, blockchain smart contracts can function as intelligent agents capable of recognizing user input in natural language and executing the corresponding transactions. To ensure that the LLM accurately interprets natural language inputs and maps them to the appropriate MCP functions, the LLM was fine-tuned using a custom dataset comprising user inputs paired with their corresponding MCP server functions. This fine-tuning process significantly improved the platform's performance and accuracy. To validate the effectiveness of MCC, we have developed an end-to-end prototype implemented on the Rahasak blockchain with the fine-tuned Llama-4 LLM. To the best of our knowledge, this research represents the first approach to using the concept of Model Context Protocol to integrate LLMs with blockchain.
Fernando Richter Vidal, Naghmeh Ivaki, Nuno Laranjeiro
The performance assessment of blockchain applications holds significant challenges due to their decentralized architecture, immutable smart contracts, distributed ledgers, and operational costs such as gas fees. Existing blockchain benchmarks often either fail to fully capture blockchain-specific behaviors or offer limited configurability and metric reporting. In this paper, we present a new and comprehensive benchmark designed explicitly for blockchain applications, named bBench. Building on established principles from traditional benchmarking and by specializing them in the blockchain context and supported by customized blockchain tools (i.e., Hyperledger Caliper, web3.eth, and node-os-utils), bBench characterizes blockchain application performance in four dimensions: network performance, resource utilization, storage usage, and operational cost. We demonstrate the effectiveness of our benchmark through a case study involving 12 smart contract applications with varying performance demands, some of which hold known vulnerabilities. The results show the benchmark’s ability to quantify performance deviations across different applications, as well as those caused by the activation of specific vulnerabilities.
This paper investigates performance bottlenecks of consortium blockchains under high-throughput and low-latency requirements, focusing on excessive storage burden on full nodes and redundant computation in transaction validation. Based on consortium blockchain, a novel architecture named Server-Side Core Chain (SSC) is proposed. In this architecture, the core functions of blockchain ledger data storage and smart contract execution are delegated from decentralized consensus nodes to a server cluster jointly managed and trusted by consortium members. The consensus node layer is restructured into a lightweight ``Consensus and Audit Network,” dedicated to transaction ordering and state commitment verification. This paper elaborates on the design principles, operational workflow, and security model of the SSC architecture. Theoretical analysis and prototype experiments demonstrate that the architecture significantly enhances the transaction processing capacity of consortium blockchains (experimental results show a throughput improvement of more than 18 times), greatly reduces the entry barriers and operational costs for member nodes (storage overhead reduced by over 99%), and ensures the verifiability of off-chain computations and data privacy through cryptographic commitments and zero-knowledge proofs [1]. The SSC architecture offers a new solution for deploying consortium blockchains in large-scale applications, including finance, supply chain management, and e-government.
Lê Thái Hùng, Huu-Han Nguyen, Thai Hung Van, Doan Minh Trung · 5 authors
Smart contract vulnerabilities pose serious risks in blockchain ecosystems, yet existing detection methods often rely on either source code or opcode analysis in isolation, missing complementary information across modalities. This paper presents a multimodal learning framework that combines semantic features extracted from source code using CodeBERT with Structure-Based Traversal (SBT) encoding and behavioral patterns derived from opcode sequences using a gMLP(gated Multi-Layer Perceptron) model applied to TF-IDF vectors. The framework systematically evaluates various fusion strategies, including concatenation, self-attention, cross-attention, and a hybrid attention mechanism, all within a unified architecture and dataset. Extensive experiments on the SmartBugs benchmark demonstrate two key findings: (1) the pairing of CodeBERT(SBT) and gMLP(opcode) achieves superior modality synergy (F1-score: 0.84), and (2) our hybrid attention fusion mechanism further improves performance to 0.87 F1, outperforming other fusion strategies by up to 3.6%. Compared to the best unimodal baselines, our approach yields a 12.8% F1 gain. To the best of our knowledge, this is the first study to provide a systematic benchmark of these fusion strategies under a unified framework for smart contract vulnerability detection. These results underscore the importance of informed modality selection and intelligent fusion design in building robust AI-driven vulnerability detection tools.
Tuan-Dung Tran, Huynh Phan Gia Bao, Tra Minh Trong, Nguyen Tan Cam · 5 authors
Integrating decentralized identity (DID) systems with state authorities introduces complex challenges related to trust, privacy, and auditability. The Lotus Bridge framework addresses these by proposing a hybrid digital identity architecture suitable for national-scale deployment. It integrates a permissioned Proof-of-Authority (PoA) blockchain for sovereign credential issuance with a cross-chain verification bridge that utilizes zero-knowledge proofs to enable privacy-preserving selective disclosure. This architecture is one of the first to combine state-backed issuance with interoperable, private cross-chain verification in a unified system. Two core protocols—state-anchored issuance and cross-chain verification—are formally defined and implemented in a working prototype. Experimental results demonstrate strong performance: cryptographic proofs remain under 600 bytes and end-to-end verification latency consistently stays below 2 seconds, enabling real-time applicability. Additionally, parallelization reduces proof generation time by over 90%, and the system achieves significant cost efficiency, with on-chain verification starting at 244 Gwei per credential and scaling to 14K Gwei for 1,000, offering up to 75% cost savings compared to existing Ethereum and Polygon solutions. These findings establish Lotus Bridge as a scalable and costeffective foundation for sovereign digital identity in cross-chain ecosystems.
AbstractAs a key bridge between the real economy and financial capital, supply chain finance generates core data such as transaction documents, logistics information, and financing contracts, whose secure, trustworthy, and controllable management is crucial.Traditional centralized notarization schemes suffer from single points of failure, risks of data tampering, and high trust costs.Although blockchain offers tamper-evident notarization, limited on-chain storage and throughput constrain its direct use in large-scale data scenarios.To address this tension, this paper investigates a secure data notarization and access control algorithm grounded in an on-chain/off-chain hybrid storage architecture and smart contracts.We first construct a layered data management model: high-value, low-volume data hashes (digital fingerprints) and key access-control policies are anchored on-chain to ensure immutability, while complete large-volume raw data are encrypted and stored off-chain (e.g., in IPFS or distributed databases) to ensure scalability.To tackle potential challenges of on-/off-chain consistency and integrity verification under this hybrid architecture, we design an efficient verification mechanism based on cryptographic commitments, ensuring any tampering with off-chain data can be detected quickly and succinctly.Furthermore, to achieve fine-grained privacy protection and compliant use, we propose a smart-contract-based dynamic access control algorithm.By deploying access-control policies as executable code on-chain, the algorithm performs automated logical checks to deliver precise authorization and comprehensive audit logging, ensuring security and transparency throughout data sharing and circulation.Through theoretical security analysis and prototype experiments, the proposed scheme preserves data immutability and traceability while significantly improving the storage efficiency and processing performance of supply chain finance notarization systems, and it enables flexible and secure access control.The results indicate that the coordinated mechanism of on-chain/off-chain hybrid storage and smart contracts offers a feasible technical pathway for building efficient, trustworthy, and secure supply chain finance infrastructure.
Venkata Sivakumar Musam, Nagendra Kumar Musham, C Siva, S. Karimulla Basha · 6 authors
The field of healthcare monitoring has been revolutionized by a combination of cloud computing and IoT-enabled sensor networks that enable real-time data collection, storage and processing. However, because healthcare data is sensitive, strong security and privacy-preserving measures are required to stop unwanted access and preserve data integrity in multi-authority settings. To propose a collaborative and privacy-preserving sensor cloud architecture for safe, scalable, and fine-grained access control for healthcare monitoring systems by utilizing Zero-Knowledge Proofs (ZKP) and Multi-Authority Attribute-Based Encryption (MA-ABE). The proposed method combines ZKP for authentication and MA-ABE for attribute-based encryption to protect sensitive data. It uses gateways to securely aggregate and transmit data to the cloud, and elliptic curve cryptography to optimize performance. The framework decreased computational overhead by 40% while achieving notable gains in encryption (120 ms) and decryption (150 ms). Outperforming current approaches in healthcare data security and access management, classification accuracy reached 96.5% with improved privacy preservation (96%) and scalability (94%). The suggested system addresses privacy, scalability, and computational efficiency while incorporating cutting-edge cryptographic approaches to provide secure healthcare monitoring. Large-scale, real-time healthcare applications can benefit greatly from its strong solution.
Jongho Seol, J. Deuja, Indy Park, Cong Pu · 5 authors
This paper presents a new quantitative model to study the interplay across CIA (Confidentiality, Integrity, Availability) Triad and performance in blockchain-based crypto space with specific reference to Ethereum or Ethereum-equivalent chains. The model introduces and incorporates three new random variables on top of the baseline chain model [16], C: the likelihood to secure confidentiality; $\boldsymbol{I}$: to secure integrity; and $\boldsymbol{A}$: to secure availability. Thus, the model will orchestrate an extensive set of key random variables such as λ: transaction slot arrival rate; μ: block posting rate; $\boldsymbol{i}$: number of transaction slots pending on the current block along with $\boldsymbol{C}, \boldsymbol{I}$ and $\boldsymbol{A}$. The underlying mathematical method employed is an embedded Markovian queueing model as the model traces the stochastic flow of the transactions as well as the Markovian flow of them with respect to $\boldsymbol{C}, \boldsymbol{I}$ and $\boldsymbol{A}$. The state in the model is defined by $P_{i_{C / I / A}}$, i.e., the likelihood to have i number of transaction slots pending on the current block and the stochastic CIA status of the crypto space thus far is in C or $\bar{C}, I$ or $\overline{\boldsymbol{I}}$, and $\boldsymbol{A}$ or $\overline{\boldsymbol{A}}$. The solutions to the model will be provided to assess a few basic performance metrics such as W: the average transaction waiting time, $L:$ the average block capacity required; and G: the throughput of transactions per block. And further and primarily, a unique and extensive simulation and analysis will be conducted to evaluate the impact of base random variables such as i, λ, μ, and various combinations of $\mathcal{C}, I$ and A on the overall $P_{i_{C / I / A}}$ in steady state. The results of the simulation reveal tradeoffs between the CIA Triad and performance that is uniquely identifiable by the proposed model.
Sandeep Kalari, Ravi Mukkamala, Vikas Ashok, Stephan Olariu · 6 authors
Large Language Models (LLMs) are increasingly being adopted in sensitive domains such as healthcare and finance. However, persistent challenges such as unreliable data sources, privacy breaches, and hallucinated output continue to hinder their usage. To address these shortcomings, we propose BlockQwen, a blockchain-augmented framework that integrates decentralized trust validation, role-specific access control, and verifiable audit trails into Qwen 2.5 LLM workflow. Here, blockchain not only anchors the authenticity of retrieved documents, but also enforces dynamic, tamper-proof access and authorization policies and preserves transparent, immutable records of model interactions. This decentralized infrastructure ensures that the LLM operates on verified inputs while maintaining privacy and compliance with regulatory standards. A layered security module, combined with reinforcement learning, is further tailored to detect privacy risks and to mitigate hallucinations in real time. A prototype implementation, with simulated healthcare data, achieved an $86.25 \%$ privacy preservation rate and an $88.33 \%$ hallucination mitigation rate, significantly outperforming conventional LLM deployments. These results demonstrate the potential of combining blockchain functionality with LLM, resulting in robust, secure, transparent, and trustworthy AI systems.
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
This paper presents a quantitative model to estimate the sustainability of blockchain networks. Blockchains with three different consensus protocols such as Proof of Work (PoW), Proof of Stake (PoS) and Proof of Hybrid consensus protocols across PoW and PoS(PoH) [12], are considered in this research. Sustainability issues in blockchain network systems due to their energy-depleting mining processes and transactions are hindering the technology from further advancement. The sustainability of concern in blockchain networks is assumed, in this paper, to be an inverse of their energy consumption without loss of generality and intuitiveness. The novelty of the proposed model is that the sustainability is estimated quantitatively in a specific context of blockchain network architectures and stochastic behaviors of the transactions. An extensive variety of variables are employed to build a model that is blockchain (e.g., $\mathrm{PoW}, \mathrm{PoS}$ and PoH)-specific, as three representative benchmark architectures with respect to consensus protocols, and each of those will be quantitatively expressed via the baseline chain model [7], the PoS chain model [9] and the PoH chain model [12], respectively. Extensive numerical simulations will be conducted to demonstrate various design considerations to be taken in specific regard of sustainability versus a few primary design variables as employed in the sustainability model. The proposed sustainability model is expected to establish a sound and quantitative foundation for the design of sustainable blockchain networks.
Distributed-ledger technologies (DLTs) have upended the design logic of, data-sharing web architectures, especially within sectors that demand uncompromising transparency, indelible audit trails, and decentralised governance. Yet curating an optimal DLT stack remains an intricate optimisation puzzle involving nuanced trade-offs across cryptographic rigour, elastic scalability, experiential ergonomics, propagation latency, cross-ledger interoperability, and fiscal prudence. To navigate this complexity, we introduce a tiered decision-support framework that welds expert-elicited priorities to empirical performance signals within a rigorous multi-criteria outranking model. The scheme yields transparent, rank-ordered shortlists of candidate ledgers and is demonstrated across healthcare, fintech, and supply-chain provenance scenarios. Results confirm the model’s ability to surface context-specific “best fits” even when decision objectives clash, thereby equipping engineers, CIOs, and policy designers with a defensible roadmap for trustworthy, efficient, and governance-aligned blockchain adoption. Future iterations will embed fuzzy logic and live-telemetry feedback to sharpen responsiveness in rapidly evolving operating environments.
Yazid Maafa, Alexandre Chalal, Jiahui Xiang, Osman Salem · 5 authors
Blockchains have revolutionized information systems, evolving continuously in both performance and application sophistication. This paper compares public blockchain performances by examining their technical foundations and practical applications across sectors. Through benchmark analysis of key criteria, we develop hypotheses explaining performance variations. Our goal is to provide insight into this maturing ecosystem whose impact now extends well beyond cryptocurrencies into numerous innovation domains.