Smart agriculture is transforming a traditionally static sector by introducing advanced monitoring of crop processes and field conditions. In particular, the integration of the Internet of Things with Distributed Ledger Technology enhances agricultural operations by enabling real-time insights and fostering trust through secure, tamper-proof data management. This paper presents an innovative system that leverages IOTA’s decentralized ledger to securely capture and store realtime data from IoT sensors monitoring key environmental parameters such as temperature, humidity, and soil moisture. By removing centralized control, the system ensures data integrity, transparency, and resistance to tampering. Additionally, the use of smart contracts developed in the Move programming language strengthens the platform by automating data validation and facilitating traceable, reliable interactions. Field implementation demonstrates the system’s potential to improve decision-making, minimize resource waste, and support sustainable agricultural practices. Emphasizing security, scalability, and cost-efficiency, this solution offers a forward-looking approach to precision agriculture.
Blockchain technology offers significant potential for enhancing data integrity, transparency, and patient consent management in clinical trials. However, the security and reliability of these systems, particularly permissioned blockchains relying on threshold cryptography operated by collaborating institutions (nodes), fundamentally depend on the active and honest participation of these nodes. While technical safeguards are crucial, the underlying economic incentives and strategic behaviors governing node participation are often underexplored yet critical for long-term system viability and trustworthiness. This paper addresses this gap by applying game theory to analyze the strategic interactions between participating nodes (shareholders) and potential attackers within a permissioned blockchain framework designed for secure clinical trial data sharing. The strategic interactions are modeled by incorporating rational actors with defined costs (e.g., operational overhead), benefits (e.g., system access, reputation), potential illicit gains (e.g., from data breaches or collusion), and penalties (e.g., regulatory fines, exclusion). Payoff functions explicitly consider the influence of key system parameters: the total number of participating nodes (N) and the security threshold (t) required for critical operations. Using Nash equilibrium analysis, we investigate how variations in $\mathbf{N}$ and $\mathbf{t}$, alongside different cost-benefit-penalty structures relevant to the clinical trial context, impact the rational strategic choices of participants (i.e., choosing honest cooperation versus malicious collusion or attack). Our analysis aims to identify the conditions under which honest participation emerges as the dominant strategy, thereby ensuring the stability and security of the blockchain network from an incentive-compatibility perspective. The findings provide insights for designing more robust and trustworthy blockchain systems for sensitive healthcare applications by aligning participant incentives with overall network security, complementing existing technical and probabilistic security assessments.
This paper surveys the evolution of smart contract virtual machines (VMs), highlighting major designs such as Ethereum Virtual Machine (EVM), Move VM, Solana VM and couple of lesser known VMs. It eventually proposes a Rust implementation of Ton Open Network Virtual Machine (TVM) called Tycho VM. We analyze the theoretical foundations behind TVM’s “everything is a cell” paradigm and message execution lifecycle inside sandboxed VM. We also contextualize the role of virtual machines in the broader blockchain ecosystem by referencing developer adoption metrics and indicate challenges arising from that, namely, inefficiencies of Solidity to Tycho VM compiler which are going to be addressed in the future research.
Online review systems play a critical role in shaping consumer decisions and business reputation. However, they are increasingly vulnerable to manipulation, particularly through a coordinated practice known as review bombing. This occurs when large numbers of negative reviews are posted in a short time frame, often driven by political or ideological motives rather than genuine user experience. Such attacks distort public perception and can cause significant economic harm, particularly to small and medium-sized businesses. This paper presents a decentralized review platform architecture designed to enhance fairness, transparency, and resistance to manipulation. The system leverages Blockchain (BC) technology to enforce a one-NFT-one-review policy, in which each review is linked to a unique Non-Fungible Token (NFT) representing the user’s right to post. To ensure real-world authenticity, the platform incorporates a Proof-of-Visit mechanism using timelimited QR codes displayed on-site. Only users who scan the QR code at a physical location are authorized to mint the NFT. A prototype was implemented using CosmJS, CW721 smart contracts, and Keplr wallet integration, and deployed on the Neutron testnet. Empirical evaluation shows that the total cost for a complete review submission-including NFT minting and review posting-is approximately 0.012845 NTRN ($\approx$ ${\$}$ 0.00257${\$}$ USD), confirming the system’s economic feasibility. By addressing both the social dynamics of review bombing and the technical limitations of centralized platforms, this study proposes a scalable and cost-effective BC-based architecture for secure, verifiable, and tamper-resistant online review systems.
Industrial Control Systems (ICS) play a crucial role in ensuring the safe and reliable operation of high-risk sectors, including the oil and gas industry. Their system is highly vulnerable to sophisticated cyberphysical threats, which can disrupt processes, compromise safety, and incur significant economic losses. Conventional security measures focus primarily on detecting and preventing these threats. Still, they offer minimal assurance of resilience (which refers to the system’s ability to anticipate, absorb, adapt, and recover from incidents). This research suggests the use of Blockchain-Enabled Agile Cyber Resilience (B-ACR) framework to integrate blockchain as a tamper-evident trust backbone within agile validation cycles. The proposed framework integrates dynamic IT/OT telemetry, Zero Trust enforcement, and Red/Blue Team simulation for the generation of measurable and auditable resilience indicators across the main pillars (PTO) (People, Technology, Organisation). This research utilises an oil and gas pipeline case study to demonstrate how blockchain anchoring can enhance forensic integrity while reducing mean time to detect and mean time to recover. It also highlights how this framework enables transparent maturity benchmarking. The results of this study suggest that blockchainsecured agile validation provides a verifiable and scalable trail for regulators, strengthening resilience in ICS defence environments.
Managing cryptographic keys remains a major barrier to blockchain adoption, especially for non-technical users. This paper presents a smart cardbased solution for secure and user-friendly key management, offering physical isolation of private keys and PIN-protected access via NFC. In a comparative study with the Waves Keeper browser extension, 33 participants completed blockchain-related tasks and rated both solutions in different categories of the Technology Acceptance Model (TAM) such as Perceived Usefulness, Ease of Use, and Result Demonstrability. The smart card system showed clear advantages in usability and perceived security. In general, the results highlight the potential of hardware-based approaches to improve blockchain accessibility and acceptance, with implications for Web3 applications and future research on usability and security integration.
Blockchains have revolutionized information systems, evolving continuously in both performance and application sophistication. This paper compares public blockchain performances by examining their technical foundations and practical applications across sectors. Through benchmark analysis of key criteria, we develop hypotheses explaining performance variations. Our goal is to provide insight into this maturing ecosystem whose impact now extends well beyond cryptocurrencies into numerous innovation domains.
Decentralized Autonomous Organizations (DAOs) are emerging as key governance structures in Web3 ecosystems, enabling community-driven decision-making without centralized control. Yet, current DAO implementation frameworks often lack modularity, role adaptability, and low-code accessibility, limiting broader adoption-especially among non-technical users and emerging organizations. Targeting these issues, this paper proposes a modular, template-driven DAO system for public blockchains such as Ethereum, integrating reusable governance logic units, token-based role configuration, and low-code interfaces to support flexible and secure deployment. The system allows role-scoped configuration of governance parameters via templates and supports scenario-specific deployment using predefined smart contracts, avoiding the need for direct code modification in the demonstrated use case. A quadratic votingbased governance scenario was used to demonstrate how the proposed framework can enable DAO setup and participation without requiring direct contract modification for that specific use case. Internal validation, conducted with simulated stakeholder roles, confirmed improvements in usability, configuration safety, and governance clarity, while also identifying practical gaps in role-specific UI guidance and simulation tooling. Although the current implementation is limited to a single voting model and local deployment, the findings highlight the potential of templatebased DAO systems to enable more inclusive, adaptable, and transparent decentralized governance.
Open Source Intelligence (OSINT) plays a critical role in cybersecurity and threat intelligence. However, traditional methods are slow, manual, and difficult to scale. Although Large Language Models (LLMs) and Generative AI have been explored for OSINT, most existing approaches apply them to isolated tasks without developing an integrated and autonomous architecture. This paper proposes a novel Agentic AI-driven OSINT framework that enables autonomous information gathering, reasoning, and tool orchestration across heterogeneous open-source data streams. The system uses retrieval-augmented generation (RAG), chain-of-thought reasoning, and adaptive agent planning to determine which tools to invoke, how to process intermediate outputs, and when to escalate findings for human review. The proposed architecture includes modules for multi-source data ingestion, LLM-powered analysis, generative scenario simulation, and ethical safeguard enforcement. A proof-of-concept use case involving the location of missing persons from public data demonstrates how the framework improves coverage, accuracy, and decision speed when compared to conventional OSINT workflows. This work introduces the first unified and reproducible design for an Agentic AI OSINT system that incorporates transparency, accountability, and ethical compliance into its operational core.
This paper presents a new quantitative model to study the interplay across CIA (Confidentiality, Integrity, Availability) Triad and performance in blockchain-based crypto space with specific reference to Ethereum or Ethereum-equivalent chains. The model introduces and incorporates three new random variables on top of the baseline chain model [16], C: the likelihood to secure confidentiality; $\boldsymbol{I}$: to secure integrity; and $\boldsymbol{A}$: to secure availability. Thus, the model will orchestrate an extensive set of key random variables such as λ: transaction slot arrival rate; μ: block posting rate; $\boldsymbol{i}$: number of transaction slots pending on the current block along with $\boldsymbol{C}, \boldsymbol{I}$ and $\boldsymbol{A}$. The underlying mathematical method employed is an embedded Markovian queueing model as the model traces the stochastic flow of the transactions as well as the Markovian flow of them with respect to $\boldsymbol{C}, \boldsymbol{I}$ and $\boldsymbol{A}$. The state in the model is defined by $P_{i_{C / I / A}}$, i.e., the likelihood to have i number of transaction slots pending on the current block and the stochastic CIA status of the crypto space thus far is in C or $\bar{C}, I$ or $\overline{\boldsymbol{I}}$, and $\boldsymbol{A}$ or $\overline{\boldsymbol{A}}$. The solutions to the model will be provided to assess a few basic performance metrics such as W: the average transaction waiting time, $L:$ the average block capacity required; and G: the throughput of transactions per block. And further and primarily, a unique and extensive simulation and analysis will be conducted to evaluate the impact of base random variables such as i, λ, μ, and various combinations of $\mathcal{C}, I$ and A on the overall $P_{i_{C / I / A}}$ in steady state. The results of the simulation reveal tradeoffs between the CIA Triad and performance that is uniquely identifiable by the proposed model.
Smart contracts have emerged as key components within decentralized environments, enabling the automation of transactions through self-executing programs. While these innovations offer significant advantages, they also present potential drawbacks if the smart contract code is not carefully designed and implemented. This paper investigates the capability of large language models (LLMs) to detect OWASP-inspired vulnerabilities in smart contracts beyond the Ethereum Virtual Machine (EVM) ecosystem, focusing specifically on Solana and Algorand. Given the lack of labeled datasets for nonEVM platforms, we design a synthetic dataset of annotated smart contract snippets in Rust (for Solana) and PyTeal (for Algorand), structured around a vulnerability taxonomy derived from OWASP. We evaluate LLMs under three configurations: prompt engineering, fine-tuning, and a hybrid of both, comparing their performance on different vulnerability categories. Experimental results show that prompt engineering achieves general robustness, while fine-tuning improves precision and recall on less semantically rich languages such as TEAL. Additionally, we analyze how the architectural differences of Solana and Algorand influence the manifestation and detectability of vulnerabilities, offering platform-specific mappings that highlight limitations in existing security tooling. Our findings suggest that LLM-based approaches are viable for static vulnerability detection in smart contracts, provided domain-specific data and categorization are integrated into training pipelines.
In the unregulated world of Initial Coin Offerings (ICOs), hiding malicious trading is all too easy in a large-scale set of transactions. This paper uses a graph-based representation of the blockchain to identify a topology that reveals suspicious intent to manipulate the perceived value of those offerings. As the computational complexity of identifying this topology could be prohibitive for unfiltered data-sets, this work derives metrics indicative of the topology. Using these explicitly-defined metrics and a past degradation of service on the Ethereum network originating with the iFishYunYu token, we show how this approach can reveal it to have been a deliberate attack, rather than simply an unprecedentedly highly-traded token. The formalization of this approach in the paper will allow detection of other such “pump-and-dump” attacks in the future.
Global supply chains face pervasive challenges-low traceability issues and counterfeit products cost industries billions each year. Opaque and siloed records make it hard to verify provenance or detect tampering. Numerous studies have proposed the use of RFID, NFC, and barcode technologies for product traceability in supply chains. However, these tagging methods present significant security limitations. RFID and NFC tags are susceptible to cloning and spoofing, while barcodes and optical markers can be easily duplicated or physically tampered with. Such vulnerabilities undermine the reliability of these systems for ensuring data integrity and product authenticity in high-security supply chain environments. To address the aforementioned challenges, we propose a lightweight framework for product authentication and traceability that leverages an Arbiter Physical Unclonable Function (PUF) simulated in Python with Elliptic Curve Cryptography (ECC) key pairs derived from Elliptic Curve Digital Signature Algorithm (ECDSA), integrated into a permissionless Ethereum blockchain using a smart contract. This two-step approach ensures genuine products and maintains an immutable and easily retrievable audit trail of their journey through the supply chain. The experiments included smart contract unit tests, gas profiling, and transaction latency measurements using a local blockchain environment. In addition, we conducted a controlled experiment to evaluate the performance impact of storing product and authentication data either fully on-chain or partially off-chain using IPFS-based content identifiers (CIDs). The results validate the feasibility of our architecture as a practical, cost-efficient, and scalable solution for real-world PUF-enabled supply chain systems.
This study aims to investigate whether Blockchain technology plays the same role as central banks in controlling the money supply. Central banks use monetary policies to maintain financial stability and achieve macroeconomic objectives. However, since their emergence in 2009, cryptocurrencies, particularly Bitcoin, Ethereum, and then later USDT, have become universally accepted as a means of payment or assets. This could greatly impact local and global economic performance, because cryptocurrencies are not controlled by any authority. Instead, they are managed by Blockchain technology for issuing new units, ensuring transfers, and recording transactions. Blockchain technology is a decentralized digital lager that is administered by a collective of peers, where each one has its own objective, which may be inconsistent with the goals of central banks.
Citizen Science involves the engagement of public in scientific research to augment and disseminate scientific knowledge. This nurtures the practice of sharing and contributing to data gathering and monitoring activities with the participation of scientific communities and general public. It incorporates idea generation through community formation and invites scientists for leadership, guidance and coordination. Existing citizen science programs are centralized; however, such endeavors may be supported through decentralized autonomous communities realized through blockchains and smart contracts adding transparency and security to the projects. Blockchain is a shared immutable distributed ledger technology that addresses double spending and Byzantine General’s Problem and enables peer to peer digital payments in absence of intermediaries. It has several applications beyond financial sector or cryptocurrencies and one such utility is the smart contract that entails a piece of script that will be automatically executed if certain conditions are fulfilled, in other words, it is a self-executing digital contract. In this paper, we propose a novel colored blockchain technique to develop decentralized autonomous communities leveraging which citizen science can be perceived in a decentralized context. We present the details of the framework that uses such colored blockchain technology to implement decentralized citizen science concept. We also propose an alternate decentralized application leveraging smart contract to implement decentralized citizen science concept.
The rapid expansion of the Internet of Things (IoT) has intensified the demand for secure, interoperable, and userfriendly data sharing platforms. While blockchain-based decentralized architectures have been proposed to address privacy and transparency issues, most existing solutions lack a standardized authentication mechanism for both data providers and consumers, as well as flexible search capabilities for efficient data retrieval. This paper presents a blockchain-based decentralized IoT data distribution system that integrates Self-Sovereign Identity (SSI) for trustless authentication and an enhanced metadata search mechanism for condition-based data discovery. In the proposed architecture, raw IoT data remains on the originating devices to preserve privacy, while metadata is managed using PostgreSQL and stored on IPFS, with blockchain smart contracts ensuring transparency and traceability. SSI enables secure, decentralized verification of stakeholders without relying on a central authority, and the search framework allows data consumers to locate and acquire datasets matching specific requirements.
The Ripple-developed XRP Ledger (XRPL) is fundamental for securing blockchain transactions efficiently. However, the rise of quantum computing threatens the cryptographic algorithms the XRPL relies on, for example Secp256k1 and Ed25519-donna for securing transactions and validating digital signatures. Shor’s and Grover’s algorithms can break the security of Secp256k1 and Ed25519-donna which will make them vulnerable to attack. To mitigate this critical threat, we propose integrating Post-Quantum Cryptographic (PQC) algorithms into XRPL. Our focus is on the NIST standardized CRYSTALSDilithium candidate, which is a lattice-based algorithm that offers robust security against classical and quantum attacks. By incorporating CRYSTALS-Dilithium alongside existing classical algorithms, a smooth transition to quantum resistance is enabled while also ensuring backward compatibility. This requires modifications to the XRPL protocol, key management infrastructure, and transaction validation process. Our approach not only strengthens XRPL against future threats, but also sets a standard for quantum-resistant blockchain security.
Blockchain technologies have revolutionized data storage by ensuring immutability, decentralized control, and enhanced system availability. However, current blockchain frameworks often fall short in providing sufficient privacy and control to data owners. As privacy concerns grow, highlighted by regulations such as the GDPR’s ‘Right to be Forgotten’, there is an increasing demand for blockchain architectures that grant data owners greater control while allowing flexible and efficient participation of nodes as miners. In this paper, we introduce MerkleChain, an innovative redactable ledger architecture designed to address these challenges. Built upon Merkle trees and chameleon hashes, our system ensures tamper-evidence for individual data owners and preserves privacy. The architecture allows miners to store only the Merkle trees and chameleon hashes of data they subscribe to, enhancing both data privacy and system efficiency. In addition, it supports parallel processing of individual owner-controlled Merkle trees, leading to increased throughput. MerkleChain is adaptable to a range of applications, and we demonstrate its potential in a healthcare setting.
This paper evaluates and systematizes the existing knowledge (SoK) on blockchain governance. It starts with an overview of existing research on decentralized blockchain governance through a little-known but extremely relevant paradox on blockchain governance, called the governance paradox. In that paradox a distinction is made between making the rules and following the rules, something various other studies also found be it slightly differently worded. The paper then presents the results of an empirical study, defining what blockchain governance is, inspired by the views of blockchain insiders and experts. Through a Delphi study, they were asked several questions related to blockchain governance. This was done remotely, over time, iteratively and, ideally, led to a group consensus in the spirit of blockchain philosophy. The respondents identified 43 attributes by which they described the governance of the decentralized blockchain. After coding and analysis, they were subsequently reduced to core categories, leading to the characterization of blockchain governance by only two attributes. The results were then compared with existing academic literature, showing a remarkable overlap. The empirical component, in which blockchain experts were interviewed, added a new insight that matched the existing research and, by that, validates both the existing literature and the empirical findings of this study. This paper ends with three conclusions regarding blockchain governance. To begin with, governance can be reduced to two attributes, namely engagement and confidence. Furthermore, the human factor, as represented by the attribute engagement, plays and should play a significant role. Lastly, there should be an ongoing interaction between engagement and confidence: the blockchain governance loop.
Digital twins have become essential to Industry 5.0, enabling continuous synchronization between physical systems and their digital counterparts. They support predictive maintenance, operational efficiency, and data-driven decision-making. Several solutions for ensuring integrity of static data are available. However, ensuring the integrity and trustworthiness of data exchanged in real time remains a major challenge, particularly in decentralized industrial environments prone to tampering, high latency, and performance bottlenecks. This paper presents TwinIpfsChain, a decentralized architecture that combines the Solana blockchain and IPFS to guarantee secure, verifiable, and scalable data management for digital twins. The proposed approach stores sensor data hashes on-chain, while the full data is preserved in InterPlanetary File System (IPFS). A smart contract manages automatic integrity verification and enforces access control.TwinIpfsChain ensures real-time synchronization of high-frequency sensor data for digital twins in Industry 5.0. We implemented and evaluated the system under varying transaction loads $(\mathbf{1, 0 0 0 - 1 0, 0 0 0})$ to assess performance across key metrics: latency, throughput, energy consumption, and security. Our results show that TwinIpfsChain achieves low latency (average 218 ms), high throughput (up to 26,457 TPS), low power usage (under 13 W), and $100 \%$ detection of tampering, hash manipulation & replay attacks. Transaction costs remain negligible 0.84 USD per 1,000 transactions making our solution viable for resource constrained industrial deployments. Compared to existing blockchain-based models, TwinIpfsChain offers stronger real-time guarantees, improved scalability, and robust protection against data integrity threats, positioning it as a practical and high-performance solution for digital twin applications in Industry 5.0.
