Purpose This conceptual paper aims to adopt the marketing perspective of tokenomics to explore the role of decentralised autonomous organisations (DAOs) as an emerging organisational form enabled by blockchain technology, through a dynamic conceptual framework and six specific propositions, across three lifecycle stages (Tokenisation, Platformisation and Adaptation) of DAOs. Design/methodology/approach This paper draws upon the blockchain institutional theory, attention-based marketing theory, community marketing, adaptive marketing and business-to-business (B2B) marketing principles to develop its conceptual framework and propositions. Findings This paper offers six specific propositions: (1) DAOs leveraging attention-based tokenisation increase perceived value; (2) Community-oriented Platformisation boosts user engagement and retention; (3) Strong adaptive marketing capabilities lead to viability innovation; (4) Engaging in protocol sharing fosters robust DAO-to-DAO (D2D) relationships; (5) Community marketing contributes to brand awareness and partnership opportunities; and (6) Stakeholder-centric governance mechanisms enhance resilience during disruptions. Research limitations/implications This paper uses an extensive literature review to develop a theory-based conceptual framework and six propositions to provide useful directions for future marketing research on DAOs. However, these propositions need to be empirically tested using quantitative data. Practical implications The six propositions have important implications for managers to develop appropriate marketing strategies and governance structures to foster collaborative value creation within the marketing landscape, to leverage the unique advantages offered by the emerging DAO ecosystem. Originality/value This paper extends B2B marketing principles to the Tokenomics context, to posit that cooperation-based partnerships among DAOs would transform current marketing models via protocol exchange and community advocacy, using Tokens and non-fungible tokens.
Predictive maintenance in cross-border unmanned logistics systems (CBULS) faces persistent challenges, including data privacy, system heterogeneity, and collaborative efficiency. Existing studies that combine federated learning with blockchain address only partial aspects—such as communication or trust—but fail to effectively handle non-independent and identically distributed (non-IID) data, integrate multi-layer privacy, or design consensus mechanisms tailored to cross-border logistics. This paper proposes a predictive maintenance framework that integrates an improved FedProx algorithm with a hybrid Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) consensus. The framework incorporates zero-knowledge proofs, fully homomorphic encryption, and local differential privacy, while employing hierarchical architecture and sharding for scalability. Simulation results show that the proposed method improves prediction accuracy by 6.9% compared with FedAvg and 3.7% compared with FedProx, enhances privacy protection by over 12%, increases system throughput by approximately 23%, and reduces transaction confirmation latency by nearly 18%. These results demonstrate that the framework provides a secure, efficient, and scalable solution for predictive maintenance in CBULS.
With the widespread application of smart contracts and the expansion of asset management scale, various new attacks continue to emerge, and a method that can adapt to new vulnerabilities more quickly is urgently needed. Although deep learning methods have shown superior performance in vulnerability detection, their dependence on a large number of labeled samples limits their applicability in new vulnerability scenarios. Therefore, this article introduces a dual adversarial domain adaptation (DADA) approach. This approach consists of two generators and two discriminators. First, the source generator is pretrained with extensive labeled known vulnerability samples in the source domain to extract discriminative features, and its parameters are shared with the target generator. Subsequently, the features of the source and target domain samples are input into the source discriminator, and the target generator is guided to learn domain-invariant features through adversarial training; at the same time, the target domain discriminator is introduced to further weaken its dependence on the distribution of source domain features, thereby improving its adaptability to new vulnerabilities. We conducted experimental evaluations on public datasets, and the results show that our proposed method outperforms six mainstream deep learning-based detection methods. We applied domain adaptation methods to smart contract vulnerability detection for the first time, providing a reference for small sample learning in this field in the future.
G Jayashree, M. Anitha, R Chethana, Prateek Prasanna Savanur
Blockchain technology has emerged as a revolutionary force in the digital era, redefining transactional security and data integrity. This paper presents a blockchain-based solution to enhance the security and transparency of crowdfunding platforms using Ethereum smart contracts. Traditional crowdfunding platforms are marred by security vulnerabilities, lack of transparency, high transaction fees, and reliance on intermediaries. Our proposed system leverages Ethereum's decentralized architecture, smart contracts, and Polygon network scalability, coupled with the Interplanetary File System (IPFS) for decentralized storage. The platform automates fund disbursement based on milestone verification, mitigating fraud risks and enabling global participation. Performance evaluation demonstrates the system's efficiency, with transaction costs reduced by 30% and milestone-based fund disbursement achieving 98% accuracy compared to traditional systems.
Social media platforms rely heavily on user interaction data to personalize content and advertisements, raising concerns regarding user privacy and data misuse. Although regulations such as the General Data Protection Regulation (GDPR) aim to address these concerns, enforcement remains under the control of the platforms themselves. To address these issues, this paper proposes a privacy-preserving recommender system that minimizes personal preference data exposure while maintaining the potential for equivalent personalization accuracy (including for advertisements) through local data processing, which could access the same amount or more of pure user data than server-side models. This system integrates a lightweight client-based machine learning model to infer user preferences locally, combined with Merkle tree-based Zero-Knowledge Proof (ZKP) scheme to anonymously authenticate user requests. The authors develop a working web app prototype and evaluate performance across a range of user devices. Results show minimal latency for the client-based model (under 5 milliseconds on most devices) and diverse proof generation times, ranging from 2.6 seconds to over 18 seconds, depending on hardware capability. Server-side verification remains consistent and fast under 250 milliseconds. Although proof generation latency remains a bottleneck for real-time applications, optimization strategies such as proof-caching, cross-application preferences synchronization, and native implementation provides a promising path toward privacy-preserving personalization in social media systems.
Cryptocurrency research is vital for promoting safe, informed, and responsible adoption, benefiting governments, businesses, and individuals alike. With its growing impact on finance, technology, and the global economy, further study is essential. This research reviews literature on Bitcoin and Ethereum, focusing on their features, similarities, advantages, and disadvantages. Sources from journals, reports, proceedings, and other materials were examined to synthesize existing knowledge. The study highlights that while cryptocurrencies offer opportunities, their volatility and regulatory uncertainties pose significant risks. Therefore, cautious and informed decision-making is necessary for users and investors. Overall, this study provides valuable insights for policymakers, practitioners, and researchers, contributing to better strategies for the responsible adoption and regulation of cryptocurrencies.
The integration of IoMT is IoMT is medical image encrypting blockchain's paradigm in privacy has IoT advancements in healthcare have been modified by blockchain technology. The Internet of Medical Things is using blockchain frameworks and alongside other decentralized technologies to establish interoperability in health monitoring and safeguard medical data through patient remote monitoring. IoMT networks comprise numerous autonomous IoT-based systems working synergistically to facilitate patient health maintenance. Various methodologies for securing medical images, like PACS and IPFS, as well as off-chain storage with cryptographic hash referencing, have been devised. Every approach has its strengths and weaknesses, and the research considered the security, cost, scalability, and integrity of data. In this way, systems can enable remote monitoring of patients while preserving the confidentiality of their sensitive data. The RPM framework empowered by blockchain is also essential in improving health systems, engaging patients, and streamlining clinical workflows. This paper analyzes the intersection between IoMT and blockchain technology focusing on remote patient monitoring (RPM) systems.
The article presents a comprehensive overview of zero-knowledge proof (ZKP) protocols as a fundamental concept of modern cryptography. The historical background of their emergence and the main properties ensuring reliability and confidentiality, i.e., completeness, soundness, and zero-knowledge — are considered. A classification of protocols into interactive and non-interactive ones is provided, with a special focus on modern solutions such as the zk-SNARK and the zk-STARK. The mathematical foundations of ZKPs are described in detail, including discrete logarithm proofs, the use of homomorphic encryption, polynomial commitments, hashing, and elliptic curves. Practical application areas are analyzed, including cryptocurrencies (Zcash, Ethereum), authentication systems, digital identity, and electronic voting. The advantages of using ZKPs are shown, such as enhanced privacy, reduced need for trusted intermediaries, and strengthened security. At the same time, key challenges are outlined, including scalability, implementation complexity, the problem of trusted setup, and potential vulnerability to quantum computing. It is concluded that zero-knowledge proof protocols are a powerful tool for ensuring confidentiality and reliability of digital systems, while further research is aimed at creating more efficient and quantum-resistant solutions.
Rosario Gilmary, N Manvizhi, M Abinanthida, H. B. · 5 authors
Efficient management of construction waste is essential for achieving sustainability in building projects. However, current waste tracking systems often lack waste transparency, traceability, and trust among stakeholders. This study suggests a blockchain-based framework to improve how construction waste information networks operate. By using distributed ledger technology, the system allows safe data sharing among stakeholders, while minimizing data manipulation and inefficiencies. A prototype was created and tested using simulated construction waste scenarios. The testing showed better data accuracy and traceability. Results showed a 35% increase in waste audit reliability and a major decrease in reporting delays. The proposed method provides a scalable and tamper-proof solution for sustainable waste management. Future work will look at real-world implementation and how it can fit with regulatory platforms..
Satellite communication (SATCOM) networks are essential in delivering long-range and high-capacity data transmission on a global basis. With emerging satellite-ground-air integrated networks (SAGIN) responding to growing demands for communication and low-latency connections, their unique, dynamic infrastructure raises novel issues in security. Traditional centralized defenses are increasingly ineffective against advanced attacks such as distributed denial-of-service (DDoS). This research advocates an integrated solution that combines blockchain infrastructure with deep learning approaches to address these security challenges. The model was simulated in an NS-3 environment, and normal and attack traffic were generated to train a hybrid CNN-LSTM-based anomaly detection model. Distinct types of threats were recorded on a private Ethereum-based blockchain using smart contracts, enabling decentralized blacklist control and automated response behaviors. With decentralized control of threats, detection efficiency is enhanced by application of AI-driven analysis, and trust is ensured by virtue of immutable logging. The test results hold promise for this solution in delivering scalable, robust, and autonomous security for modern SATCOM networks.
Ahmet Efe Başol, Şerif Bahtıyar, Mehmet Tahir Sandıkkaya
Quantum computing presents a concrete threat to the cryptographic infrastructure of financial cryptocurrency systems. Commonly used public-key schemes, which are based on integer factorization and discrete logarithms, are vulnerable to quantum algorithms such as Shor’s. Post-quantum cryptography (PQC) provides distinct primitives that are conjectured to be resistant to known quantum computing attacks. Despite ongoing standardization efforts, these schemes remain largely untested in production-scale blockchain environments. Additionally, integrating PQC into distributed ledger systems introduces implementation challenges, including increased key and signature sizes, protocol compatibility issues, as well as potential disruptions to transaction verification, privacy guarantees, and governance mechanisms. This survey systematically examines the relationship between PQC and cryptocurrency systems, with a focus on algorithmic suitability, protocol-level impact, and deployment constraints.
Nishant Kumar -, Rishabh Dwivedi -, Om Babu Das Baniya -, T - Shilpa · 5 authors
An innovative DeFi application designed to empower users with seamless token swapping and liquidity management. Built to provide transparency and ease of use, DeFi-Swap incorporates real-time analytics, offering a dedicated page that showcases all listed tokens, their 24-hour price hanges, and intuitive visualizations through interactive graphs. Leveraging smart contract technology, DeFi-Swap ensures secure and trustless transactions, while the platform's backend, powered by cutting-edge tools, guarantees high performance and reliability. The user interface, designed with a focus on simplicity and engagement, provides an accessible experience for both seasoned traders and newcomers.
U ovom završnom radu se govori o temama koje su usko vezane za kriptovalute i njihovim načinima plaćanja. Od samog početka čovječanstva ljudi su međusobno razmjenjivali robu. Kako je čovječanstvo napredovalo, tako je napredovala i robna razmjena. Kako je napredovala razmjena, tako se razvijao novac i mijenjao svoj oblik. U današnje vrijeme se koristi papirnati novac, no sve veći udio na tržištu ima elektronički novac i plaćanje karticama koje polako postaju standard. Još trenutno slabije prihvaćen način plaćanja je plaćanje kriptovalutama, koje imaju svoje pozitivne i negativne strane, uspone i padove, te se bore za svoje mjesto na tržištu. No zasigurno imaju potencijala, što se jednim djelom vidi i kod udruživanja sa velikim igračima kao što je Visa, i sa sve većom prepoznatljivošću na tržištu.
Modern agriculture faces critical challenges such as climate change, food security and supply chain inefficiencies, which demand innovative solutions. Traditional farming systems often lack real time monitoring, data security and transparency, leading to wastefulness and quality concerns. To address these, we present a comprehensive precision agriculture framework that integrates Internet of Things (IoT) sensors, Raspberry Pi (R-Pi) edge computing, blockchain based data management and computer vision (CV) assisted statistical modeling. The system collects environmental data via a sensor network, processes it at the edge using R-Pi, and records summarized outputs on a secure Ethereum based blockchain using smart contracts. Simultaneously, CV modules perform real time quality assessment and anomaly detection. A Markov chain based stochastic model is employed to track quality degradation in high value crops. The methodology is validated through a saffron use case, demonstrating effectiveness in monitoring filament degradation and detecting potential fraud. This integration enhances real time decision making, ensures traceability and promotes sustainability in climate smart agriculture.
This paper studies the aspects of Distributed Ledger Technology (DLT) related to deciding how to order incoming ledger data records under the requirement to provide transparency and security in public information systems, particularly in the context of e-government. The study addresses the challenges of synchronizing ledger replicas across distributed systems, ensuring consistency and reliability in data storage and retrieval. The research highlights the importance of maintaining a unified ledger state, which is crucial for decisionmaking processes in the public and social sectors. The proposed solution combines Lamport's clock algorithm with wave algorithms to overcome the limitations of traditional timestamping methods, such as the duplication of timestamps for concurrent events. This approach ensures that each event in the distributed ledger is uniquely identified, thereby enhancing the system's robustness and efficiency.
Soleti Navya, Pankaj Bharati, S. Sree Hari Raju, Anil Kumar G · 6 authors
Blockchain technology is popular for its safe and decentralized architecture that help build digital contracts, supply chain management, and financial services applications. The key features of this technology is that smart contracts automatically perform agreements when certain predefined conditions are met. Coding errors or design flaws make them vulnerable to security attacks. People with malicious intent may exploit these flaws, resulting in significant financial losses and a decline in trust in blockchain technology as a whole. With help of artificial intelligence and machine learning algorithms this paper identifies anomalies in smart contracts and increase blockchain security by its mitigation. The machine learning algorithm is trained to identify anomalies that deviate from standard norms by analyzing patterns and behavior. The paper focuses on identifying common vulnerabilities that attackers may exploit like overflows, unauthorized access channels and re-entrancy bugs using a gradient boosting classifier. Developers and other stakeholders can be informed about possible security vulnerabilities before they become serious threats by using anomaly detection to generate early alerts. The primary goal of this research is to create a strong and reliable security tool that will significantly improve blockchain security and guarantee that smart contracts operate as intended and increase public confidence in blockchain technology.
This research presents a unified and intelligent security framework for cloud computing environments by integrating Quantum Neural Networks (QNNs), Zero-Knowledge Proof Engines (ZKEs), and Blockchain technology. As the scale and complexity of cloud infrastructures grow, traditional security mechanisms are proving insufficient against advanced cyber threats such as Distributed Denial of Service (DDoS), malware attacks, and Man-in-the-Middle (MITM) exploits. The proposed framework leverages the computational power of quantum systems to enhance the performance of Deep Learning models, enabling faster and more accurate threat detection. ZKEs provide privacy-preserving authentication by verifying user identities without revealing sensitive information, while Block chain ensures data integrity, decentralization, and tamper-proof transaction records. Experimental evaluationconducted using IBM Qiskit and a honey net-integrated Deep Neural Network (DNN) demonstrates a significant improvement in detection accuracy, reduced response time, and enhanced system resilience compared to conventional approaches. The results validate the effectiveness of the multi-layered model in addressing real-world cloud security challenges. This research contributes a scalable, privacy-centric, and quantum-secure architectural foundation for the next generation of cloud-based systems
Smart contracts power a vast array of blockchain applications, securing billions of dollars on decentralized finance, but their immutable nature turns every vulnerability into a permanent exploitable liability. Although automated security tools can efficiently detect many issues, their high false positive rates and lack of trust still require manual audits, which are costly and introduce deployment delays. In this paper, we present an end to end AI augmented auditing framework that leverages a multi-agent pipeline for comprehensive vulnerability detection and automated exploit generation. First, we review existing approaches such as static analysis, fuzzing, symbolic execution, formal verification, and machine learning methods, highlighting their strengths, limitations, and real world deployment experience. Building on this survey, we introduce a multi agent architecture composed of a Distributor Agent, an Attack Planner Agent, an Exploit Generator Agent, and an Audit Report Generator Agent. The pipeline ingests smart contract source code, documen-tation, and test suites to outline stepwise attack strategies and synthesize ready to compile Solidity exploit code. Exploits are compiled and validated in a containerized environment, enabling automated verification of attack effectiveness. We outline a validation strategy for future work, more specifically, applying the pipeline to capture the flag challenges and online bug bounty platforms, and we describe plans for prompt fine tuning, retrieval augmented generation, and formal verification integration to further enhance detection accuracy and exploit reliability. Our proposed framework promises a more comprehensive, scalable, and cost effective approach to smart contract security verification.
Blockchain sharding is a promising approach to improving system scalability. However, traditional designs rely on lock-based cross-shard commit protocols, which introduce significant performance bottlenecks due to repeated on-chain communication and consensus. The emergence of complex cross-shard contracts further exacerbates these issues. Although recent off-chain execution models reduce on-chain overhead by decoupling contract execution from consensus, they still incur high communication costs and struggle to maintain state consistency. To address these challenges, this paper presents a sharding framework that seamlessly integrates on-chain and off-chain processing. By leveraging Trusted Execution Environments (TEEs), the framework enables secure and efficient off-chain execution of cross-shard smart contracts. It incorporates an off-chain execution hub for verifiable contract execution and a state-aware cross-shard commit protocol to guarantee correctness. Furthermore, a genetic algorithm-based contract-migration strategy dynamically reduces cross-shard interactions. Prototype evaluations show that the proposed framework significantly outperforms mainstream sharding solutions, achieving at least 2.1× higher throughput and reducing cross-shard transaction latency by over 52.6%.
ABSTRACT Cryptocurrency markets are known for their wide price fluctuations, lack of central control, and fast‐paced development. These characteristics present serious challenges to traditional theories about how markets work and how prices reflect available information. Understanding how information is processed in these markets is essential for investors, policy makers, and academic researchers. This paper offers a thorough review on the extent to which cryptocurrency markets reflect information, based on 977 peer‐reviewed articles published between 2015 and 2024 and indexed in Scopus. Using a combined method of bibliometric analysis and thematic review, the study identifies key research directions and common methods used to explore how information affects cryptocurrency prices. The review goes beyond the Efficient Market Hypothesis (EMH) and includes related topics such as volatility modelling, behavioral dynamics, spillovers, liquidity, and institutional influences. It presents a detailed overview of the most influential publications and organises the literature into six thematic research clusters, highlighting conceptual tensions and new methodological approaches. Finally, the paper outlines a future research agenda that connects market efficiency with changing regulatory environments, innovations in market structure, and the increasing role of institutional actors in the cryptocurrency space.
Віталій Носов, Oleksandr Manzhai, Svitlana Luchyk, Vasyl Luchyk
With the rapid advancement of smart contracts, ensuring their security has become critically important. A key aspect of this security-both for potential users and for law enforcement investigating offenses involving smart contracts (modus operandi)-is the ability to interpret the logic of published contracts and verify the consistency between deployed bytecode and the original source code. The authors' research demonstrates that such verification was successfully achieved only within the Remix integrated development environment (IDE) using the Contract Verification-Etherscan plugin. Upon activation, the plugin verified the consistency of the deployed bytecode with the contract's source code, which was subsequently published on Etherscan and marked as "Contract Source Code Verified (Exact Match)." Similar results were obtained during testing with a smart contract for issuing non-fungible ERC721 tokens (NFTs). The study also identified potential vulnerabilities involving NFTs linked to digital assets stored off-chain at a URI defined during token issuance. These risks can be mitigated by employing an additional smart contract to ensure that NFT transfers include the associated digital asset, with the asset hosted on IPFS. The analysis and testing tools presented herein provide law enforcement with the means to understand the modus operandi of offenses involving smart contracts, thereby aiding in criminal investigations.