Addressing educational inequity in Sub-Saharan Africa, this research presents an autonomous agent-orchestrated framework for decentralized, culturally adaptive educational content generation on edge devices. The system leverages four specialized agents that work together to generate contextually appropriate educational content. Experimental validation on platforms including Raspberry Pi 4B and NVIDIA Jetson Nano demonstrates significant performance achievements. InkubaLM on Jetson Nano achieved a Time-To-First-Token (TTFT) of 129 ms, an average inter-token latency of 33 ms, and a throughput of 45.2 tokens per second while consuming 8.4 W. On Raspberry Pi 4B, InkubaLM also led with 326 ms TTFT and 15.9 tokens per second at 5.8 W power consumption. The framework consistently delivered high multilingual quality, averaging a BLEU score of 0.688, cultural relevance of 4.4/5, and fluency of 4.2/5 across tested African languages. Through potential partnerships with active community organizations including African Youth & Community Organization (AYCO) and Florida Africa Foundation, this research aims to establish a practical foundation for accessible, localized, and sustainable AI-driven education in resource-constrained environments. Keeping focus on long-term viability and cultural appropriateness, it contributes to United Nations SDGs 4, 9, and 10. Index Terms - Multi-Agent Systems, Edge AI Computing, Educational Technology, African Languages, Rural Education, Sustainable Development, UN SDG.
Chong Chen, Jiachi Chen, Lingfeng Bao, David F. Lo · 10 authors
Smart contract vulnerabilities, particularly improper Access Control that allows unauthorized execution of restricted functions, have caused billions of dollars in losses. GitHub hosts numerous smart contract repositories containing source code, documentation, and configuration files-these serve as intermediate development artifacts that must be compiled and packaged before deployment. Third-party developers often reference, reuse, or fork code from these repositories during custom development. However, if the referenced code contains vulnerabilities, it can introduce significant security risks. Existing tools for detecting smart contract vulnerabilities are limited in their ability to handle complex repositories, as they typically require the target contract to be compilable to generate an abstract representation for further analysis. This paper presents TRACE, a tool designed to secure non-compilable smart contract repositories against access control vulnerabilities. TRACE employs LLMs to locate sensitive functions involving critical operations (e.g., transfer) within the contract and subsequently completes function snippets into a fully compilable contract. TRACE constructs a function call graph from the abstract syntax tree (AST) of the completed contract. It uses the control flow graph (CFG) of each function as node information. The nodes of the sensitive functions are then analyzed to detect Access Control vulnerabilities. Experimental results demonstrate that TRACE outperforms state-of-the-art tools on an open-sourced CVE dataset, detecting 14 out of 15 CVEs. In addition, it achieves 89.2% precision on 5,000 recent on-chain contracts, far exceeding the best existing tool at 76.9%. On 83 real-world repositories, TRACE achieves 87.0% precision, significantly surpassing DeepSeek-R1's 14.3%.
Digital transactions currently exceed trillions of dollars annually, yet traditional paper-based agreements remain a bottleneck for automation, enforceability, and dispute resolution. Natural language contracts introduce ambiguity, require manual processing, and lack computational verifiability, all of which hinder efficient digital commerce. Computable legal contracts, expressed in machine-readable formats, offer a potential solution by enabling automated execution and verification. Blockchain-based smart contracts further strengthen enforceability and accelerate dispute resolution; however, current implementations risk exposing sensitive agreement terms on public ledgers, raising serious privacy and competitive intelligence concerns that limit enterprise adoption. We introduce zk-agreements, a protocol designed to transition from paper-based trust to cryptographic trust while preserving confidentiality. Our design combines zero-knowledge proofs to protect private agreement terms, secure two-party computation to enable private compliance evaluation, and smart contracts to guarantee automated enforcement. Together, these components achieve both privacy preservation and computational enforceability, resolving the fundamental tension between transparency and confidentiality in blockchain-based agreements.
Shikah J. Alsunaidi, Hamoud Aljamaan, Mohammad Hammoudeh
Smart Contracts (SCs), self-executing programs on blockchain platforms, are transforming industries such as banking, healthcare, and supply chains through automated, trustless transactions. However, their inherent vulnerabilities have led to severe financial and operational losses, with large-scale exploits causing substantial economic damage. Machine Learning (ML) has emerged as a promising approach for SC vulnerability detection, yet its effectiveness, adaptability, and generalizability remain insufficiently explored. This article comprehensively classifies current Ethereum SC vulnerabilities and attacks. It also surveys 108 ML-based detection methods, covering both traditional models and a structured taxonomy of advanced approaches such as GNN-based, LLM-based, contrastive learning, ensemble, hybrid, meta-learning, and transfer learning techniques. The strengths, limitations, and practical challenges of these methods are systematically analyzed, with particular attention to factors such as detection stages, classification problems, dataset characteristics, feature engineering, performance evaluation, generalizability, detection capability, model aging, and ethical and privacy implications. Additionally, existing datasets on SC vulnerabilities are reviewed and consolidated. By integrating these insights, this work provides actionable guidelines and a foundation for building secure, resilient, and trustworthy SC ecosystems.
This paper addresses the challenge of designing secure and private digital credentialing systems by leveraging advanced mathematical primitives from applied cryptography. The core of our proposed solution is the application of Zero-Knowledge Proofs (ZKPs), a class of cryptographic protocols that allows for the verification of assertions without disclosing the underlying secret data. We introduce a formal, layered architecture that demonstrates how the mathematical properties of ZKPs can be systematically translated into a robust, large-scale information system. The framework's design is validated against the complex requirements of the academic domain, which serves as a rigorous testbed for our architectural approach. The primary contribution is a blueprint for integrating complex cryptographic protocols into practical system design, demonstrating how mathematical guarantees of privacy can be preserved in a distributed and verifiable manner. This work provides a novel contribution at the intersection of applied cryptography, system architecture, and information security.
This study explores the design and implementation of a blockchain-based system to enhance trust, transparency, and security in academic credentialing. Motivated by the growing distrust in centralized institutions and the inefficiencies of traditional credential verification processes, the research leverages the immutability, decentralization, and transparency of blockchain to develop a tamper-proof mechanism for academic record storage and validation. Using the Ethereum Sepolia test network and real-world student performance data from the Open University Learning Analytics Dataset (OULAD), the system securely issues, verifies, and revokes academic credentials through a custom smart contract developed in Solidity. Each credential is hashed using SHA-256 to ensure student privacy while enabling public, real-time verification. The implementation was conducted in a Google Colab environment using Web3.py and Infura, with batch processing mechanisms and a Web3 interface for seamless interaction. Empirical results reveal performance patterns across modules and highlight opportunities for academic intervention. The system not only demonstrates operational feasibility but also offers a scalable, interoperable, and ethical framework for higher education institutions to combat credential fraud and enhance institutional accountability. Future work will focus on privacy-enhancing cryptographic integrations and decentralized identity standards to further solidify blockchain’s role in education.
Covariance matrices estimated from short, noisy, and non-Gaussian financial time series are notoriously unstable. Empirical evidence suggests that such covariance structures often exhibit power-law scaling, reflecting complex, hierarchical interactions among assets. Motivated by this observation, we introduce a power-law covariance model to characterize collective market dynamics and propose a hybrid estimator that integrates Random Matrix Theory (RMT) with deep Residual Neural Networks (ResNets). The RMT component regularizes the eigenvalue spectrum in high-dimensional noisy settings, while the ResNet learns data-driven corrections that recover latent structural dependencies encoded in the eigenvectors. Monte Carlo simulations show that the proposed ResNet-based estimators consistently minimize both Frobenius and minimum-variance losses across a range of population covariance models. Empirical experiments on 89 cryptocurrencies over the period 2020-2025, using a training window ending at the local Bitcoin peak in November 2021 and testing through the subsequent bear market, demonstrate that a two-step estimator combining hierarchical filtering with ResNet corrections produces the most profitable and well-balanced portfolios, remaining robust across market regime shifts. Beyond finance, the proposed hybrid framework applies broadly to high-dimensional systems described by low-rank deformations of Wishart ensembles, where incorporating eigenvector information enables the detection of multiscale and hierarchical structure that is inaccessible to purely eigenvalue-based methods.
Michael J. Wooldridge, Attila Bagoly, Jonathan J. Ward, Emanuele La Malfa · 5 authors
Recent surges in LLM-driven intelligent systems largely overlook decades of foundational multi-agent systems (MAS) research, resulting in frameworks with critical limitations such as centralization and inadequate trust and communication protocols. This paper introduces the Fetch.ai architecture, an industrial-strength platform designed to bridge this gap by facilitating the integration of classical MAS principles with modern AI capabilities. We present a novel, multi-layered solution built on a decentralized foundation of on-chain blockchain services for verifiable identity, discovery, and transactions. This is complemented by a comprehensive development framework for creating secure, interoperable agents, a cloud-based platform for deployment, and an intelligent orchestration layer where an agent-native LLM translates high-level human goals into complex, multi-agent workflows. We demonstrate the deployed nature of this system through a decentralized logistics use case where autonomous agents dynamically discover, negotiate, and transact with one another securely. Ultimately, the Fetch.ai stack provides a principled architecture for moving beyond current agent implementations towards open, collaborative, and economically sustainable multi-agent ecosystems.
Phishing attacks in Web3 ecosystems are increasingly sophisticated, exploiting deceptive contract logic, malicious frontend scripts, and token approval patterns. We present DeepTx, a real-time transaction analysis system that detects such threats before user confirmation. DeepTx simulates pending transactions, extracts behavior, context, and UI features, and uses multiple large language models (LLMs) to reason about transaction intent. A consensus mechanism with self-reflection ensures robust and explainable decisions. Evaluated on our phishing dataset, DeepTx achieves high precision and recall (demo video: https://youtu.be/4OfK9KCEXUM).
A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchain’s permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.
Mahran Morsidi, Sharul Tajuddin, S. H. Shah Newaz, Ravi Kumar Patchmuthu · 5 authors
Blockchain technology, originally designed as a secure and immutable ledger, has expanded its applications across various domains. However, its scalability remains a fundamental bottleneck, limiting throughput, specifically Transactions Per Second (TPS) and increasing confirmation latency. Among the many proposed solutions, sharding has emerged as a promising Layer 1 approach by partitioning blockchain networks into smaller, parallelized components, significantly enhancing processing efficiency while maintaining decentralization and security. In this paper, we have conducted a systematic literature review, resulting in a comprehensive review of sharding. We provide a detailed comparative analysis of various sharding approaches and emerging AI-assisted sharding approaches, assessing their effectiveness in improving TPS and reducing latency. Notably, our review is the first to incorporate and examine the standardization efforts of the ITU-T and ETSI, with a particular focus on activities related to blockchain sharding. Integrating these standardization activities allows us to bridge the gap between academic research and practical standardization in blockchain sharding, thereby enhancing the relevance and applicability of our review. Additionally, we highlight the existing research gaps, discuss critical challenges such as security risks and inter-shard communication inefficiencies, and provide insightful future research directions. Our work serves as a foundational reference for researchers and practitioners aiming to optimize blockchain scalability through sharding, contributing to the development of more efficient, secure, and high-performance decentralized networks. Our comparative synthesis further highlights that while Bitcoin and Ethereum remain limited to 7–15 TPS with long confirmation delays, sharding-based systems such as Elastico and OmniLedger have reported significant throughput improvements, demonstrating sharding’s clear advantage over traditional Layer 1 enhancements. In contrast to other state-of-the-art scalability techniques such as block size modification, consensus optimization, and DAG-based architectures, sharding consistently achieves higher transaction throughput and lower latency, indicating its position as one of the most effective Layer 1 solutions for improving blockchain scalability.
This paper investigates how the evolution of interbank payments towards central bank settlement, and thus central bank money as a settlement asset, has affected the dynamics of bank crises.We take the cluster of bank defaults in the United States in 2023 as a starting example and show how, alongside fractional reserves and fast digital communication, centralized settlement in central bank money played a critical role in triggering swift bank failures.We argue that technical centralization has amplified banks' fragility in the development of confidence crises, making bank runs easier and expanding the role of central banks to a point where conflict of interest becomes nearly inevitable.While previous literature has emphasized the effects of fast news spread and online banking, the role of settlement technology in recent bank runs has been largely overlooked.Thus we describe the stability consequences of different settlement architectures in detail, and also discuss potential improvements to the current architecture, particularly decentralized approaches built on distributed ledgers, to mitigate financial instability and reduce the negative effects of centralization without reverting to inefficient legacy systems.
The effective management of electronic medical records is critical to deliver high-quality healthcare services. However, existing systems often suffer from issues such as fragmented data, lack of interoperability, and weak privacy protections, which hinder collaboration among healthcare stakeholders. This paper proposes a blockchain-based system to securely manage and share medical records in a decentralized and transparent manner. By leveraging smart contracts and access control policies, the system empowers patients with control over their data, ensures auditability of all interactions, and facilitates secure data sharing among patients, healthcare providers, insurance companies, and regulatory authorities. The proposed architecture is implemented using a private Ethereum blockchain and evaluated through a scenario-based comparison with the Prince Sultan Military Medical City system, as well as quantitative performance measurements of the blockchain prototype. Results demonstrate significant improvements in data security, access transparency, and system interoperability, with patients gaining the ability to track and control access to their records across multiple healthcare providers, while system performance remained practical for healthcare workflows.
The integration of smart contracts and AI agents in clinical trials fosters a new paradigm of transparent, efficient, and patient-centric research. Smart contracts—self-executing code on permissioned blockchains—automate protocol enforcement, consent capture, randomization, and remuneration. AI agents complement them by enabling intelligent participant matching, real-time safety monitoring, and adaptive analytics. In a simulated Phase II oncology trial and pilot implementations in rare-disease cohorts, this framework demonstrated up to 60% faster enrollment, 87% reduction in protocol deviations, near-instantaneous safety alerting, and near complete audit trails. This article details the system architecture, methodologies, results, discussion, and conclusions, with no speculation on future work.
Type of the article: Research ArticleAbstractThe rise of decentralized finance (DeFi) presents new opportunities for accessing modern financial services. Despite their transformative architecture, most DeFi applications are currently unregulated, which exposes market participants to unforeseen risks. Therefore, understanding the level of connectedness between DeFi and traditional finance (TradFi) is crucial, particularly in emerging Asian markets where the level of cryptocurrency acceptance is high. Applying the time-varying parameter vector autoregressive model, this study examines the return connectedness between leading DeFi assets and traditional financial sectors in Indonesia, India, and Vietnam – the top three countries in Asia for cryptocurrency adoption. By analyzing TradFi at the industry level, this study captures sector-specific spillover dynamics that are essential to the monitoring of systemwide risk. The empirical results reveal low, time-varying return spillovers between DeFi and traditional financial sectors in the selected emerging Asian markets. The emerging financial sectors exhibit stronger linkages with broader traditional market indicators than with DeFi, in which assets interact primarily with each other. Emerging financial sectors and gold are the recipients of return spillovers, and DeFi assets act as the return transmitters. The current low degree of integration between DeFi and TradFi offers policymakers a window of opportunity to develop a robust financial regulatory framework that addresses issues of market stability and consumer protection while promoting the advancement of financial innovation.AcknowledgmentsWe thank the editors and anonymous reviewers for their valuable and constructive feedback, which has contributed significantly to improving the quality of this manuscript.
Luca Olivieri, David Beste, Luca Negrini, Lea Schönherr · 6 authors
Hyperledger Fabric (HF) is currently the one that made blockchain and smart contracts accessible to industries, providing highly customizable solutions for many enterprise use cases. Despite this, programmers are often discouraged from implementing smart contracts due to the high learning curve and security risks of naive smart contract implementations. At the same time, the advent of Large Language Models (LLMs) for code generation led to new possible scenarios such as creating new smart contract applications starting from natural language, allowing to reduce costs and development times. This paper investigates the maturity of LLMs for the code generation of HF smart contracts. In particular, we (i) generate smart contracts written in Go for HF starting from natural language descriptions, (ii) select state-of-the-art static analyzers of Go program, and (iii) perform a quality and security assessment of the generated smart contracts. Our empirical results show current LLMs do not produce high-quality smart contracts, and a relevant effort to debug and patch contracts containing bugs and possible vulnerabilities.
Abstract Buildings contribute significantly to global energy consumption, positioning them as pivotal to achieving global sustainability and climate goals. Although renewable energy technologies hold significant transformative potential, their integration into building systems is hindered by fragmented technological, economic, policy, and social dimensions. This systematic review addresses the existing lack of holistic synthesis by examining peer-reviewed studies published from 2019 to the second quarter (Q2) of 2025. Methodologically, the study adheres to the PRISMA 2020 framework to ensure transparency and replicability and employs bibliometric analysis to map thematic clusters across disciplines. Five major themes emerged from the synthesis: photovoltaic integration, retrofitting strategies, governance frameworks, smart grid applications, and stakeholder acceptance. Analysis highlights notable regional disparities, with Western Europe and East Asia demonstrating higher integration rates due to robust policy structures and established financial incentives. Conversely, regions like Sub-Saharan Africa and Latin America continue to experience significant barriers linked to financing constraints, regulatory fragmentation, and infrastructural limitations. Community-led microgrid initiatives in Kenya and Brazil emerge as exemplars of successful context-sensitive, low-cost renewable integration, emphasizing the value of inclusive governance and localized solutions. The review reconceptualizes buildings as active socio-technical nodes within decentralized energy networks rather than passive energy infrastructures. For renewable energy integration to progress effectively, coordinated systemic efforts spanning technical innovation, adaptive governance, and behavior-sensitive policy design are imperative. These insights carry substantial implications, offering actionable guidance for policymakers, engineers, and urban planners seeking equitable, contextually appropriate, and scalable renewable energy transitions within the built environment.
Reliable asset price data are critical for the functioning of decentralized finance (DeFi) protocols, particularly those involving collateralized lending. The accuracy of blockchain-based price oracles directly affects key processes such as collateral valuation, liquidation, and risk management. This paper presents a comprehensive empirical analysis of Chainlink Price Feeds (CPFs), the dominant oracle infrastructure in DeFi. We compile a novel dataset of over 150 million observations from 40 CPFs on Ethereum over an 18-month period, matched to benchmark prices from a centralized exchange. To identify the determinants of oracle inaccuracy, we estimate pooled OLS and fixed effects regressions, relating price deviations to design parameters, reporter dynamics, and market conditions. We then introduce a Markov-like state transition framework to model the resolution of target corridor violations, using multinomial logistic regression to estimate transition probabilities. Finally, we exploit position-level data from one of the largest decentralized lending markets and apply entity fixed effects regressions to examine how users adjust collateralization in response to oracle design. Our findings highlight economically significant deviations that are systematically related to oracle accuracy configurations and market stress, and show that users internalize these risks in their financial decisions. The results offer new insights for the design of resilient oracle systems and the management of risk in decentralized financial markets.
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.
The subject of the research is methods for detecting attacks in networks with the Proof-of-Stake (PoS) consensus mechanism. The purpose of this experimental investigation and analysis is to evaluate the effectiveness of classical machine learning algorithms for detecting malicious nodes in blockchain systems. The tasks include the analysis of blockchain technology vulnerabilities, the creation and use of a specialized dataset for PoS networks, as well as the construction and testing of machine learning models. The main focus is placed on comparing three algorithms – Random Forest, Support Vector Machine, and k-Nearest Neighbors – in order to determine their suitability for monitoring node activity and detecting anomalies. To solve the tasks set, the following methods were implemented: modeling, empirical, and mathematical approaches were applied. Modeling consisted of software implementation of the selected algorithms and subsequent analysis of their performance using accuracy, recall, F1-score metrics, and confusion matrices. Empirical methods were realized through testing the models on a partially synthetic dataset containing more than 10,000 records of blockchain nodes and transactions. Mathematical methods involved the calculation of statistical performance indicators and the analysis of feature importance that characterizes node behavior. The achieved results include the validation of a dataset for PoS blockchains that incorporates key operational parameters of transactions and nodes, the development of recommendations for further use of machine learning models, and the testing of selected models. Conclusions. The study demonstrated that machine learning is an effective tool for identifying anomalies and malicious activity in blockchain systems. The obtained results lay the foundation for further research, which may focus on expanding the feature space, integrating deep neural networks, developing ensemble approaches, and adapting methods to different types of blockchains.
Open access
Economic and Technological Systems Analysis
Advanced Research in Systems and Signal Processing
Hadis Rezaei, Ahmed Afif Monrat, Karl Andersson, Francesco Palmieri
The deterministic nature of blockchain technology creates fundamental difficulties in producing secure random numbers within smart contracts, a limitation that exposes vulnerabilities in applications such as decentralized finance (DeFi) protocols and blockchain-based gaming platforms. From our observations, the current state-of-the-art detection tools suffer from inadequate precision while dealing with random number vulnerabilities. To address this problem, we propose TaintSentinel, a novel path-sensitive vulnerability detection system designed to analyze smart contracts at the execution path level and gradually analyze taint with domain-specific rules. This paper discusses a solution that incorporates a multifaceted approach, integrating rule-based taint analysis to track data flow, a dual-stream neural network to identify complex vulnerability signatures, and evidence-based parameter initialization to minimize false positives. The two-phase operation of the system involves the construction of semantic graphs and the analysis of taint propagation, followed by pattern recognition using PathGNN and global structural analysis via GlobalGCN. Our experiments on 4,844 contracts demonstrate the superior performance of TaintSentinel relative to existing tools, yielding an F1-score of 0.892, an AUC-ROC of 0.94, and a PRA accuracy of 97%.
The problem of reproducibility of experiments in optimizing validator allocation in blockchain networks with Proof of Stake consensus was investigated, in particular due to the absence of standardized datasets and unified testing methods, which complicates the objective comparison of algorithms. To tackle this issue, we propose a method for building test datasets that rely on deterministic pseudorandom sequence generators and validator profiles calibrated against Ethereum network statistics. Each validator is described by a set of parameters that includes the stake size with the minimum requirement according to Ethereum standards, performance with a uniform distribution, reliability in a high range, network delays depending on the geographical proximity of participants, geographical location according to the actual statistics of validator distribution by regions, quality of network connection, and slashing history according to the violation statistics in the Beacon Chain. Three datasets of different scales were created for small, medium, and large network configurations with fixed initial values of the generators to ensure full reproducibility of experiments. A multi-criteria evaluation system was developed based on a generalized quality indicator that maximizes system throughput and minimizes load imbalance and network delays with scientifically grounded weighting coefficients. The tenfold testing protocol ensures the statistical reliability of results and reduces the impact of randomness on conclusions. The experiments conducted a comparative analysis of four allocation algorithms: a hybrid metaheuristic method based on particle swarm optimization with local search, random allocation with correction, an adapted Ethereum shuffling mechanism, and a greedy algorithm. The experimental results revealed scale-dependent efficiency of the algorithms: the hybrid method provides high optimization quality at all investigated scales, but quadratic growth of execution time limits its application to periodic offline planning of network configuration; the shuffling mechanism demonstrates stable medium-quality results with fast execution; the random method is characterized by moderate speed with variable results; the greedy algorithm shows maximum speed with deterministic results but variable efficiency depending on the network scale. The proposed method forms a basis for standardizing experimental research in Proof of Stake consensus systems. It ensures the objective comparison of new algorithmic solutions for validator allocation in decentralized blockchain networks.
Open access
Advanced Research in Systems and Signal Processing
Yerlan Kistaubayev, Francisco Liébana‐Cabanillas, Aijaz A. Shaikh, Galimkair Mutanov · 6 authors
It has been recognized that Blockchain technology contributes to environmentally sustainable development goals (SDGs). It has emerged as a disruptive innovation capable of transforming various economic and social sectors significantly. This conceptual paper is driven by the need to explore how blockchain, specifically a consortium-based Ethereum architecture, can be integrated into higher education institutions to ensure data sovereignty, integrity, and verifiability while adhering to legal and ethical standards such as GDPR. We propose a multi-layered blockchain-based model for Kazakhstan’s Unified Platform of Higher Education (UPHE). This model employs hybrid on-chain/off-chain data storage, smart contract automation, and a Proof-of-Authority consensus mechanism to address system limitations, including data centralization and inadequate verification of academic credentials. Empirical simulations using Blockscout and Ethereum-compatible tools demonstrate the model’s feasibility and performance. This paper contributes to the growing discussion on educational blockchain applications by presenting a scalable, secure, and transparent architecture that aligns with institutional governance and Environmental, Social, and Governance (ESG) principles. It also supports the objectives of UN SDG 4 (i.e., Quality education) by fostering trust, transparency, and equitable access to verifiable educational credentials.