Search Results (209)

Natural disasters often strike unexpectedly, leaving thousands of victims and affected individuals each year. Effective disaster preparedness is critical to reducing these consequences and accelerating recovery. This paper presents Eghatha, a blockchain-based decentralized system designed to optimize humanitarian aid delivery during crises. By enabling secure and transparent transfers of donations and relief from donors to beneficiaries, the system enhances trust and operational efficiency. All transactions are immutably recorded and verified on a blockchain network, reducing fraud and misuse while adapting to local contexts. The platform is volunteer-driven, coordinated by civil society organizations with humanitarian expertise, and supported by government agencies involved in disaster response. Eghatha’s design accounts for disaster-related constraints—including limited mobility, varying levels of technological literacy, and resource accessibility—by offering a user-friendly interface, support for local currencies, and integration with locally available technologies. These elements ensure inclusivity for diverse populations. Aligned with Morocco’s “Digital Morocco 2030” strategy, the system contributes to both immediate crisis response and long-term digital transformation. Its scalable architecture and contextual sensitivity position the platform for broader adoption in similarly affected regions worldwide, offering a practical model for ethical, decentralized, and resilient humanitarian logistics. Full article

As intelligent transportation systems (ITSs) evolve rapidly, the increasing computational demands of connected vehicles call for efficient task offloading. Centralized approaches face challenges in scalability, security, and adaptability to dynamic network conditions. To address these issues, we propose a blockchain-based decentralized task offloading framework with network-aware resource allocation and tokenized economic incentives. In our model, vehicles generate computational tasks that are dynamically mapped to available computing nodes—including vehicle-to-vehicle (V2V) resources, roadside edge servers (RSUs), and cloud data centers—based on a multi-factor score considering computational power, bandwidth, latency, and probabilistic packet loss. A blockchain transaction layer ensures auditable and secure task assignment, while a proof-of-stake (PoS) consensus and smart-contract-driven dynamic pricing jointly incentivize participation and balance workloads to minimize delay. In extensive simulations reflecting realistic ITS dynamics, our approach reduces total completion time by 12.5–24.3%, achieves a task success rate of 84.2–88.5%, improves average resource utilization to 88.9–92.7%, and sustains >480 transactions per second (TPS) with a 10 s block interval, outperforming centralized/cloud-based baselines. These results indicate that integrating blockchain incentives with network-aware offloading yields secure, scalable, and efficient management of computational resources for future ITSs. Full article

The convergence of decentralized artificial intelligence (DAI), blockchain technology, and smart contracts is reshaping the design and governance of intelligent systems. As these technologies rapidly evolve, addressing privacy within their architecture, usage models, and associated risks has become increasingly critical. This systematic literature review examines architectural patterns, governance frameworks, real-world applications, and persistent challenges in DAI systems. It identifies prevailing designs such as federated learning integrated with consensus protocols, smart contract-based incentive mechanisms, and decentralized verification methods. Drawing from a diverse body of recent literature, the review highlights implementations across sectors, including healthcare, finance, IoT, autonomous systems, and intelligent infrastructure, each demonstrating significant contributions to privacy, security, and collaborative innovation. Despite these advancements, DAI systems face ongoing obstacles such as scalability limitations, privacy trade-offs, and difficulties with regulatory compliance. The review emphasizes the need for integrative governance approaches that balance transparency, accountability, incentive alignment, and ethical oversight. These elements are proposed as co-evolving pillars essential to establishing trustworthiness in decentralized AI ecosystems. This work offers a comprehensive review for understanding the current landscape and guiding the development of responsible and effective DAI systems in the Web3 era. Full article

This paper introduces a novel approach to tree modeling architecture integrated with blockchain technology, aimed at enhancing landscape spatial planning and forest monitoring systems. The primary objective is to develop a low-cost, automated tree CAD modeling methodology combined with blockchain functionalities to support smart forest projects and collaborative design processes. The proposed method utilizes a parametric tree CAD model consisting of four 2D tree-frames with a 45° division angle, enriched with recorded tree-leaves’ texture and color. An “AI Text-by-Voice CAD Programming” technique is employed to create tangible tree-model NFT tokens, forming the basis of a thematic “Internet-of-Trees” blockchain. The main results demonstrate the effectiveness of the blockchain/Merkle hash tree in tracking tree geometry growth and texture changes through parametric transactions, enabling decentralized design, data validation, and planning intelligence. Comparative analysis highlights the advantages in cost, time efficiency, and flexibility over traditional 3D modeling techniques, while providing acceptable accuracy for metaverse projects in smart forests and landscape architecture. Core contributions include the integration of AI-based user voice interaction with blockchain and behavioral data for distributed and collaborative tree modeling, the introduction of a scalable and secure “Merkle hash tree” for smart forest monitoring, and the facilitation of fintech adoption in environmental projects. This framework offers significant potential for advancing metaverse-based landscape architecture, smart forest surveillance, sustainable urban planning, and the improvement of citizen involvement in sustainable forestry paving the way for a greener future. Full article

The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents—trained using centralized training with decentralized execution (CTDE)—handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems. Full article

Education 4.0 promotes active, personalized, and competency-based learning aligned with the Sustainable Development Goals (SDGs), yet most current platforms rely on centralized architectures that restrict access, agency, and adaptability. To address this problem, Web3 technologies—including blockchain, decentralized identifiers (DIDs), peer-to-peer storage, and smart contracts—enable the creation of platforms that uphold equity, data sovereignty, and pedagogical flexibility. This paper investigates how the convergence of Education 4.0 and Web3 technologies can drive the development of sustainable, inclusive, and learner-centered digital education systems. We examine two decentralized education platforms, EtherLearn and DeLMS, to assess their design affordances and limitations. Building on these insights, we propose a layered architectural framework grounded in sustainability principles. Our analysis shows that decentralized infrastructures can expand access in underserved regions, increase credential portability, empower learners with greater autonomy, and foster participatory governance through decentralized voting, token-based incentives, and community moderation. Despite these advantages, significant challenges remain around usability, energy efficiency, and regulatory compliance. We conclude by identifying key research priorities at the intersection of sustainable educational technology, digital equity, and decentralized system design. 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

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

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

The collaborative construction of large-scale, diverse datasets is crucial for developing high-performance machine learning models. However, this collaboration faces significant challenges, including ensuring data security, protecting participant privacy, maintaining high dataset quality, and aligning economic incentives among multiple stakeholders. Effective risk management strategies are essential to systematically identify, assess, and mitigate potential risks associated with data collaboration. This study proposes a federated blockchain-based framework designed to manage multiparty dataset collaborations securely and transparently, explicitly incorporating comprehensive risk management practices. The proposed framework involves six core entities—key distribution center (KDC), researcher (RA), data owner (DO), consortium blockchain, dataset evaluation platform, and the orchestrating model itself—to ensure secure, privacy-preserving and high-quality dataset collaboration. In addition, the framework uses blockchain technology to guarantee the traceability and immutability of data transactions, integrating token-based incentives to encourage data contributors to provide high-quality datasets. To systematically mitigate dataset quality risks, we introduced an innovative categorical dataset quality assessment method leveraging label reordering to robustly evaluate datasets. We validated this quality assessment approach using both publicly available (UCI) and privately constructed datasets. Furthermore, our research implemented the proposed blockchain-based management system within a consortium blockchain infrastructure, benchmarking its performance against existing methods to demonstrate enhanced security, reliability, risk mitigation effectiveness, and incentive alignment in dataset collaboration. Full article

The blockchain technology is transforming the mining industry by enabling mineral reserve tokenization, improving security, transparency, and traceability. However, controlling access to sensitive mining data remains a challenge. Existing access control models, such as role-based access control, are too rigid because they assign permissions based on predefined roles rather than real-world conditions like mining licenses, regulatory approvals, or investment status. To address this, this paper explores an attribute-based access control model for blockchain-based mineral tokenization systems. ABAC allows access permissions to be granted dynamically based on multiple attributes rather than fixed roles, making it more adaptable to the mining industry. This paper presents a high-level system design that integrates ABAC with the blockchain using smart contracts to manage access policies and ensure compliance. The proposed model is designed for permissioned blockchain platforms, where access control decisions can be automated and securely recorded. A comparative analysis between ABAC and RBAC highlights how ABAC provides greater flexibility, security, and privacy for mining operations. By introducing ABAC in blockchain-based mineral reserve tokenization, this paper contributes to a more efficient and secure way of managing data access in the mining industry, ensuring that only authorized stakeholders can interact with tokenized mineral assets. Full article

Honey is globally recognized for its substantial nutritional and therapeutic properties. However, its high market value makes it susceptible to counterfeiting, negatively impacting consumers and beekeepers. This paper presents a blockchain-based framework to monitor the honey trade supply chain, ensuring authenticity. The framework employs an oracle component to verify honey quality and origin using IoT data. Additionally, it integrates fungible and non-fungible tokens to track honey batches. The study evaluates the economic feasibility of this approach, demonstrating that the cost of performing a trade is less than USD 1, with the oracle component achieving an average accuracy rate of 90% in detecting falsified sensor data. Full article

Secure Digital Evidence Management Systems (DEMSs) ae crucial for law enforcement agencies, because traditional systems are prone to tampering and unauthorised access. Blockchain technology, particularly private blockchains, offers a solution by providing a centralised and tamper-proof system. This study proposes a private blockchain using Proof of Work (PoW) to securely manage digital evidence. Miners are assigned specific nonce ranges to accelerate the mining process, called collaborative mining, to enhance the scalability challenges in DEMSs. Transaction data includes digital evidence to generate a Non-Fungible Token (NFT). Miners use NFTs to solve the puzzle according to the assigned difficulty level d, so as to generate a hash using SHA-256 and add it to the ledger. Users can verify the integrity and authenticity of records by re-generating the hash and comparing it with the one stored in the ledger. Our results show that the data was verified with 100% precision. The mining time was $2.5$ s, and the nonce iterations were as high as $80\times {10}^3$ for $d=5$. This approach improves the scalability and integrity of digital evidence management by reducing the overall mining time. Full article

As climate change concerns, urban congestion, and environmental degradation intensify, cities prioritise cycling as a sustainable transport option to reduce CO₂ emissions and improve quality of life. However, rampant bicycle theft and poor security infrastructure often deter daily commuters and tourists from cycling. This study explores how advanced security measures can bolster sustainable urban mobility and tourism by addressing these challenges. A mixed-methods approach is utilised, incorporating primary survey data from Slovenia and secondary data on bicycle sales, imports and thefts from 2015 to 2024. Findings indicate that access to secure parking substantially enhances users’ sense of safety when commuting by bike. Regression analysis shows that for every 1000 additional bicycles sold, approximately 280 more thefts occur—equivalent to a 0.28 rise in reported thefts—highlighting a systemic vulnerability associated with sustainability-oriented behaviour. To bridge this gap, the study advocates for an innovative security framework that combines blockchain technology and Non-Fungible Tokens (NFTs) with encrypted Quick Response (QR) codes. Each bicycle would receive a tamper-proof QR code connected to a blockchain-verified NFT documenting ownership and usage data. This system facilitates real-time authentication, enhances traceability, deters theft, and builds trust in cycling as a dependable transport alternative. The proposed solution merges sustainable transport, digital identity, and urban security, presenting a scalable model for individual users and shared mobility systems. Full article

Prior research has focused on the technical and institutional challenges of blockchain adoption. However, little is known about how blockchain ventures claim categorical space in the market and how such domain positioning influences their visibility and evaluation. This study investigates the relationship between strategic domain positioning and market recognition among blockchain-based ventures, with a particular focus on applications relevant to e-commerce, such as non-fungible tokens (NFTs) and decentralized finance (DeFi). Drawing on research on categorization, legitimacy, and the technology lifecycle, we propose a domain lifecycle perspective that accounts for the evolving expectations and legitimacy criteria across blockchain domains. Using BERTopic, a transformer-based topic modeling method, we classify 9665 blockchain ventures based on their textual business descriptions. We then test the impact of domain positioning on market recognition—proxied by Crunchbase rank—while examining the moderating effects of external validation signals such as funding events, media attention, and organizational age. Our findings reveal that clear domain positioning significantly enhances market recognition, but the strength and direction of this effect vary by domain. Specifically, NFT ventures experience stronger recognition when young and less institutionally validated, suggesting a novelty premium, while DeFi ventures benefit more from conventional legitimacy signals. These results advance our understanding of how categorical dynamics operate in emerging digital ecosystems and offer practical insights for e-commerce platforms, investors, and entrepreneurs navigating blockchain-enabled innovation. Full article