Delia-Elena Bărbuță, Cristian Nicolae Buţincu, Adrian Alexandrescu
Encouraging green innovation has become increasingly important in order to address sustainability challenges, yet startups and early-stage initiatives in this domain face numerous barriers, including lack of structured guidance, limited access to mentorship and funding, and concerns around intellectual property protection. This paper presents a solution entitled SPARK-IT GREEN, a decentralized platform designed to support eco-friendly innovation through a secure and transparent ecosystem. Building upon the original SPARK-IT framework, the platform introduces several new features, including a Green Business Model Canvas, lifecycle-aware AI matchmaking, decentralized governance via a Decentralized Autonomous Organization (DAO), and advanced intellectual property protection through blockchain-integrated encrypted storage. As an incentive for users to join the proposed SPARK-IT system, they can earn reward and reputation tokens. Our novel solution empowers innovators to define sustainability goals, find verified expert support, and apply for DAOgoverned funding based on community-aligned green priorities. It uses smart contracts deployed on a permissioned blockchain, token-based incentives, and a semantic matchmaker enriched with green project metrics. A detailed use case scenario shows how the proposed system can be used by a biodegradable packaging startup. SPARK-IT GREEN sets the foundation for a scalable, transparent, and sustainability-focused innovation ecosystem.
The blockchain technology is sweeping the globe. Blockchain has emerged as a disruptive technology for the future generation of multiple industrial applications because to its decentralised, transparent, and secure nature. Cloud of Things, which is possible by the marriage of cloud computing with the Internet of Things, is one of them. Considering the need for security and efficiency as a problem, this paper proposes a safe and efficient smart home design that combines blockchain and cloud computing technologies to provide a comprehensive solution. The decentralised nature of blockchain technology allows it to provide processing services and create transaction copies of obtained sensible user data from smart homes. Blockchain, a distributed ledger technology that provides an immutable log of transactions recorded on a distributed network, has lately gained popularity as the underlying technology of cryptocurrencies and is revolutionising data storage and processing in computer network systems. Blockchain is seen as a possible option for future data-driven networks (DDNs) to provide safe data storage, sharing, and analytics, user privacy protection, strong, trustworthy network governance, and decentralised routing and resource management
A mobilidade urbana representa um dos maiores desafios das cidades contemporâneas, sendo a imprevisibilidade do transporte público um fator crítico que impacta milhões de cidadãos e turistas. Atrasos decorrentes de congestionamentos, acidentes e outros eventos inesperados, somados à complexidade das rotas, comprometem significativamente a experiência do usuário. Este artigo apresenta o SIGRÔ (Sistema Inteligente de Gerenciamento de Rotas de Ônibus), uma solução inovadora para o rastreamento e previsão em tempo real da localização de ônibus coletivos. A arquitetura do sistema baseia-se em uma rede descentralizada Web3, na qual cada veículo atua como um nó comunicante em uma malha peer-to-peer (p2p), utilizando GSM LTE-M e, de forma redundante, LoRa, para mitigar falhas de cobertura. Cada ônibus é equipado com sistemas embarcados dotados de Unidades de Processamento Neural (NPUs), que aplicam Inteligência Artificial para corrigir perdas de sinal de GPS e aprimorar estimativas de chegada, integrando dados históricos e em tempo real. O ecossistema é complementado por um aplicativo multiplataforma (iOS, Android, WebApp e sistema embarcado), que oferece planejamento de rotas, visualização em tempo real, informações sobre paradas e uma interface de gestão para operadores, permitindo o reporte de incidentes. O projeto tem como objetivo aprimorar a pontualidade percebida, otimizar a experiência do usuário e fornecer dados estratégicos para a gestão inteligente do transporte público urbano.
The integration of Internet of Things (IoT) technologies into public healthcare enables continuous monitoring and sustainable health management. However, conventional frameworks often depend on transmitting and storing raw personal data on centralized servers, posing challenges related to privacy, security, ethical compliance, and long-term sustainability. This study proposes a privacy-preserving framework that avoids the exposure of true health-related data. Sensor nodes encrypt collected measurements and collaborate with a secure computation core to evaluate health indicators under homomorphic encryption, maintaining confidentiality. For example, the system can determine whether a patient’s heart rate within a monitoring window falls inside clinically recommended thresholds, while the framework remains general enough to support a wide range of encrypted computations. A compliance verification client generates zero-knowledge range proofs, allowing external parties to verify whether health indicators meet predefined conditions without accessing actual values. Simulation results confirm the correctness of encrypted computation, controllability of threshold-based compliance judgments, and resistance to inference attacks. The proposed framework provides a practical solution for secure, auditable, and sustainable real-time health assessment in IoT-enabled public healthcare systems.
ABSTRACT Blockchain technology has emerged as a pivotal solution for securing sensitive data across various domains, including artificial intelligence (AI), supply chain management, cloud computing, and healthcare. Its core attributes, confidentiality, decentralization, security, and privacy, offer significant advantages to the healthcare sector. The integration of Internet of Things ( IoT ) devices within healthcare systems enhances interoperability, enabling seamless communication between healthcare software and IT infrastructure. However, traditional healthcare systems face persistent security challenges, including phishing, masquerading, and identity theft. To address these issues, we propose a secure blockchain‐based decentralized application for generating, maintaining, and validating medical certificates. This application facilitates secure interactions among healthcare entities, including hospitals, patients, and IoT devices, while ensuring confidentiality, authentication, and access control through smart contracts. The proposed blockchain architecture enhances data integrity and secure transmission using the zero‐knowledge proof ( ZKP ) mechanism. Additionally, we incorporate the interplanetary file system ( IPFS ) for off‐chain data storage to optimize storage costs and enhance security through Ethereum smart contracts. Performance evaluations demonstrate the effectiveness of our approach in mitigating existing security vulnerabilities, thereby offering a robust and scalable solution for secure healthcare data management.
As blockchain systems grow in complexity, secure and efficient smart contract development remains a crucial challenge. Large Language Models (LLMs) like DeepSeek promise significant enhancements in developer productivity through automated code generation, debugging, and testing. This study focuses on Solidity, the dominant language for Ethereum smart contracts, where correctness, gas efficiency, and security are critical to real-world adoption. This study evaluates the capabilities of DeepSeek’s V3 and R1 models, a non-reasoning Mixture-of-Experts architecture and a reasoning-based model trained via reinforcement learning, respectively, in automating Solidity contract generation and testing, as well as identifying and fixing common vulnerabilities. We designed a controlled experimental framework to evaluate both models by generating and analysing a diverse set of smart contracts, including standardised tokens (ERC20, ERC721, ERC1155) and real-world application scenarios (Supply Chain, Token Exchange, Auction). The evaluation is grounded on a multidimensional metric suite covering quality, technical robustness and process characteristics. Vulnerability detection and patching capabilities are tested using predefined vulnerable contracts and guided patch prompts. The analysis spans six levels of prompt complexity and compares the impact of reasoning-based and non-reasoning-based generation strategies. Findings reveal that R1 delivers more accurate and optimised outputs under high complexity, while V3 performs more consistently in simpler tasks with simpler code structures. However, both models exhibit persistent hallucinations, limitations in vulnerability coverage, and inconsistencies due to prompt formulation. The correlation between re-evaluation patterns and output quality suggests that reasoning helps in complex scenarios, although excessive revisions may lead to over-engineered or unstable solutions. Neither model is robust enough to autonomously generate issue-free smart contracts in complex or security-critical scenarios, underscoring the need for human oversight. These findings highlight best practices for integrating LLMs into blockchain development workflows and emphasise the importance of aligning model selection with task complexity and security requirements.
J. Balamurugan, Devineni Poojitha, R Bindu, Archana Pallakonda · 8 authors
Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an innovative machine learning-driven solar energy trading platform on the Ethereum blockchain that uniquely integrates Bayesian-optimized XGBoost models with dynamic pricing mechanisms inherently incorporated within smart contracts. The principal innovation resides in the real-time amalgamation of meteorological data via Chainlink oracles with machine learning-enhanced price optimization, thereby establishing an adaptive system that autonomously responds to fluctuations in supply and demand. In contrast to existing static pricing methodologies, our framework introduces a multi-faceted dynamic pricing model that encompasses peak-hour adjustments, prediction confidence weighting, and weather-influenced corrections. The system dynamically establishes energy prices predicated on real-time supply–demand forecasts through the implementation of role-based access control, cryptographic hash functions, and ongoing integration of meteorological and machine learning data. Utilizing real-world meteorological data from La Trobe University’s UNISOLAR dataset, the Bayesian-optimized XGBoost model attains a remarkable prediction accuracy of 97.45% while facilitating low-latency price updates at 30 min intervals. The proposed system delivers robust transaction validation, secure offer creation, and scalable dynamic pricing through the seamless amalgamation of off-chain machine learning inference with on-chain smart contract execution, thereby providing a validated platform for trustless, real-time, and intelligent decentralized energy markets that effectively address the disparity between theoretical blockchain energy trading and practical implementation needs.
This study examines the design and deployment of scalable blockchain protocols that can serve as the backbone for smart city applications. The manuscript reviews existing blockchain consensus mechanisms—including Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Proof of Authority (PoA)—and evaluates their suitability for heterogeneous smart city ecosystems. It further explores emerging scalability approaches such as sharding, sidechains, directed acyclic graphs (DAGs), and layer-2 protocols, alongside hybrid models that incorporate AI-driven optimization. A comparative simulation-based methodology is employed, assessing throughput, latency, and energy consumption across multiple blockchain prototypes. Results demonstrate that modular hybrid architectures leveraging sharding and DAG structures can increase throughput by up to 400% compared to traditional blockchains, with latency reductions of over 90% and significant energy savings. Beyond technical findings, the study contextualizes blockchain scalability within broader smart city governance frameworks, addressing interoperability between diverse urban domains such as energy microgrids, healthcare data platforms, autonomous mobility systems, and decentralized citizen services. The implications for data privacy, regulatory compliance, and citizen trust are also highlighted, emphasizing the necessity of balancing decentralization with governance oversight. By synthesizing technical, social, and policy considerations, this work contributes a comprehensive roadmap for scalable blockchain adoption in smart cities. Ultimately, the research demonstrates that with careful architectural design and integration of scalability-enhancing techniques, blockchain can evolve from a niche financial tool into a universal urban infrastructure enabler. The findings not only advance blockchain scalability research but also provide actionable insights for policymakers, urban planners, and technologists seeking to design sustainable, citizen-focused smart cities.
Vladyslav Nekriach, Sidi Mohamed Beillahi, C. Li, Peilun Li · 7 authors
This paper introduces HEMVM, an innovative heterogeneous blockchain framework that seamlessly integrates diverse virtual machines (VMs), including the Ethereum Virtual Machine (EVM) and the Move Virtual Machine (MoveVM), into a unified system. This integration facilitates interoperability while retaining compatibility with existing Ethereum and Move toolchains by preserving high-level language constructs. HEMVM's unique cross-VM operations allow users to interact with contracts across various VMs using any wallet software, effectively resolving the fragmentation in user experience caused by differing VM designs. Our experimental results demonstrate that HEMVM is both fast and efficient, incurring minimal overhead (less than 4.4 %) for intra-VM transactions and achieving up to 9300 TPS for cross-VM transactions. Our results also show that the cross-VM operations in HEMVM are sufficiently expressive to support complex decentralized finance interactions across multiple VMs. Finally, the parallelized prototype of HEMVM shows performance improvements up to 44.8 % compared to the sequential version of HEMVM under workloads with mixed transaction types.
Abdullah Yousafzai, Muhammad Mohsan Sheeraz, Ganna Pogrebna, Jon Crowcroft · 5 authors
The metaverse is a shared virtual 3D space that combines immersive experiences with applications in gaming, social interactions, commerce, and more. It is rapidly becoming a reality, driven by advances in virtual reality, augmented reality, artificial intelligence, blockchain, and other emerging technologies. Among these, blockchain technology enables secure and decentralized ownership as well as seamless interoperability of virtual assets. Non-fungible tokens ensure verifiable ownership and fraud prevention, while smart contracts facilitate automated peer-to-peer transactions. Blockchain’s security and transparency promote trust and innovation, laying the foundation for a connected and user-driven metaverse ecosystem. In this paper, we explore the role of blockchain technology as a key enabler for the metaverse, providing solutions for decentralization, governance through decentralized autonomous organizations, interoperable mechanisms, digital asset ownership, traceability, auditing, and identity management. We present the key difference between traditional virtual worlds and the metaverse, and why blockchain is preferred over other decentralized technologies for the metaverse. We comprehensively review recent advances in metaverse system architectures, focusing on state-of-the-art solutions and lessons learned. We compare the existing literature based on key parameters; namely, contributions, advantages, limitations, and applications. We present key challenges, including deepfake threats, identity theft and brand infringement risks, mental health risks, digital safety and gambling risks, virtual world laws and regulations, and privacy and data security concerns. We outline future recommendations for enabling a sustainable and user-friendly metaverse ecosystem.
Shahad Altamimi, Qasem Abu Al‐Haija, Abdullah AlShuaibi
Incorporating BC technology in Internet of Things (IoT) networks allows secure peer-to-peer communication in Distributed Information Systems (DIS). This research analyzes the protocols implemented on the Blockchain (BC) about data integrity, authentication, and trust issues, which are core challenges of IoT. Thus, the study discussed a secure communication protocol based on BC. These protocols incorporate consensus and smart contracts, which remove central points of failure and improve transparency. Thus, the study setup simulates various IoT devices sending classified messages through a FastAPI backend integrated with Ethereum (Geth) via web3.py. The recent designs and blueprints show where they shine and where overhead or speed still bite. We aim to steer future work toward light, expandable, and privacy-first tools that lock down open IoT systems.
With the rapid expansion of the digital economy ecosystem, blockchain has become the core technology and theoretical path to support data interaction and trusted transactions. However, it still faces significant challenges in terms of privacy protection, transaction traceability and anti-attack capabilities. To address the above challenges, this study proposes a dynamic trust-aware blockchain security algorithm (DTBCSA) based on differential privacy and zero-knowledge proof. This algorithm is used to improve the system robustness and privacy protection capabilities in multiple scenarios. The core design of the algorithm includes: (1) introducing a dynamic trust evaluation mechanism. This mechanism dynamically adjusts the trust level of the node by analyzing its behavioral characteristics and historical interactions. At the same time, the secondary authentication mechanism is activated when the risk threshold is triggered; (2) embedding a differential privacy mechanism in the computing power transaction and model training process. This mechanism protects sensitive data and model parameters through Laplace noise; (3) using zero-knowledge proof to ensure the non-repudiation of transactions. At the same time, the aggregation and parallel optimization strategies are adopted to significantly reduce the computational overhead of proof and verification. In the experimental part, DTBCSA reduced the acceptance rate of malicious transactions from 70% of the baseline to 8% in the computing power trading market simulation. At the same time, it reduced the reputation distribution imbalance (Gini) from 0.42 to 0.20. In the federated learning collaborative training experiment, DTBCSA reduced the success rate of member inference attacks from 81% to 38% under the condition of privacy budget ε=1. While maintaining privacy protection, it improved the model accuracy by about 1.8 percentage points compared to FedDP. In addition, in the zero-knowledge proof verification performance test, DTBCSA reduced the proof size and generation time through aggregation optimization.
Ambi Rachel Alex, Syed Hassan Imam Gardezi, P S Krishnendu, P. Aruna · 5 authors
The rapid expansion of the Internet of Things (IoT) has led to an unprecedented rise in interconnected devices, generating vast volumes of sensitive data that demand robust security and trust mechanisms. Traditional centralized architectures often struggle to ensure integrity, privacy, and resilience against single points of failure, making them unsuitable for next-generation IoT ecosystems. This paper proposes a blockchain-enabled decentralized trust framework to strengthen the security, transparency, and reliability of IoT networks. By integrating distributed ledger technology with lightweight consensus protocols, the framework establishes immutable device identities, secure data exchange, and automated access control without dependence on centralized authorities. The proposed approach enhances interoperability among heterogeneous IoT devices while minimizing latency and computational overhead. Experimental evaluation and comparative analysis demonstrate that the blockchain-based trust model effectively mitigates common threats such as data tampering, spoofing, and unauthorized access, paving the way for a scalable and trustworthy foundation for future IoT applications.
Mahalinoro Razafimanjato, Malik Muhammad Saad, Dongkyun Kim
The Internet of Vehicles (IoV), a critical component of Intelligent Transportation Systems (ITS), enhances driving safety and traffic efficiency through real-time data exchange. However, the dynamic and heterogeneous nature of IoV introduces significant security and trust challenges. To address these, trust management systems have emerged as vital mechanisms to ensure the reliability and integrity of data exchanged between vehicles. Blockchain technology offers a robust framework for addressing security and trust issues in IoV environments. The decentralized, tamper-resistant, and transparent nature of the blockchain makes it suitable for complex vehicular environments. This survey provides an overview of state-of-the-art blockchain-based trust management systems in IoV. Following a systematic literature review that filtered 8,280 publications to 63 core studies from 2019 to 2024, we present a thematic classification of existing solutions, focusing on those employing public and private blockchains. Unlike previous surveys, our work focuses specifically on the intersection of blockchain and trust management systems in IoV by analyzing approaches across four dimensions: trust computation methods, such as game theory and AI-driven models; blockchain scaling solutions, including sharding, sidechains, and optimized consensus mechanisms; integration with emerging technologies such as 5G/6G, Digital Twins, and Federated Learning; and security and privacy mechanisms. Finally, this survey identifies current challenges and provides future research directions, highlighting the need for more scalable, adaptive, secure, and privacy-preserving trust management systems in IoV.
Dong‐Seong Kim, Esmot Ara Tuli, Igboanusi Ikechi Saviour, Md Mehedi Hasan Somrat · 5 authors
Technological advancements have fostered ubiquitous connectivity, driving the adoption of cloud computing and cloud-based services and applications for maintenance and management purposes. Consequently, cloud computing and cloud-based services have gained popularity for their scalability and efficiency in maintenance and management. However, such interconnected systems are inherently vulnerable to cybersecurity threats, including unauthorized access, data breaches, and others. Blockchain technology, initially popularized through cryptocurrencies, has proven to be a robust and reliable solution for various applications beyond financial transactions, such as supply chain management and secure data sharing. The integration of blockchain with cloud computing has given rise to the concept of “Blockchain as a Service” (BaaS), which provides developers with scalable and ready-to-use blockchain frameworks without the need for backend management. The current blockchain network has limitations such as low transaction per second (TPS), prolonged mining times, and other challenges that make it difficult for certain service-oriented applications. To address these issues, this paper introduces a hybrid on-off chain, edge-enabled BaaS solution called Pure Chain for service computing. Pure Chain refines the consensus algorithm, enhances the mining process, and enables seamless edge-supported online-offline hybrid transactions. Moreover, modified blockchain layers to improve efficiency and scalability. Pure Chain facilitates blockchain-enabled services and smart contract deployment in a cloud computing environment. Pure Chain optimizes energy consumption and delivers transaction speeds that are 12 times faster than conventional blockchain networks.
With the advancement of edge intelligence technology and the acceleration of urbanization, intelligent transportation systems (ITS) have experienced rapid development. Vehicle-road-cloud (VRC) collaboration was enabled through the coordinated sharing of vehicle-to-vehicle (V2V), vehicle-to-road (V2R), and vehicle-to-cloud (V2C) data in the Internet of vehicles, thereby constructing a more efficient cooperative intelligent transportation system (C-ITS). However, numerous security threats in VRC collaboration were found to severely impede the development of cooperative autonomous driving. The development status of VRC collaboration was first summarized, and the history of autonomous driving and the VRC-based autonomous driving environment were elaborated. Subsequently, attacks and security defense technologies in VRC collaboration were systematically categorized into two types: classical information security mechanisms and defense technologies, which were detailed from five aspects—information availability, integrity, confidentiality, authenticity, and non-repudiation; and machine learning-based security threats and defense technologies, which were analyzed from both centralized and distributed perspectives. Finally, future development directions and research priorities of VRC collaborative security technologies were forecasted, primarily covering federated learning, blockchain technology, secure multi-party computation, zero-knowledge proof, and differential privacy technology.
Rajasekaran P, M. Duraipandian, Johny Renoald Albert
The increasing rate of growth of the Internet of Things (IoT) in cloud-hospitality health has brought in data storage, transmission, and security challenges with the advent of quantum-enabled threats. Traditional compression methods struggle with computational inefficiency and the threat of invasion of privacy. This paper proposes a Quantum-Enhanced Zero-Knowledge Healthcare Compression Network for solving these challenges by combining Zero-Knowledge Proofs and Quantum-Inspired Deep Learning. The main goal is to provide privacy-preserving, efficient data compression along with optimizing computation costs and safeguarding sensitive healthcare records. Drawbacks in present cryptographic techniques, e.g., high computational costs in homomorphic encryption and scalability limitations in blockchain, require a novelty Adaptive Quantum-Assisted Zero-Knowledge Verification and Quantum Fusion-AutoCNN Encoder (QF-AutoCNN) to overcome this research. This work’s originality lies in combining Quantum zk-SNARKs, Hybrid Quantum Feature Encoding, and Reinforcement Learning-Based Challenge Optimization to provide better security, compression ratio, and verification efficiency. Experimental results show better accuracy (0.9816), improved F-measure (0.9709), and less computational overhead, better than other current methods such as convolutional neural networks-encryption and proxy re-encryption. This research greatly adds to safe cloud healthcare IoT by lessening privacy threats, maximizing storage space, and minimizing processing time, guaranteeing real-time handling of medical information.
Blockchain has moved from a cryptocurrency infrastructure to a coordination technology for modern communication systems. This review examines how blockchain is being embedded into next-generation communication environments, with particular attention to Internet of Things deployments, edge-cloud collaboration, cyber-physical infrastructures, security and privacy management, smart grids, vehicular networking, and emerging 5G/6G ecosystems. Following the logic of recent survey work on blockchain-enabled communications, the article synthesizes representative peer-reviewed studies, clarifies the blockchain mechanisms that matter for communication engineering, and organizes the literature around application layers rather than isolated protocols. The review shows that blockchain creates value when communication systems require shared trust, auditable automation, decentralized identity, incentive-compatible coordination, or tamper-resistant data exchange across organizational boundaries. At the same time, real deployment remains constrained by throughput, latency, storage overhead, interoperability, privacy leakage, governance complexity, and uneven energy efficiency across consensus designs. Building on both communication-network research and information-systems scholarship, the article develops an integrated analytical view of when blockchain genuinely improves communication architectures and when lighter coordination mechanisms are preferable. The paper concludes by identifying future directions around lightweight consensus, AI-native blockchain orchestration, cross-chain communication fabrics, privacy-preserving verification, and programmable trust for 6G and autonomous infrastructures.
Adam Zahir, Milan Groshev, Carlos J. Bernardos, Antonio de la Oliva
Edge computingbrings computation near end users, enabling the provisioning of novel use cases. To satisfy end-user requirements, the concept ofedge federationhas recently emerged as a key mechanism for dynamic resources and services sharing across edge systems managed by different administrative domains. However, existing federation solutions often rely on pre-established agreements and face significant limitations, including operational complexity, delays caused by manual operations, high overhead costs, and dependence on trusted third parties. In this context, Distributed Ledger Technologies (DLTs) such asblockchaincan create dynamic federation agreements that enable service providers to securely interact and share services without prior trust. This article first describes the problem of edge federation, using the standardized ETSImulti-access edge computing (MEC)framework as a reference architecture, and how it is being addressed. Then, it proposes a novel solution usingblockchainandsmart contractsto enable distributed MEC systems to dynamically negotiate and execute federation in a secure, automated, and scalable manner. We validate our framework’s feasibility through a performance evaluation using a private Ethereum blockchain, built on the open-source Hyperledger Besu platform. The testbed includes a large number of MEC systems and compares two blockchain consensus algorithms. Experimental results demonstrate that our solution automates the entire federation lifecycle-from negotiation to deployment–with a quantifiable overhead, achieving federation in approximately 18 seconds in a baseline scenario. The framework scales efficiently in concurrent request scenarios, where multiple MEC systems initiate federation requests simultaneously. This approach provides a promising direction for addressing the complexities of dynamic, multi-domain federations across the edge-to-cloud continuum.