Internet of Things (IoT) technologies in the healthcare industry, also known as the Internet of Medical Things (IoMT), have proven to greatly improve patient monitoring, diagnostics, and clinical decision-making. The increasing prevalence of resource-challenged medical devices, wireless connectivity, and cloud services, however, has brought new risks around security and privacy concerns that can now directly impact patient safety and data integrity. In this paper, a thorough study of 41 peer-reviewed research papers from January 2018 through May 2025 revealed the current state of security vulnerabilities and resilience strategies in healthcare IoT systems. It provides a comprehensive analysis of security threats at the device, network, and application levels such as unauthorized access, malware and ransomware, data breaches, and denial-of-service attacks delivered in a systematic manner. This contrasts with existing surveys, which consider single security mechanisms and improve upon various multi-layered security means such as AI-enabled anomaly detection, blockchain-based authentication and auditability, low-compute cryptographic techniques, and privacy-preserving methods such as federated learning. The outcomes also show that although emerging technologies add a great deal of security and trust capabilities, issues on scalability, interoperability, deployment, and regulations are not yet fully addressed. This review highlights important knowledge gaps and offers structured knowledge and future directions for research to address the design of secure, resilient, and practically deployable IoMT architectures for real-world healthcare environments.
The Role of Artificial Intelligence in Optimizing Supply and Demand Management in Modern Businesses: A Review Article Faezeh Mokarrami1 1- mokarrami76@gmail.comM.Sc. Student in Entrepreneurship, Small Business Concentration, Islamic Azad University, Electronic Branch Abstract This narrative review examines how artificial intelligence has transformed supply and demand management in contemporary supply chains, particularly within emerging enterprises and environments characterized by volatility and uncertainty. By integrating conceptual, historical, and applied literature, the article demonstrates how artificial intelligence enhances core supply chain functions—such as demand forecasting, inventory control, logistics planning, supplier selection, and risk management—through data-driven decision-making, intelligent automation, and predictive analytics. Furthermore, the strategic role of artificial intelligence in strengthening supply chain resilience, agility, flexibility, transparency, and sustainability is highlighted, especially when combined with machine learning, deep learning, big data, blockchain, and the Internet of Things. A central axis of this review is the transition from reactive, historical data-based forecasting toward demand sensing and proactive, real-time decision-making. Simultaneously, the article emphasizes that the adoption of artificial intelligence depends not only on technical capabilities but also on organizational readiness, institutional context, data governance, and ethical considerations such as fairness, transparency, and environmental impacts. Ultimately, this review concludes that artificial intelligence has evolved from a marginal tool to a structural driver of competitiveness and recovery capacity in supply chains, although significant gaps remain regarding human-AI collaboration, longitudinal evidence, and context-appropriate adoption.
This study develops and validates an ecological economics framework integrating green logistics practices to enhance supply chain resilience in Southeast Asia's automotive sector. Using mixed-methods analysis of 272 firms across Thailand, Indonesia, and Malaysia, structural equation modelling confirms three hypotheses: green logistics adoption significantly predicts resilience (β=0.38, p<0.001); ecological economics tools (full-cost accounting, ecosystem service valuation) double these gains through heightened environmental cost awareness; and the hybrid framework yields superior economic returns compared to standalone practices. Thailand leads (SCR=65.3) due to BCG policies, while Indonesia lags (ROI=9.1%) amid nickel dependency. Simulations project +34% resilience under carbon pricing scenarios. Qualitative interviews reveal disaster-driven adoption and SME capex barriers, with ECA>4.0 thresholds flipping green logistics from cost to profit centre. Findings advance dynamic capabilities theory with biophysical limits, resolve triple bottom line tensions, and deliver managerial roadmaps (rail pilots→FCA training→blockchain Scope 3) plus ASEAN policy blueprints (CBAM harmonisation, $500M capacity fund). The framework positions the ASEAN automotive sector for regenerative leadership, converting natural capital from externality to competitive asset amid global decarbonisation pressures. These findings offer actionable insights for managers, investors, and policymakers seeking to align profitability with ecological resilience in emerging economies.
Open access
Supply Chain Resilience and Risk Management
Sustainable Supply Chain Management
Infrastructure Resilience and Vulnerability Analysis
Khoya (khoa or mawa) is a traditional dairy product, prepared by heating and concentrating milk, which is widely used in preparation of indigenous milk sweets. But, challenges such as process variability, quality deterioration, microbial contamination, adulteration, limited shelf life and inefficient supply chain management hinder its production and distribution. New solutions to these challenges are available across the khoya value chain due to recent advancements in artificial intelligence (AI) and Industry 4.0 technologies. This review highlights the applications of AI in khoya processing, packaging, transportation, distribution and quality management. The role of machine learning, deep learning, computer vision, Internet of Things (IoT), digital twins, smart sensors, and blockchain in process optimization, automated quality inspection, adulteration detection, shelf-life prediction, intelligent packaging, cold-chain monitoring, logistics optimization and demand forecasting is explored. We also review AI-enabled analytical tools for rapid and non-destructive quality assessment, such as hyperspectral imaging, electronic nose, and electronic tongue. The review also discusses the contribution of AI to improving food safety, traceability, sustainability and operational efficiency, as well as to reducing post-harvest losses and environmental impacts. Finally, the paper discusses the existing challenges, future research directions, and prospects of AI-enabled smart dairy manufacturing. The review finds that AI can play a significant role in improving the quality, safety, efficiency, and sustainability of the khoya industry and helping its transition to intelligent and data-driven dairy processing.
Omojola Awogbemi, S. A. Aasa, Oluwaseun O. Martins, Anthony O. Onokwai
Abstract The worrisome economic, environmental, and energy security implications of the continuous use of fossil-based sources as road transport fuel have made Nigeria consider sustainable alternatives. With the country’s abundant natural gas reserves and growing climate commitments, compressed natural gas (CNG) presents a viable pathway for decarbonizing road transport, curbing urban air pollution, and ensuring energy security. The current study examines the adoption, deployment, and integration of CNG into Nigeria’s road transport ecosystem. The study reviews the CNG resources and infrastructure, impact and achievements, and highlights the challenges of CNG deployment as a road transport fuel, case studies from other jurisdictions, suggestions for improvement, and future research perspectives. Though reasonable grounds have been covered, overcoming the technological and infrastructure gaps, economic and financial inadequacy, health, environmental, and safety issues, ensuring social and stakeholder acceptance, and instituting appropriate policy and regulatory frameworks are fundamental to ensure scalability and energy security. Nigeria can leverage case studies from other jurisdictions to leapfrog and accelerate nationwide deployment, mitigate risks, and guarantee a low-carbon road transport future for Nigeria. More sensitization campaigns, investment and fiscal incentive models, price reduction strategies, and rapid upgrade of CNG infrastructure across the country to ensure wide acceptability, affordability, and nationwide deployment. Future research should integrate lifecycle and techno-economic analysis, smart metering, blockchain tracking, spatial modeling, macroeconomic impact, and process optimization to guide stakeholders in designing a resilient, inclusive, and scalable CNG transport framework for Nigeria.
This paper develops a Quantum-Institutional Automated Negotiation (QIAN) algorithm as an intelligent decision support system for carbon credit markets, contributing to quantum game theory applications in automated negotiation and institutional decision-making. We extend the Eisert–Wilkens–Lewenstein (EWL) framework by introducing an Institutional Filter Function Φ_C that maps continuous quantum strategies—phase shifts and superpositions—onto finite, legally viable contract archetypes. This filter models regulatory, political, and organizational constraints that collapse the infinite quantum strategy space into a tractable finite set, enabling computationally efficient decision support. We prove convergence of the automated negotiation algorithm to a Pareto-superior Nash Equilibrium and demonstrate, through Monte Carlo simulation with literature-calibrated parameters, that the collapsed quantum equilibrium yields a mean joint utility uplift of 13.5% over classical cooperation (95% CI: 9.8%–17.3%, p &lt; 0.001), with the upper bound reaching 17.3% and 26.8% of simulations achieving uplifts in the 15–30% range. The framework maps directly to blockchain-based smart contracts, providing a deployable mechanism for sustainable carbon markets that aligns with SDG 13 (Climate Action) and SDG 17 (Partnerships). This work advances quantum game theory from abstract formalism to computational institutional design, offering a novel decision support approach for negotiation analysis under real-world constraints.
The rapid growth of cybercrime has significantly increased the importance of digital evidence in criminal investigations and judicial proceedings. However, ensuring the admissibility and reliability of electronic evidence remains a complex challenge due to technological advancements, evolving legal standards, cross-border investigations, and concerns regarding evidence integrity. This narrative review examines the legal and forensic dimensions of digital evidence by synthesizing contemporary literature on its sources, characteristics, governing legal frameworks, and the factors influencing its acceptance in court. The review discusses key issues related to authentication, chain of custody, expert testimony, procedural fairness, and evidence validation, while also evaluating the impact of emerging technologies, including artificial intelligence, blockchain, the Internet of Things, and deepfake detection on digital forensic practice. The findings indicate that reliable digital evidence requires standardized forensic procedures, scientifically validated investigative methods, and harmonized legal frameworks capable of addressing rapidly evolving cyber threats. Strengthening collaboration among forensic practitioners, legal professionals, researchers, and policymakers will be essential for improving evidence integrity, enhancing judicial confidence, and supporting effective cybercrime investigations. The review provides an integrated perspective that contributes to ongoing discussions on developing secure, transparent, and legally robust digital evidence management practices.
Abstract Internet of Things (IoT) technologies and healthcare present revolutionary chances to improve operational efficiency, patient outcomes, and tailored medication transformation. This paper thoroughly investigates IoT in healthcare using Latent Dirichlet Allocation (LDA) to spot important trends and research gaps in current work. To achieve this, researchers have comprehensively analyzed 11,586 published papers from 2006 to 2024 which are extracted from Scopus database. Researchers have identified 2, 5, and 10 key topics to define significant areas of the research. Over time, it compares research topics to show how important areas, including wearable technology, artificial intelligence-powered analytics, blockchain for safe data management, and edge computing, have evolved. The paper additionally examines important issues, including data privacy issues, lack of interoperability, and restricted inclusiveness for underprivileged communities. Emphasizing inclusivity, ethical compliance, and pragmatic implementation tactics catered to different healthcare environments, a strategy framework is suggested to help solve these difficulties. This paper helps IoT implementation in healthcare advance by giving actionable insights, particular discoveries, and future research directions, thereby opening the path for more fair, efficient, and sustainable healthcare systems.
Under the dual carbon targets, China's energy companies are speeding up their green transformation, but they usually encounter some common obstacles including lack of capital, weak technical assistance and an incomplete risk control system. The combination of digital technology and financial services provides new approaches to solve these problems. According to the specific characteristics of the transformation of energy enterprises, this research examines the mechanisms of digital finance from two aspects – financing enhancement and technological enhancement. It is found that methods such as digital green loans, bonds and equity financing can efficiently relieve the financial pressure of enterprises, while technologies like big data, blockchain and artificial intelligence can greatly improve the accuracy of emission reduction and the efficiency of energy operation. Furthermore, the enhancing effects have regional differences and threshold characteristics. Thus, countermeasures are put forward from four fields: improving service provision, deepening technological integration, setting up a risk management system and improving policy regulation, which offer guidance for the actual transformation of energy enterprises and the development of relevant policies.
Network slicing and resource provisioning in 6G focus on creating multiple customized virtual networks over a shared infrastructure. However, these approaches also introduce challenges, like increased architectural complexity, higher implementation costs, security vulnerabilities between slices in resource optimization across highly dynamic and heterogeneous network environments. In this work, Exponentially Tactical Unit Algorithm (ETUA) is devised for network slicing in 6G. Initially, blockchain-enabled 6G network is simulated, and the set of features, like user device type, delay rate and packet loss rate are collected from various devices. Moreover, network slicing is done by ETUA that integrates Exponentially Weighted Moving Average (EWMA) and Tactical Unit Algorithm (TUA). Finally, resource allocation is performed using Attention High-order Deep Network (AHoNet) by considering the parameters that includes bit error probability, sum rate and trust. The efficacy of ETUA is examined by bit error probability, utility and latency with 0.012, 0.950 and 0.509 Sec.
The rapid development of Large Language Models (LLMs) has led to the wide adoption of autonomous AI agents. These agents increasingly form decentralized Agent-to-Agent (A2A) networks to collaborate on complex tasks. However, a key bottleneck is the cost of GPU inference, which requires a reliable compensation system for untrusted participants. While blockchains provide accountability, they are too slow and costly. Inspired by Payment Channel Networks (PCN), we propose Ledgent (a portmanteau of Ledger and Agent), an accountable A2A PCN architecture. We combine PCN primitives with the underlying LLM serving infrastructure, enabling pay-as-you-go token streaming, compute-aware routing, and response quality verification without relying on central server. Evaluation from both simulation and prototype shows that Ledgent achieves high throughput, while introducing minimal cryptographic latency overhead.
Decentralised finance (DeFi) is a relatively new trend in finance that uses blockchain, smart contracts, and distributed ledger technology to offer financial services in a decentralised manner. Although scholars have made many theoretical advances in decentralised finance in recent years, knowledge of its theoretical structure and future research areas remains limited. This is why this study provides a bibliometric analysis of 1002 articles on DeFi published in Scopus between 2012 and 2026. The analysis uses performance analysis and a science mapping approach based on citation analysis, co-authorship, bibliographic coupling and keyword co-occurrence analysis. The results reveal a remarkably high annual growth rate of 39.34% and DeFi’s dynamism and interdisciplinary nature. The three main countries involved in DeFi research are the USA, China, and the UK. Management Science, Energy Economics and Technological Forecasting and Social Change became the main scientific journals for disseminating knowledge about DeFi. Analysis of thematic changes showed a transition of scientific interests from blockchain and cryptocurrencies to new topics, like artificial intelligence, sustainability, governance, and financial inclusion. Overall, the current study provides a better understanding of the intellectual, conceptual, and social basis of DeFi and highlights possible research areas in the use of artificial intelligence in DeFi, decentralised governance, and sustainable digital financial system development.
Lex Criptográfica Constitucional (LCC) propone un marco de gobernanza para la era algorítmica basado en la primacía de la vida, la libertad, la soberanía del Ser, la propiedad, la diversidad, la cooperación y el bienestar psicosocial global. Integra Derecho, tecnología, blockchain, inteligencia artificial y filosofía humanista bajo el paradigma Codex +HUMANO.
The article discusses a decentralized electronic voting system based on blockchain technology. This study aims to improve the performance and fault tolerance of blockchain-based electronic voting systems by introducing the Automated Leaderless Byzantine Fault Tolerance (AL-BFT) consensus protocol. This study aims to develop and evaluate an electronic voting system model that applies the proposed AL-BFT consensus mechanism in a permissioned peer-to-peer network. The methods used include computer modeling of a peer-to-peer (P2P) network, implementation of a decentralized ledger, and experimental load testing of the consensus protocol. System performance is evaluated using key metrics, such as transaction latency, throughput (requests per second), fault tolerance threshold, and scalability. The study results include the development of a conceptual architecture for the electronic voting system, the identification of its core components, and the analysis of their interactions to ensure data integrity and the reliability of voting results. At each stage of the electoral process, data security is considered, and additional protection mechanisms are analyzed to enhance system robustness. Eliminating the leader election phase from the consensus process is a key feature of the proposed approach, thereby reducing coordination overhead and enabling more efficient agreement among nodes. The proposed AL-BFT protocol reduces transaction latency and improves throughput while maintaining the fault tolerance level of traditional Byzantine Fault Tolerance-based approaches. The results confirm improved efficiency compared to classical leader-based consensus mechanisms, particularly in small permissioned blockchain networks. Conclusions. A practical implementation of the system has been developed and tested under real simulated load conditions. The proposed solution ensures stable system operation and reliable consensus formation. The system can be effectively applied to university elections, organizational voting, and other scenarios that require transparency, security, and manipulation resistance
Secure distributed systems offer reliability and privacy guarantees that are crucial across applications ranging from blockchains and cloud computing to fault-tolerant distributed Cyber-Physical Systems (CPS). These protocols enable groups of mutually distrusting parties to collaborate and execute tasks at scale while maintaining robust security guarantees against faulty and adversarial behavior. Blockchains demonstrate that the reliability half of this promise is achievable in practice, with deployments spanning hundreds of parties over geo-distributed testbeds. The privacy half has {\it not} kept pace: despite rapidly growing demand from applications such as anonymous networks and privacy-preserving AI, systems at blockchain scale have been unable to offer privacy guarantees. At the other end of the spectrum, the reliability techniques that succeeded in the blockchain setting are far too expensive for emerging distributed CPS applications, where hardware and network conditions are substantially weaker. In both settings, existing solutions are too slow and resource-intensive to be deployed in practice. This thesis asks whether both guarantees can be delivered at the scale their applications demand, on the hardware those applications actually run on.The first half of this thesis builds Multi-Party Computation (MPC) protocols for systems with a hundred or more parties over real-world geo-distributed networks, motivated by modern blockchains. MPC enables $n$ mutually distrusting parties to jointly compute any function over their private inputs. We identify computationally expensive heavyweight cryptography based on number-theoretic hardness assumptions as the central scalability bottleneck and address it by designing protocols entirely using \emph{lightweight} cryptography such as symmetric-key encryption and cryptographic Hash functions. These tools are two orders of magnitude cheaper than heavyweight operations and additionally offer post-quantum security. We present three works in this line: HashRand, a random beacon protocol, Velox, an MPC protocol achieving fairness, and Aeternum, a framework for guaranteed output delivery in asynchronous MPC and dynamic proactive secret sharing. We implement and evaluate all three, showing that they outperform prior work by two orders of magnitude and scale to $100$ or more parties on geo-distributed testbeds with practical latency and communication costs.The second half turns to Asynchronous Approximate Agreement (AAA) for distributed CPS with a hundred or more parties, motivated by robot and drone swarms. Unlike randomized Byzantine Agreement (BA) protocols, which depend on expensive heavyweight cryptography to produce common coins, AAA protocols are deterministic and avoid these tools. These protocols still have a high cubic communication cost, which is unaffordable in the low-bandwidth CPS setting. We introduce \emph{Relaxed Validity}, an approximate validity property that allows nodes to trade the accuracy of the protocol's output for sub-cubic communication.Leveraging this property, we design SensorBFT and Delphi, both AAA protocols with sub-cubic communication overhead. We apply both to agreement problems in the CPS domain and experimentally demonstrate their scalability relative to prior works. Both consume an order of magnitude less energy than prior protocols based on randomized BA, a decisive metric on resource- and power-constrained sensor devices.
This informative document explores the evolving digital asset landscape, covering cryptocurrency, NFTs, blockchain technology, Web3, and emerging market trends. It provides readers with practical insights into digital ownership, market developments, and the importance of research when evaluating opportunities in the growing blockchain economy. Collective Shift
Technological innovations are often perceived as something alien, terrifying, and monstrous. Blockchain technology that creates shared “blocks” of information, which are interconnected and verified by the network comes as no exception. Two main features of blockchain (1) the absence of a gatekeeper organisation controlling the data, and (2) the fact that the information is rather hard to corrupt and hack, makes the technology very attractive and versatile. It is also what makes it appear frightening, especially for the traditionally centralised and hierarchical disciplines like law. As there is no one to control the data and the access to it, blockchains open a whole world of new possibilities with cryptocurrencies being one of the most popular examples.Approaching blockchain technologies in the context of J. J. Cohen’s monster theory demonstrates that they can be perceived as modern monsters. Our inability to understand the technology and the way it works makes this particular monster both fearful and desired (thesis 6), and law reacts to the fears that circulate in the society. Thus, blockchain technologies are often banned by law in a similar way as in medieval narratives dragons were banished by saints and heroes. Building on Cohen’s thesis 7, which argues that monsters show how we (mis)interpret our surroundings, this article will employ the historical perspective upon the fear of the monstrous to create a better understanding of the legal policies surrounding blockchains. By comparing current legal decisions concerning blockchain technology with the strategies of dealing with monsters, offered by medieval chronicles and collections of wonders (including William of Malmesbury and William of Newburgh), we will analyse the modern way of controlling monsters – or controlling the fear of them.
Sofiatin Nur Afifah, Isnaini Rosyida, Webbyani Kartika Sari
This study aims to analyze the effect of Blockchain technology adoption and corporate transparency on firm value, with audit quality as a moderating variable, in State-Owned Enterprises (SOEs) listed on the Indonesia Stock Exchange for the 2021–2025 period. The study used a quantitative approach with secondary data obtained from annual reports and company financial statements. The sample was determined using a purposive sampling technique, resulting in 13 SOEs with a total of 65 observations. Data analysis was performed using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) method through SmartPLS. The results showed that Blockchain technology adoption had a positive and significant effect on firm value (β = 0.287; t = 2.806; p = 0.006), while corporate transparency had no significant effect on firm value (β = 0.020; t = 0.180; p = 0.857). Audit quality also has a positive and significant effect on firm value (β = 0.490; t = 8.187; p < 0.001). As a moderating variable, audit quality is proven to strengthen the influence of Blockchain technology adoption on firm value (β = 0.195; t = 2.744; p = 0.007), but is unable to moderate the relationship between corporate transparency and firm value (β = -0.068; t = 0.955; p = 0.341). These findings indicate that Blockchain implementation supported by high audit quality can increase investor confidence and firm value, while corporate transparency has not been a major factor in increasing firm value in SOEs.
Against the background of the global "dual carbon" goal and the EU Carbon Border Adjustment Mechanism (CBAM), targeting problems such as missing trust in emission reduction and insufficient technological collaboration in cross-border low-carbon supply chains, this paper incorporates blockchain technology, vertical spillover of emission reduction and consumer low-carbon preference into a unified analytical framework. It constructs a two-echelon cross-border supply chain model consisting of a single supplier and a single manufacturer, builds Stackelberg game models under centralized decision-making and decentralized decision-making respectively, comparatively analyzes the optimal emission reduction levels, pricing strategies and profit distributions under two scenarios with and without vertical spillover, and verifies the conclusions through numerical simulation. The research shows that the EU CBAM carbon tax, vertical spillover of emission reduction and consumer low-carbon preference form a positive synergistic incentive, which significantly lifts the supply chain's emission reduction level and overall profit, and the synergistic effect is more prominent under centralized decision-making. A rising emission reduction cost coefficient will restrain enterprises' investment in emission reduction, and vertical spillover will aggravate this restraining effect. Whether vertical spillover is considered or not, centralized decision-making outperforms decentralized decision-making in both emission reduction efficiency and total supply chain profit; the higher the carbon tax rate and vertical spillover rate, the wider the gap between the two. This paper further puts forward management insights from the aspects of enterprise technology sharing, decision-making mode selection and government policy guidance, so as to provide theoretical reference and decision support for cross-border supply chains to respond to CBAM regulations and realize low-carbon transformation.
Oleksandr Pidpalyi, Олександр Романов, Larysa Globa, Антон Романов · 6 authors
The subject matter of the article is the iTZBEI (Integrated Trust–ZTA–Blockchain SDN Efficiency Index) – a novel composite metric for quantitative security assessment of software-defined networks (SDN) integrating Zero Trust Architecture (ZTA) and Blockchain technologies. The relevance of the research is determined by the fact that the centralized SDN control model generates critical vulnerabilities, including DDoS attacks, unauthorized routing manipulation, and insider threats – for which no unified quantitative evaluation framework currently exists. The study introduced a formalized aggregated security metric that enables continuous monitoring and comparative assessment across all components of the SDN–ZTA–Blockchain architecture. The tasks to be solved include: (1) identification of principal SDN attack vectors; (2) formalization of a transaction-processing algorithm covering the full access lifecycle; (3) definition of nine local security indicators; and (4) construction of the iTZBEI index with justified weighting coefficients. The methods used combine mathematical formalization of access control processes, cryptographic transaction verification, and experimental emulation of attack scenarios in a Mininet–OpenDaylight–Hyperledger Fabric environment. Conclusions. The obtained results of the article consist in the development of a functional algorithm that performs dynamic verification of user requests, makes adaptive authorization decisions according to the principles of least privilege, and records these decisions in an immutable distributed ledger. A metrics system is proposed, including local indicators such as the Continuous Authorisation Integrity Score (CAIS), the Blockchain Audit Integrity Score (BAIS), and the Local Policy Integrity (LPI). On this basis, the generalized Integrated Trust and Zero-Trust Blockchain Evaluation Index (iTZBEI) is described as an aggregated metric for comparative evaluation and continuous monitoring of the network’s security state. Scientific novelty. This study introduces a unified SDN + ZTA + Blockchain framework for network security, formalizes a transaction-level algorithm that directly links access decisions with distributed audit procedures, and proposes the iTZBEI metric as the first integral indicator for evaluating the integration’s effectiveness in dynamic network environments.
Dileep Kumar Murala, G. Siva Krishna, P. N. V. M. Syamala Rao, Madhusudana Rao Nalluri · 5 authors
Integrating blockchain technology with cloud computing has enabled Blockchain as a Service (BaaS), a cloud-based paradigm that allows users to design, deploy, and manage customized blockchain applications, including smart contracts and domain-specific business functionalities. BaaS providers manage infrastructure provisioning, maintenance, and scalability while ensuring quality of service (QoS) compliance through service level agreements (SLAs). However, existing resource management approaches often struggle to satisfy dynamic performance requirements in a cost-efficient manner without increasing operational overhead or reducing provider efficiency. This paper presents a structured Adaptive SLA-based assistance framework for deploying Hyperledger Fabric on cloud platforms. The framework integrates automated performance monitoring using Hyperledger Caliper, SLA violation detection through programmed SLA chaincode, and automated VM scaling via the OpenStack4J API within a 3E (effective–efficient–economical) verification methodology. A greedy multi-objective scheduling mechanism guides parameter optimization by selecting scaling actions according to marginal performance gain per unit cost. Experiments were conducted on the Nectar Research Cloud using Hyperledger Fabric 2.5 to evaluate the impact of VM size, block size, peer count, and storage configuration on throughput (TPS) and average latency. Results demonstrate that the framework consistently achieves performance targets of 50%, 100%, and 200% above the baseline configuration through adaptive resource reconfiguration. Among evaluated strategies, Comb2, which combines dual block size adjustment with VM scaling, emerged as the optimal balanced configuration in terms of performance and cost efficiency. While automated monitoring, SLA enforcement, and VM scaling are fully implemented, continuous online scheduling under dynamically changing workloads remains future work. The proposed framework establishes a practical foundation for SLA-driven blockchain optimization in cloud environments and supports future extensibility to additional BaaS platforms.