Search Results (1,243)

Blockchain sharding has emerged as a promising solution to address scalability and performance challenges in distributed ledger systems. In the sharded blockchain, yanking can reduce the communication overhead of smart contracts between shards. However, the existing smart contract yanking methods are inefficient, increasing the latency and reducing the throughput. In this paper, we propose a novel DRL-Based Cross-Shard Smart Contract Yanking (DCSCY) framework which intelligently balances three critical factors: the number of smart contracts processed, node waiting time, and yanking costs. The proposed framework dynamically optimizes the relocation trajectory of smart contracts across shards. This reduces the communication overhead and enables adaptive, function-level migrations to enhance the execution efficiency. The experimental results demonstrate that the proposed approach reduces the cross-shard transaction latency and enhances smart contract utilization. Compared to random-based and order-based methods, the DCSCY approach achieves a performance improvement of more than 95%. Full article

Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products. Full article

This paper proposes and evaluates a novel real-time cybersecurity framework integrating artificial intelligence (AI) and blockchain technology to enhance the detection and auditability of cyber threats. Traditional cybersecurity approaches often lack transparency and robustness in logging and verifying AI-generated decisions, hindering forensic investigations and regulatory compliance. To address these challenges, we developed an integrated solution combining a convolutional neural network (CNN)-based anomaly detection module with a permissioned Ethereum blockchain to securely log and immutably store AI-generated alerts and relevant metadata. The proposed system employs smart contracts to automatically validate AI alerts and ensure data integrity and transparency, significantly enhancing auditability and forensic analysis capabilities. To rigorously test and validate our solution, we conducted comprehensive experiments using the CICIDS2017 dataset and evaluated the system’s detection accuracy, precision, recall, and real-time responsiveness. Additionally, we performed penetration testing and security assessments to verify system resilience against common cybersecurity threats. Results demonstrate that our AI-blockchain integrated solution achieves superior detection performance while ensuring real-time logging, transparency, and auditability. The integration significantly strengthens system robustness, reduces false positives, and provides clear benefits for cybersecurity management, especially in regulated environments. This paper concludes by outlining potential avenues for future research, particularly extending blockchain scalability, privacy enhancements, and optimizing performance for high-throughput cybersecurity applications. Full article

Smart contracts have become integral to decentralized applications, yet their programmability introduces critical security risks, exemplified by high-profile exploits such as the DAO and Parity Wallet incidents. Existing vulnerability detection methods, including static and dynamic analysis, as well as machine learning-based approaches, often struggle with emerging threats and rely heavily on large, labeled datasets. This study investigates the effectiveness of open-source, lightweight large language models (LLMs) fine-tuned using parameter-efficient techniques, including Quantized Low-Rank Adaptation (QLoRA), for smart contract vulnerability detection. We introduce the EVuLLM dataset to address the scarcity of diverse evaluation resources and demonstrate that our fine-tuned models achieve up to 94.78% accuracy, surpassing the performance of larger proprietary models, while significantly reducing computational requirements. Moreover, we emphasize the advantages of lightweight models deployable on local hardware, such as enhanced data privacy, reduced reliance on internet connectivity, lower infrastructure costs, and improved control over model behavior, factors that are especially critical in security-sensitive blockchain applications. We also explore Retrieval-Augmented Generation (RAG) as a complementary strategy, achieving competitive results with minimal training. Our findings highlight the practicality of using locally hosted LLMs for secure, efficient, and reproducible smart contract analysis, paving the way for broader adoption of AI-driven security in blockchain ecosystems. Full article

With the rapid development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) is emerging as a new network architecture for the modern Internet. SDN separates the control plane from the data plane, allowing a central controller, the SDN controller, to quickly direct the routing devices within the topology to forward data packets, thus providing flexible traffic management for communication between information sources. However, traditional Distributed Denial of Service (DDoS) attacks still significantly impact SDN systems. This paper proposes a novel dual-layer strategy capable of detecting and mitigating DDoS attacks in an SDN network environment. The first layer of the strategy enhances security by using blockchain technology to replace the SDN flow table storage container in the northbound interface of the SDN controller. Smart contracts are then used to process the stored flow table information. We employ the time window algorithm and the token bucket algorithm to construct the first layer strategy to defend against obvious DDoS attacks. To detect and mitigate less obvious DDoS attacks, we design a second-layer strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm from unsupervised learning techniques to perform binary classification, thereby identifying abnormal traffic. We conduct experimental validation using the publicly available DDoS dataset CIC-DDoS2019. The results show that using this strategy in the SDN network reduces the average deviation of round-trip time (RTT) by approximately 38.86% compared with the original SDN network without this strategy. Furthermore, the accuracy of DDoS attack detection reaches 97.66% and an F1 score of 92.2%. Compared with other similar methods, under comparable detection accuracy, the deployment of our strategy in small-scale SDN network topologies provides faster detection speeds for DDoS attacks and exhibits less fluctuation in detection time. This indicates that implementing this strategy can effectively identify DDoS attacks without affecting the stability of data transmission in the SDN network environment. Full article

In this paper, I propose Yul2Vec, a novel method for representing Yul programs as distributed embeddings in continuous space. Yul serves as an intermediate language between Solidity and Ethereum Virtual Machine (EVM) bytecode, designed to enable more efficient optimization of smart contract execution compared to direct Solidity-to-bytecode compilation. The vectorization of a program is achieved by aggregating the embeddings of its constituent code elements from the bottom to the top of the program structure. The representation of the smallest construction units, known as opcodes (operation codes), along with their types and arguments, is generated using knowledge graph relationships to construct a seed vocabulary, which forms the foundation for this approach. This research is important for enabling future enhancements to the Solidity compiler, paving the way for advanced optimizations of Yul and, consequently, EVM code. Optimizing the EVM bytecode is essential not only for improving performance but also for minimizing the operational costs of smart contracts—a key concern for decentralized applications. By introducing Yul2Vec, this paper aims to provide a foundation for further research into compiler optimization techniques and cost-efficient smart contract execution on Ethereum. The proposed method is not only fast in learning embeddings but also efficient in calculating the final vector representation of Yul code, making it feasible to integrate this step into the future compilation process of Solidity-based smart contracts. Full article

This paper investigates the integration of Internet of things (IoT) technology with blockchain to enhance transparency, accountability, and operational efficiency in smart contract execution for IoT ecosystems. The proposed approach extends the Three-Phase Methodology (TpM) by introducing an innovative entity, the IoT Operator, which acts as a custody caretaker, contract enforcer, and mediator. By leveraging blockchain’s secure and immutable ledger, the IoT Operator ensures the reliable monitoring and governance of IoT applications. A PoC implementation conducted at the Eldorado Research Institute demonstrates the methodology’s effectiveness, realizing a significant reduction of 95.83% in equipment search time. This work highlights the practical advantages of integrating blockchain and IoT within a structured framework, emphasizing the need for tailored, application-specific solutions rather than generic decentralization. The findings offer actionable guidelines for implementing blockchain in IoT systems, paving the way for more secure, efficient, and resilient IoT applications. Full article

In the context of the information age, a land administration system must be technologically driven to manage land information and data transparently. This ensures the registration and protection of land rights for people. In this study, we present a Blockchain Land Registration system designed as a tool for enhancing land administration in South Saharan Africa (SSA). Drawing inspiration from Namibia, we have developed a user interface comprising a homepage/landing page, a users’ registration form, a login form that incorporates MetaMask authentication prompts, and an authenticated dashboard for landowners and purchasers. Design Science was employed as the methodology for this proposal. Being technical design research for solving a land administration problem (that of inefficient land registration), the technical solution identified involves system design, the development of blockchain integration and testing, and development aspects. Based on this approach, blockchain was conceptualised as an “artefact” that could be investigated as a technical solution to address the challenges posed by inefficient land registration. This study provides a comprehensive roadmap for the conceptualisation, development, validation, and deployment of a blockchain-based land titles registry suitable for SSA countries. It also explores a discussion on the practical and policy implications of blockchain in land administration in SSA countries. Full article

Smart contracts, as a critical application of blockchain technology, significantly enhance its programmability and scalability, offering broad application prospects. However, frequent security incidents have resulted in substantial economic losses and diminished user trust, making security issues a key challenge for further development. Since smart contracts cannot be modified after deployment, flaws in their design or implementation may lead to severe consequences. Therefore, rigorous pre-deployment verification of their correctness is particularly crucial. This paper explores the symmetry in control flows and state transitions of Solidity smart contracts and leverages this inherent structural symmetry to develop a normalized state transition model based on a finite state machine. The FSM model is subsequently formalized into a Promela model with the Spin model checker. By integrating manually defined Linear Temporal Logic formulas with those generated by Smart Pulse, the Promela model is formally verified in Spin to ensure the correctness and security of smart contracts. This approach establishes a systematic verification framework, providing effective support to enhance the reliability and security of smart contracts. Full article

This study presents SmartMix Web3, a framework combining ensemble machine learning and blockchain technology to optimize low-carbon concrete design. It addresses two key challenges: (1) the limitations of conventional models in predicting concrete performance, and (2) ensuring data reliability and overcoming collaboration issues in AI-driven sustainable construction. Validated with 61 real-world experiments in Cameroon and 752 mix designs, the framework shows major improvements in predictive accuracy and decentralized trust. To address the first research question, a stacked ensemble model comprising Extreme Gradient Boosting (XGBoost)–Random Forest and a Convolutional Neural Network (CNN) was developed, achieving a 22% reduction in Root Mean Square Error (RMSE) for compressive strength prediction and embodied carbon estimation compared to traditional methods. The 29% reduction in Mean Absolute Error (MAE) results confirms the superiority of Extreme Learning Machine (EML) in low-carbon concrete performance prediction. For the second research question, SmartMix Web3 employs blockchain to ensure tamper-proof traceability and promote collaboration. Deployed on Ethereum, it automates verification of tokenized Environmental Product Declarations via smart contracts, reducing disputes and preserving data integrity. Federated learning supports decentralized training across nine batching plants, with Secure Hash Algorithm (SHA)-256 checks ensuring privacy. Field implementation in Cameroon yielded annual cost savings of FCFA 24.3 million and a 99.87 kgCO₂/m³ reduction per mix design. By uniting EML precision with blockchain transparency, SmartMix Web3 offers practical and scalable benefits for sustainable construction in developing economies. Full article

The goals of the European Union towards the energy transition imply profound changes in the energy field, so as to promote sustainable energy development while fostering economic growth. To achieve these changes, the incorporation of sustainable technologies supporting decentralisation, energy efficiency, renewable energy production, and demand flexibility is of vital importance. Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. In this context, the scope of this paper is to examine the opportunities and the risks that emerge from the adoption of blockchain in four innovative business models, while also identifying mitigation strategies to support and accelerate the energy transition, thus proposing optimal approaches of exploitation of blockchain in energy services. The business models concern Energy Performance Contracting with P4P guarantees, improved self-consumption in energy cooperatives, energy efficiency and flexibility services for natural gas boilers, and smart energy management for EV chargers and HVAC appliances. Firstly, the value proposition of the business models is analysed and results in a comprehensive SWOT analysis. Based on the findings of the analysis and consultations with relevant market actors, in combination with the examination of the relevant literature, risks are identified and evaluated through a qualitative assessment approach. Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. Furthermore, according to the results, technological and legal risks are the most significant, followed by political, economic, and social risks, while environmental risks of blockchain integration are not as important. Strategies to address risks relevant to blockchain exploitation include ensuring policy alignment, emphasising economic feasibility, facilitating social inclusion, prioritising security and interoperability, consulting with legal experts, and using consensus algorithms with low energy consumption. The findings offer clear guidance for energy service providers, policymakers, and technology developers, assisting in the design, deployment, and risk mitigation of blockchain-enabled business models to accelerate sustainable energy development. Full article

This study explores the transformative potential of blockchain technology in overhauling conventional housing rental systems. It specifically addresses persistent issues, such as information asymmetry, fraudulent listings, weak Rental Agreements, and data breaches. A comprehensive review of ten academic publications highlights the architectural frameworks, underlying technologies, and myriad benefits of decentralized rental platforms. The intrinsic characteristics of blockchain—immutability, transparency, and decentralization—are pivotal in enhancing the credibility of rental information and proactively preventing fraudulent activities. Smart contracts emerge as a key innovation, enabling the automated execution of Rental Agreements, thereby significantly boosting efficiency and minimizing reliance on intermediaries. Furthermore, Decentralized Identity (DID) solutions offer a robust mechanism for securely managing identities, effectively mitigating risks associated with data leakage, and fostering a more trustworthy environment. The suitability of platforms such as Hyperledger Fabric for developing such sophisticated rental systems is also critically evaluated. Blockchain-based systems promise to dramatically increase market transparency, bolster transaction security, and enhance fraud prevention. They also offer streamlined processes for dispute resolution. Despite these significant advantages, the widespread adoption of blockchain in the rental sector faces several challenges. These include inherent technological complexity, adoption barriers, the need for extensive legal and regulatory adaptation, and critical privacy concerns (e.g., ensuring compliance with GDPR). Furthermore, blockchain scalability limitations and the intricate balance between data immutability and the necessity for occasional data corrections present considerable hurdles. Future research should focus on developing user-friendly DID solutions, enhancing blockchain performance and cost-efficiency, strengthening smart contract security, optimizing the overall user experience, and exploring seamless integration with emerging technologies. While current challenges are undeniable, blockchain technology offers a powerful suite of tools for fundamentally improving the rental market’s efficiency, transparency, and security, exhibiting significant potential to reshape the entire rental ecosystem. Full article

The increasing reliance on cloud services demands advanced security mechanisms to protect sensitive data and ensure robust access control. This study addresses critical challenges in cloud security by proposing a novel framework that integrates blockchain-based smart contracts to enhance authorization and authentication processes. Smart contracts, as self-executing agreements embedded with predefined rules, enable decentralized, transparent, and tamper-proof mechanisms for managing access control in cloud environments. The proposed system mitigates prevalent threats such as unauthorized access, data breaches, and identity theft through an immutable and auditable security framework. A prototype system, developed using Ethereum blockchain and Solidity programming, demonstrates the feasibility and effectiveness of the approach. Rigorous evaluations reveal significant improvements in key metrics: security, with a 0% success rate for unauthorized access attempts; scalability, maintaining low response times for up to 100 concurrent users; and usability, with an average user satisfaction rating of 4.4 out of 5. These findings establish the efficacy of smart contract-based solutions in addressing critical vulnerabilities in cloud services while maintaining operational efficiency. The study underscores the transformative potential of blockchain and smart contracts in revolutionizing cloud security practices. Future research will focus on optimizing the system’s scalability for higher user loads and integrating advanced features such as adaptive authentication and anomaly detection for enhanced resilience across diverse cloud platforms. Full article

This paper presents a novel digital identity ecosystem built upon a hierarchical structure of Blockchain tokens, where both government-certified and uncertified tokens can coexist to represent various attributes of an individual’s identity. At the core of this system is the government, which functions as a trusted authority capable of creating entities and issuing a unique, non-replicable digital identity token for each one. Entities are the exclusive owners of their identity tokens and can attach additional tokens—such as those issued by the government, educational institutions, or financial entities—to form a verifiable, token-based digital identity tree. This model accommodates a flexible identity framework that enables decentralized yet accountable identity construction. Our contributions include the design of a digital identity system (supported by smart contracts) that enforces uniqueness through state-issued identity tokens while supporting user-driven identity formation. The model differentiates between user types and certifies tokens according to their source, enabling a scalable and extensible structure. We also analyze the economic, technical, and social feasibility of deploying this system, including a breakdown of transaction costs for key stakeholders such as governments, end-users, and institutions like universities. Considering the benefits of blockchain, implementing a digital identity ecosystem in this technology is economically viable for all involved stakeholders. Full article

Watermarking protocols represent a possible solution to the problem of digital copyright protection of content distributed on the Internet. Their implementations, however, continue to be a complex problem due to the difficulties researchers encounter in proposing secure, easy-to-use and, at the same time, “trusted third parties” (TTPs)-free solutions. In this regard, implementations based on blockchain and smart contracts are among the most advanced and promising, even if they are affected by problems regarding the performance and privacy of the information exchanged and processed by smart contracts and managed by blockchains. This paper presents a watermarking protocol implemented by smart contracts and blockchain. The protocol uses a “layer-2” blockchain execution model and performs the computation in “trusted execution environments” (TEEs). Therefore, its implementation can guarantee efficient and confidential execution without compromising ease of use or resorting to TTPs. The protocol and its implementation can, thus, be considered a valid answer to the “trilemma” that afflicts the use of blockchains, managing to guarantee decentralization, security, and scalability. Full article