The Ethereum network, built on the devp2p protocol stack, was designed to function as a "world computer" by supporting decentralized applications through a shared P2P infrastructure. However, the proliferation of blockchain forks has increased network diversity, complicating node discovery and reducing efficiency. Ethereum mainnet nodes cannot easily distinguish between peers from different blockchains until after establishing an expensive TCP connection, encryption, and protocol handshake. This inefficiency is further worsened by client diversity, where differences in software implementations cause protocol incompatibilities and connection failures. This paper introduces a monitoring tool that tracks devp2p message exchanges and client statuses to analyze connection dynamics and protocol variations. Our findings highlight issues such as inefficiencies in node discovery and client incompatibility, including timeouts in Geth during the discovery process. The study emphasizes the need to consider chain and client diversity when assessing the health and performance of the post-merge Ethereum network.
In the realm of decentralized applications, smart contracts play a pivotal role in managing an extensive array of digital assets within blockchain networks. Ensuring the security of these digital assets hinges upon the adept detection of vulnerabilities present within smart contracts. Extensive research efforts have scrutinized and elucidated numerous smart contract vulnerabilities. However, certain vulnerabilities, including signature malleability, hash collision, and inconsequential code segments, remain relatively unexplored and devoid of dedicated detection tools. In response to this research gap, this paper addresses these three previously understudied vulnerabilities. We contribute to the field by creating a labeled dataset comprising vulnerable smart contracts. This dataset serves as a valuable resource for further scientific inquiries, enabling the testing and validation of various detection frameworks. Additionally, we present SmartSentry a static vulnerability detection framework capable of identifying these vulnerabilities. Using both dataflow and control flow analysis, our framework exhibits exceptional performance, successfully identifying labeled vulnerabilities and real-world vulnerabilities within production smart contracts with speed and efficiency. These efforts collectively enhance our understanding of smart contract vulnerabilities and contribute to the broader advancement of blockchain security.
Chonghe Zhao, Yipeng Zhou, Shengli Zhang, Quan Z. Sheng · 6 authors
Proof of Authority (PoA) plays a pivotal role in blockchains for reaching consensus. Clique, which selects consensus nodes to generate blocks with a pre-determined order, is the most popular implementation of PoA due to its low communication overhead and energy consumption. However, our study unveils that the speed to process transactions by Clique is severely restricted by 1) the long communication delay of full blocks (each containing a certain number of transactions) between consensus nodes; and 2) occurrences of no-turn blocks, generated by no-turn nodes if an in-turn block generation fails. Consequently, Clique struggles to support distributed applications requiring a high transaction processing speed, e.g., online gaming. To overcome this deficiency, we propose an Express Clique (ExClique) algorithm by improving Clique from two perspectives: compacting blocks for broadcasting to shorten communication delay and prohibiting the occurrences of no-turn blocks. For performance evaluation, we implement ExClique by modifying Geth of Ethereum, the software implementing Clique, and deploy a permissioned blockchain network by using container technology. The experimental results show that ExClique achieves a substantial enhancement in transactions per second (TPS). Specifically, it boosts TPS by 2.25X in a typical network with 21 consensus nodes and an impressive 7.01X in a large-scale network with 101 consensus nodes when compared to Clique.
Cryptocurrencies like Ethereum and Bitcoin are highly volatile, offering both advantages and disadvantages to financiers and traders. Accurately forecasting price variations is crucial in such a dynamic environment. The intricacies of cryptocurrency trading, including the impact of emotions, often surpass conventional methods. Incorporating media scrutiny, our research proposes an improved collective system for forecasting Bitcoin and Ethereum price changes. Our integrated model combines the strengths of multiple models, leveraging historical price data and sentiment analysis of news articles to capture the influence of news sentiment on price movements. We gather news from credible sources and collect historical price data from cryptocurrency trading platforms to construct our database. The ensemble model generates more reliable predictions by combining predictions from multiple models, reducing volatility and biases. Experimental results show that our model outperforms baseline approaches, accurately predicting price swings for Bitcoin and Ethereum. Incorporating news analysis significantly improves predictive accuracy, emphasizing the importance of considering external variables in forecasting Bitcoin prices.
Payment channel networks (PCNs) offer a promising solution to address the limited transaction throughput of deployed blockchains. However, several attacks have recently been proposed that stress the vulnerability of PCNs to timelock and censoring attacks. To address such attacks, we introduce Thunderdome, the first timelock-free PCN. Instead, Thunderdome leverages the design rationale of virtual channels to extend a timelock-free payment channel primitive, thereby enabling multi-hop transactions without timelocks. Previous works either utilize timelocks or do not accommodate transactions between parties that do not share a channel. At its core, Thunderdome relies on a committee of non-trusted watchtowers, known as wardens, who ensure that no honest party loses funds, even when offline, during the channel closure process. We introduce tailored incentive mechanisms to ensure that all participants follow the protocol's correct execution. Besides a traditional security proof that assumes an honest majority of the committee, we conduct a formal game-theoretic analysis to demonstrate the security of Thunderdome when all participants, including wardens, act rationally. We implement a proof of concept of Thunderdome on Ethereum to validate its feasibility and evaluate its costs. Our evaluation shows that deploying Thunderdome, including opening the underlying payment channel, costs approximately \$15 (0.0089 ETH), while the worst-case cost for closing a channel is about \$7 (0.004 ETH).
Abstract Market efficiency assumes that prices in financial markets are perfectly informative and, therefore, it is not possible to design trading strategies that outperform the market. The concept of efficiency has important implications for financial stability and, consequently, for financial policies. If asset returns exhibit persistent or anti-persistent behavior, then predictability based on past returns might be possible, which would be a clear violation of the weak form of efficiency. Many studies rely on the Hurst exponent to evaluate the level of memory of financial returns, and the purpose of this paper is to show that long memory or anti-persistence of financial returns is not incompatible with the random walk model or the efficient market hypothesis (EMH). The use of the Hurst exponent to demonstrate the inefficiency of financial markets using common estimators is troublesome, especially when applied to financial returns, since values of $$\hat{H} \ne 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>H</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>≠</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> are not evidence against the random walk model or the EMH. Moreover, the high variability of Hurst exponent estimates and their dependence on the chosen algorithm should motivate careful use of this tool. This study proposes a simple theoretical explanation and an extensive simulation study to show that $$\hat{H} \ne 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>H</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>≠</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> for financial returns is perfectly compatible with the random walk model. As a robustness check, both the traditional rescaled range and the wavelet lifting algorithms are used. Applications to real data are also discussed to show that the empirical values of the Hurst exponent are in the range suggested by the simulations, providing evidence that over-reliance on the Hurst exponent could lead to erroneous rejection of the random walk model. Specifically, the paper presents an application to the daily returns of stock market indices (DJIA and S&P 500) over a period of more than 30 years and cryptocurrencies (Bitcoin and Ethereum) over a period of more than 5 years.
The study examines the relationship between volume and volatility in leading cryptocurrencies i.e. Bitcoin and Ethereum, within the framework of Mixture of Distribution Hypothesis (MDH). It accommodates structural shifts in the cryptocurrency prices and uses fat-tailed distributions. The results show that the MDH is rejected for both cryptocurrencies, and volume alone cannot explain the heteroskedasticity of returns; however, it acts as a significant predictor for volatility, especially when incorporating structural breaks in the model. Further, the forecasting performance improves when fat-tailed distributions, such as the skewed student’s t and Johnson’s Su distribution are used to model the innovations. Thus, volume holds important information in the crypto markets and can affect returns, thereby, raising concerns about market efficiency. Our results are robust across different periods, modelling approaches and forecasting horizons, and hold substantial implications for traders, market participants, regulators, and governments in designing effective policies.
Saralees Nadarajah, Jules Clément, Patrick Rakotomarolahy, Henri T. J. E. Ratolojanahary
The purpose of this study is to conduct an empirical comparative study of volatility models for three of the most popular cryptocurrencies. We study the volatility of the following cryptocurrencies: Bitcoin, Ethereum, and Litecoin. We consider the GARCH-type, boosting-family-tree-based ensemble learning, and ANFIS volatility models for these financial crypto-assets, which some have claimed capture stylized facts about cryptocurrency volatility well. We conduct comparative studies on in-sample and out-of-sample empirical analyses. The results show that tree-based ensemble learning delivers better forecast accuracy. Nevertheless, the performance of some GARCH-type volatility models is relatively close to that of the best model on both training and evaluation samples.
This study analyzes proposer-builder data and MEV-Boost payment data following the Ethereum merge in September 2022 to identify patterns of multi-block MEV. Our findings reveal fewer multi-slot sequences of builders than predicted by a random Monte Carlo simulation, with the longest observed sequence spanning 25 slots. Additionally, we observe that average MEV-Boost payments increase with the length of consecutive sequences, from approximately 0.05 ETH for single slots to 0.08 ETH for nine consecutive slots. Within longer sequences, payments per slot show a slight increase, suggesting that builders bid higher for longer sequences or the first slot after a longer sequence. A weak positive autocorrelation is found between subsequent MEV-Boost payments, challenging the hypothesis of alternating periods of low and high MEV. Finally, our comparison of builders during periods of low and high base fee volatility reveals minimal correlation, indicating the absence of builder specialization based on base fee volatility.
In blockchain-based order book systems, buyers and sellers trade assets, while it is miners to match them and include their transactions in the blockchain. It is found that many miners behave selfishly and myopically, prioritizing transactions with high fees and ignoring many desirable matches that could enhance social welfare. Existing blockchain mechanisms fail to address this issue by overlooking miners' selfish behaviors. To our best knowledge, this work presents the first analytical study to quantify and understand buyer and seller transaction fee choices and selfish miners' transaction matching strategies, proving an infinitely large price of anarchy (PoA) for social welfare loss. To mitigate this, we propose an adjustable block size mechanism that is easy to implement without altering the existing decentralized protocols and still allows buyers and sellers to freely decide transaction fees and miners to selfishly match. The analysis is challenging, as pure strategy Nash equilibria do not always exist, requiring the analysis of many buyers' or sellers' interactive mixed-strategy distributions. Moreover, the system designer may even lack information about each buyer's or seller's bid/ask prices and trading quantities. Nevertheless, our mechanism achieves a well-bounded PoA, and under the homogeneous-quantity trading for non-fungible tokens (NFT), it attains a PoA of 1 with no social welfare loss. We implement our mechanism on a local instance of Ethereum to demonstrate the feasibility of our approach. Experiments based on the realistic dataset demonstrate that our mechanism achieves social optimum for homogeneous-quantity trading like NFT. It can enhance social welfare up to 3.7 times compared to the existing order book benchmarks for heterogeneous-quantity trading of Bitcoin tokens. It exhibits robustness against random variations in buyers and sellers.
This paper introduces 3MEthTaskforce (https://3meth.github.io), a multi-source, multi-level, and multi-token Ethereum dataset addressing the limitations of single-source datasets. Integrating over 300 million transaction records, 3,880 token profiles, global market indicators, and Reddit sentiment data from 2014-2024, it enables comprehensive studies on user behavior, market sentiment, and token performance. 3MEthTaskforce defines benchmarks for user behavior prediction and token price prediction tasks, using 6 dynamic graph networks and 19 time-series models to evaluate performance. Its multimodal design supports risk analysis and market fluctuation modeling, providing a valuable resource for advancing blockchain analytics and decentralized finance research.
A. Bendada, Abdelaziz Amara Korba, Mouhamed Amine Bouchiha, Yacine Ghamri‐Doudane
The rapid growth of decentralized finance (DeFi) has led to the widespread use of automated agents, or bots, within blockchain ecosystems like Ethereum, Binance Smart Chain, and Solana. While these bots enhance market efficiency and liquidity, they also raise concerns due to exploitative behaviors that threaten network integrity and user trust. This paper presents a decentralized federated learning (DFL) approach for detecting financial bots within Ethereum Virtual Machine (EVM)-based blockchains. The proposed framework leverages federated learning, orchestrated through smart contracts, to detect malicious bot behavior while preserving data privacy and aligning with the decentralized nature of blockchain networks. Addressing the limitations of both centralized and rule-based approaches, our system enables each participating node to train local models on transaction history and smart contract interaction data, followed by on-chain aggregation of model updates through a permissioned consensus mechanism. This design allows the model to capture complex and evolving bot behaviors without requiring direct data sharing between nodes. Experimental results demonstrate that our DFL framework achieves high detection accuracy while maintaining scalability and robustness, providing an effective solution for bot detection across distributed blockchain networks.
The advent of Quantum Computing (QC) poses significant threats to the cryptographic foundations of Blockchain (BC) systems, as quantum algorithms like Shor’s and Grover’s undermine the security of public-key cryptography and hash functions. This research conducts a comprehensive risk assessment of quantum vulnerabilities across critical BC components, including consensus mechanisms, smart contracts, and digital wallets. Leveraging the STRIDE threat modeling framework, we analyze threat vectors specific to QC, identifying key areas most susceptible to quantum-enabled attacks, such as private key compromise, consensus disruptions, and smart contract integrity risks. Our contributions provide actionable mitigation strategies, including a detailed security blueprint for quantum resilience, encompassing the integration of Post-Quantum Cryptography (PQC) and the adoption of quantum-resistant hash functions. We offer implementation best practices, focusing on key management, secure coding, and network security to strengthen BC components against quantum threats. To mitigate the risk of QC during transition from classical to quantum-resistant BCs, we present two hybrid BC architectures. As part of a comprehensive quantum resilience strategy, these architectures facilitate a secure and scalable migration by integrating platform-specific adaptations that balance security, adaptability, and operational efficiency. Our analysis extends to major BC platforms, including Bitcoin, Ethereum, Ripple, Litecoin, and Zcash, providing platform-specific vulnerability assessments and highlighting unique weaknesses in the quantum era. By identifying vulnerabilities, developing proactive defense strategies, and adopting a structured hybrid migration approach, this research equips BC stakeholders with a robust framework to achieve long-term quantum resilience. Finally, we explore challenges and research directions for integrating emerging technologies, including quantum machine learning, Artificial Intelligence (AI), and Web3, with BC systems, and discuss new threats that may arise from this convergence in the QC era.
This study is the first to scientifically investigate stock indexes and currency exchanges that affect crypto prices. The purpose is to distinguish between the USA-Japan stock markets and the currency market&#039;s short- and long-term effects on bitcoin and ethereum. Auto Regressive Distributed Lag (ARDL) is used to analyze weekly series from 1-1-2016 to 20-10-2024. An asymmetric error-checking framework employing non-linear ARDL statistical approach to study variables affecting bitcoin and ethereum prices. Bitcoin appear to have short- and long-term linear effects on the US-Japan stock markets. Euro, GBP, and USA-Japan stock markets exhibit short-term linear effects with ethereum. Ethereum linearly affects GBP. This research helps exchange brokers and crypto traders diversify their holdings, reduce stock index and currency exchange risk, and accurately predict bitcoin and ethereum price variations.
Internet of Things (IoT) is adopted in a wide spectrum of applications in which a vast amount of data are produced and distributed to centralized cloud platforms to deliver various services. It involves smart devices that collect thousands of terabytes of heterogeneous data and deployed this to make instant decision that aids for the better performance and most comfort life. Traditional IoT architecture is heavily centralized, where it stores the most sensitive information that creates the multiple threats and security breaches as the attackers target towards these centralized cloud systems. To improve the security chain in IoT environment, edge computing (EC) was introduced to distribute the applications of IoT at the edge of the communication networks. However, these edge-based IoT are also vulnerable to many threats due to their decentralized and in secured management. Block chain (BC) technology offers a most trusted solution to resolve the security issues in the IoT-Edge computing environment. This research study presents the block chain driven medical image encryption technique using modified honey badger optimization with the ensemble chaotic systems. The proposed block chain framework uses the divergent methods that integrates differential scroll, Hénon chaotic maps and modified honey badger optimization to generate the optimum keys and high secured image data. These high secured data are stored in the block chain, ensuring the image security to be stored in edge nodes. The complete framework was experimented using Ethereum using Ganache API and Python3.19 are utilized as the major programs for designing the varied interfaces of the recommended model. The comprehensive experimentation is undertaken to assess the security strength of the recommended encryption scheme. The evaluation metrics like as NACI, UACI, Entropy and standard verification methods such as NIST standard tests are deployed and analyzed. To prove it security strength, proposed secured BC framework is compared with the wide-variety of secured frameworks. The experimental findings reveal that the suggested framework establishes a more robust and secure environment for image exchange, surpassing the performance of other blockchain-based systems in terms of integrity, robustness and security. Finally, the paper spreads the bright light of advantages in deploying the proposed framework to formulate the most secured environment in the IoT-Edge environment for medical image transmission.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
We explore volatility spillover effects between mainstream cryptocurrencies and energy token markets in the 120 days following three notable Blockchain bridge heists in 2022. Using the DCC-GARCH model, we find significant spillover effects between Bitcoin, Ethereum, and energy tokens like Power Ledger Token and Energy Web Token post-heists. This indicates heightened investor concern and panic trading impacting cryptocurrencies and energy token markets. Our analysis also reveals a herding behaviour in energy tokens under market stress and increased liquidity issues, leading to broader market quality deterioration. Based on these findings, we propose regulatory enhancements and the ‘Energy Future Fund’ to support the stability and growth of energy token markets.
Ethereum is currently the second largest blockchain by market capitalization and a popular platform for cryptocurrencies. As it has grown, the high value present and the anonymity afforded by the technology have led Ethereum to become a hotbed for various cybercrimes. This paper seeks to understand how these fraudulent schemes may be characterized and develop methods for detecting them. One key feature introduced by Ethereum is the ability to use programmable smart contracts to execute code on the blockchain. A common use of smart contracts is implementing fungible tokens with the ERC-20 interface. Such tokens can be used to impersonate legitimate tokens and defraud users. By parsing the event logs emitted by these ERC-20 contracts over 20 different periods of 100K blocks, we construct token transfer graphs for each of the available ERC-20 tokens on the blockchain. By analyzing these graphs, we find a set of characteristics by which suspicious contracts are distinguished from legitimate ones. These observations result in a simple model that can identify scam contracts with an average of 88.7% accuracy. This suggests that the mechanism by which fraudulent schemes function strongly correlates with their transfer graphs and that these graphs may be used to improve scam-detection mechanisms, contributing to making Ethereum safer.
Selfish mining is strategic rule-breaking to maximize rewards in proof-of-work protocols. Markov Decision Processes (MDPs) are the preferred tool for finding optimal strategies in Bitcoin and similar linear chain protocols. Protocols increasingly adopt DAG-based chain structures, for which MDP analysis is more involved. To date, researchers have tailored specific MDPs for each protocol. Protocol design suffers long feedback loops, as each protocol change implies manual work on the MDP. To overcome this, we propose a generic attack model that covers a wide range of protocols, including Ethereum Proof-of-Work, GhostDAG, and Parallel Proof-of-Work. Our approach is modular: we specify each protocol as a concise program, and our tooling then derives and solves the selfish mining MDP automatically.
Decentralized application make use of peer-to-peer networks, this ensures that no network failure can occur due to central node failure. Blockchain serves as an immutable ledger which allows messaging to take place in a decentralized manner. A decentralized application for communication and resource sharing is need in today’s world, where keeping data on a centralized server can be risky and costly experience. With the help of various consensus, we can implement different ways to share resources and communicate. Together with Blockchain and Decentralized Applications, we can create a secure and reliable messaging application that overcomes the drawbacks of traditional messaging applications.
Essa Al-Mansouri, Ahmet Faruk Aysan, Ruslan Nagayev
This paper examines Bitcoin’s viability as money through the lens of its risk profile, with a particular focus on its store of value function. We employ a suite of wavelet techniques, including Wavelet Transform (WT), Wavelet Transform Coherence (WTC), Multiple Wavelet Coherence (MWC), and Partial Wavelet Coherence (PWC), to decompose the risk structure of Bitcoin and analyze its relationship with various systematic risk factors. Our dataset spans from 13 August 2015 to 29 June 2024, and includes Bitcoin, major commodities, global and US equities, Shari’ah-compliant equities, Ethereum, and the Secured Overnight Financing Rate (SOFR). We find that Bitcoin’s risk profile is increasingly aligned with traditional financial assets, indicating growing market integration. While Bitcoin exhibits high volatility, a significant portion of this volatility can be attributed to systematic rather than idiosyncratic factors. This suggests that Bitcoin’s risk may be more diversifiable than previously thought. Our findings have important implications for monetary policy and financial regulation, challenging the notion that Bitcoin’s volatility precludes its use as money and suggesting that regulatory approaches should consider Bitcoin’s evolving risk characteristics and increasing integration with broader financial markets.
Many blockchain-based crowdsourcing frameworks currently struggle to address the high costs associated with on-chain storage and computation effectively, and they lack a quality-driven incentive mechanism tailored to bounding box annotation scenarios. To address these challenges, this paper proposes CrowdBA: A low-cost, quality-driven crowdsourcing architecture. The CrowdBA utilizes the Ethereum public blockchain as the foundational architecture and develops corresponding smart contracts. First, by integrating Ethereum with the InterPlanetary File System (IPFS), storage and computation processes are shifted off-chain, effectively addressing the high costs associated with data storage and computation on public blockchains. Additionally, the CrowdBA introduces a Dynamic Intersection over the union-weighted bounding box fusion (DWBF) algorithm, which assigns dynamic weights based on IoU to infer true bounding boxes, thereby assessing each worker’s annotation quality. Annotation quality then serves as a key criterion for incentive distribution, ensuring fair and appropriate compensation for all contributors. Experimental results demonstrate that the operational costs of each smart contract function remain within reasonable limits; the off-chain storage and computation approach significantly reduces storage and computation expenses, and the DWBF algorithm shows marked improvements in accuracy and robustness over other bounding box fusion methods.
Open access
Blockchain Technology Applications and Security
Mobile Crowdsensing and Crowdsourcing
Advanced Steganography and Watermarking Techniques
Irving Jared Villanueva, Madhusudan Srinivasan, Faqeer Ur Rehman
Blockchain smart contracts play a crucial role in automating and securing agreements in diverse domains such as finance, healthcare, and supply chains. Despite their critical applications, testing these contracts often receives less attention than their development, leaving significant risks due to the immutability of smart contracts post-deployment. A key challenge in the testing of smart contracts is the oracle problem, where the exact expected outcomes are not well defined, complicating systematic testing efforts.Metamorphic Testing (MT) addresses the oracle problem by using Metamorphic Relations (MRs) to validate smart contracts. MRs define how output should change relative to specific input modifications, determining whether the tests pass or fail. In this work, we apply MT to test an Ethereum-based crowdfunding smart contract, focusing on core functionalities such as state transitions and donation tracking.We identify a set of MRs tailored for smart contract testing and generate test cases for these MRs. To assess the effectiveness of this approach, we use the Vertigo mutation testing tool to create faulty versions of the smart contract. The experimental results show that our Metamorphic Relations (MRs) detected 25.65% of the total mutants generated, with the most effective MRs achieving a mutant-killing rate of 89%. These results highlight the utility of MT to ensure the reliability and quality of blockchain-based smart contracts.
The Internet and the World Wide Web were designed with the intention of being decentralised and interoperable, and therefore more democratic, where users have power over their data. Despite efforts to maintain this decentralisation, in recent years, the Internet has become a highly centralised network, thus ignoring the principles of decentralisation and democratisation on which it was designed. In response to this increasing centralisation of the Internet, different technologies have emerged that advocate the principles on which the Internet was built, such as the semantic web and blockchain. This thesis addresses the synergy between blockchain technology and the semantic web, two technologies that advocate the decentralisation of the Internet and give control of data back to users, allowing them to manage their own information in a secure way while both technologies have unique characteristics and can feed back on each other, either by offering the interoperability characteristic of the semantic web or the security of data provided by blockchain technologies. Therefore, this thesis explores and lays the groundwork for how the combination of these technologies can address the problem of decentralisation while offering solutions for transparent and interoperable data management. First, a state-of-the-art analysis of both technologies is performed, defining the main characteristics of the blockchain and the semantic web. The potential of blockchain to provide the decentralisation of the web and the immutability of records is analysed, as well as the different existing blockchain technologies, while the main standards and characteristics of the semantic web, such as interoperability and data understanding through ontologies and knowledge graphs, are analysed. Second, an analysis of the benefits that arise from the synergy between both technologies is made, and different prototypes of scenarios that integrate blockchain with the semantic web are proposed. Third, an analysis of the most promising scenarios is performed, and experiments are designed to evaluate the effectiveness and performance of the proposed scenarios. Fourth, ontology models are developed to cover Ethereum blockchain technology and smart contracts. Finally, a knowledge graph with these ontologies is built to demonstrate how the integration of these technologies can facilitate data management and improve blockchain analytics. In conclusion, this study lays the foundation for achieving a decentralised, standards-based, user-driven, data-driven Internet. Despite the benefits reflected in this thesis in achieving decentralisation of the Internet, the results obtained indicate that the integration of blockchain technology with the semantic web is feasible, but has certain limitations that need to be addressed, such as the cost of storing semantic data on the blockchain. RESUMEN El internet y la World Wide Web fueron diseñados con la intención de ser descentralizados e interoperables y, por lo tanto, más democráticos, donde los usuarios tuvieran el poder sobre sus datos. A pesar de los esfuerzos por mantener esta descentralización, en los últimos años Internet se ha vuelto una red altamente centralizada, ignorando así los principios de descentralización y democratización sobre los que fue diseñado. En respuesta a esta creciente centralización de Internet, han surgido diferentes tecnologías que abogan por los principios sobre los que se construyó Internet, como la web semántica y la blockchain. La presente tesis aborda la sinergia entre la tecnología blockchain y la web semántica, dos tecnologías que abogan por la descentralización de Internet y por devolver el control de los datos a los usuarios, permitiendo gestionar su propia información de manera segura, al mismo tiempo que ambas tecnologías tienen características únicas y pueden retroalimentarse entre sí, ya sea por ejemplo ofreciendo la interoperabilidad característica de la web semántica o la seguridad de los datos que proporcionan las tecnologías blockchain. Por lo tanto, esta tesis explora y sienta las bases de cómo la combinación de estas tecnologías pueden abordar el problema de la descentralización, ofreciendo además soluciones para la gestión transparente e interoperable de los datos. En primer lugar, se realiza un análisis del estado del arte de ambas tecnologías, definiendo las principales características de blockchain y la web semántica. Se analiza el potencial del blockchain para proporcionar la descentralización de la web y la inmutabilidad de los registros, así como se estudian las distintas tecnologías blockchain existentes, mientras que se analizan los principales estándares y características de la web semántica, como la interoperabilidad y la comprensión de los datos a través de ontologías y grafos de conocimiento. En segundo lugar, se realiza un análisis de los beneficios que surgen producto de la sinergia al combinar ambas tecnologías y se proponen diferentes prototipos de escenarios que integran blockchain con la web semántica. En tercer lugar, se realiza un análisis de los escenarios más prometedores y se realizan experimentos para evaluar la eficacia y rendimiento de los escenarios propuestos. En cuarto lugar, se desarrollan modelos ontológicos para cubrir la tecnología blockchain Ethereum y los contratos inteligentes. Por último, con estas ontologías se construyó un grafo de conocimiento para demostrar cómo la integración de estas tecnologías puede facilitar la gestión de datos y mejorar el análisis de la blockchain. En conclusión, este estudio sienta una base para alcanzar un Internet descentralizado, cuyos datos sean gestionados por los propios usuarios y esté basado en estándares. A pesar de los beneficios que se reflejan en esta tesis para alcanzar la descentralización de Internet, los resultados obtenidos indican que la integración de la tecnología blockchain con la web semántica es viable, pero tiene ciertas limitaciones que deben ser abordadas, como el coste de almacenar datos semánticos en blockchain.