Blockchain Papers

Quan Zou, Wenyang Yu, Ziwei Bao

A large number of raw data collected by satellites are processed by the production chain to obtain a large number of product data, of which the secure exchange and storage is of interest to researchers in the field of remote sensing information science. Authentic, secure data provide a critical foundation for data analysis and decision-making. Traditional centralized cloud computing systems are vulnerable to attack and, once the central server is successfully attacked, all data will be lost. Distributed ledger technology (DLT) is an innovative computer technology that can ensure information security and traceability, is tamper-proof, and can be applied to the field of remote sensing. Although there are many advantages to using DLT in remote sensing applications, there are some obstacles and limitations to its application. Remote sensing data have the characteristics of a large data volume, a spatiotemporal nature, global scale, and so on, and it is difficult to store and interconnect remote sensing data in the blockchain. To address these issues, this paper proposes a trustworthy and decentralized system using blockchain technology. The novelty of this paper is the proposal of a multi-level blockchain architecture in which the system collects remote sensing data and stores them in the Interplanetary File System (IPFS) network; after generating the IPFS hash, the network rehashes the value again and uploads it on the Ethereum chain for public query. The distributed data storage improves data security, supports the secure exchange of information, and improves the efficiency of data management.

Muhammad Mahmudul Karim, Md Hakim Ali, Larisa Yarovaya, Md Hamid Uddin · 5 authors

Implied volatility has consistently demonstrated its reliability as a superior estimator of the expected short-term volatility of underlying assets. In this study, we employ the newly constructed robust model-free implied volatility (MFIV) indices for Bitcoin and Ethereum (BitVol and EthVol) to explore the asymmetric return-volatility relationship of these cryptocurrencies through the lens of behavioral finance theories. Utilizing the asymmetric quantile regression model (QRM) and the Non-linear ARDL (NARDL) approach, our results reveal a notable difference from equities. Both positive and negative return shocks in the cryptocurrency market lead to an increase in volatility. However, during high volatility regimes, positive (negative) return shocks exert a more substantial impact on positive innovations of volatility for Bitcoin (Ethereum) compared to negative (positive) return shocks. The degree of asymmetry steadily intensifies as we progress from medium to uppermost quantiles of the volatility distribution. These observed phenomena can be attributed to behavioral aspects among market participants, including noise trading, behavioral biases, and fear of missing out (FOMO). Our findings hold significant implications for various aspects of cryptocurrency trading, portfolio hedging strategies, volatility derivatives pricing, and risk management.

Walter Hernández, Kamil Tylinski, Alastair Moore, Niall Roche · 9 authors

Abstract Distributed Ledger Technology (DLT) faces increasing environmental scrutiny, particularly concerning the energy consumption of the Proof of Work (PoW) consensus mechanism and broader Environmental, Social, and Governance (ESG) issues. However, existing systematic literature reviews of DLT rely on limited analyses of citations, abstracts, and keywords, failing to fully capture the field’s complexity and ESG concerns. We address these challenges by analyzing the full text of 24,539 publications using Natural Language Processing (NLP) with our manually labeled Named Entity Recognition (NER) data set of 39,427 entities for DLT. This methodology identified 505 key publications at the DLT/ESG intersection, enabling comprehensive domain analysis. Our combined NLP and temporal graph analysis reveals critical trends in DLT evolution and ESG impacts, including cryptography and peer-to-peer networks research’s foundational influence, Bitcoin’s persistent impact on research and environmental concerns (a “Lindy effect”), Ethereum’s catalytic role on Proof of Stake (PoS) and smart contract adoption, and the industry’s progressive shift toward energy-efficient consensus mechanisms. Our contributions include the first DLT-specific NER data set addressing the scarcity of high-quality labeled NLP data in blockchain research, a methodology integrating NLP and temporal graph analysis for large-scale interdisciplinary literature reviews, and the first NLP-driven literature review focusing on DLT’s ESG aspects.

Kuna Venkateswararao, Pravati Swain

Summary Small cell networks can fulfill the increasing demandfor the high data rate of wireless applications. Energy efficiency is an important design parameter of the ultra dense small cell network (UDSCN). The sleeping strategy of small base stations (s‐BSs) is used to enhance the network's energy efficiency. An efficient sleeping strategy of s‐BSs is required while preserving users' quality of service (QoS). The idle s‐BSs can be switched to sleep mode. This paper proposes a blockchain‐enabled solution for the sleeping strategy of s‐BSs. Here, a blockchain‐enabled small cell network is created between the s‐BSs. The network is decentralized, which eliminates the workload of the macro base station (MBS). The proposed network architecture is enabled as a decentralized network through blockchain. The blockchain provides distributed control over the s‐BS operations through a smart contract. Here, smart contracts act as distributed self organizing network features to handle self‐transactions among small cells for switching off s‐BSs in the network. All the software logic required to perform s‐BS operations is written in a smart contract using Ethereum. The proposed solution improves energy efficiency and enables the ultra dense small cell network to be decentralized.

Majd Soud, Grischa Liebel, Mohammad Hamdaqa

Context:Smart contracts are prone to numerous security threats due to undisclosed vulnerabilities and code weaknesses. In Ethereum smart contracts, the challenges of timely addressing these code weaknesses highlight the critical need for automated early prediction and prioritization during the code review process. Efficient prioritization is crucial for smart contract security. Objective:Toward this end, our research aims to provide an automated approach, PrAIoritize, for prioritizing and predicting critical code weaknesses in Ethereum smart contracts during the code review process. Method: To do so, we collected smart contract code reviews sourced from Open Source Software (OSS) on GitHub and the Common Vulnerabilities and Exposures (CVE) database. Subsequently, we developed PrAIoritize, an innovative automated prioritization approach. PrAIoritize integrates advanced Large Language Models (LLMs) with sophisticated natural language processing (NLP) techniques. PrAIoritize automates code review labeling by employing a domain-specific lexicon of smart contract weaknesses and their impacts. Following this, feature engineering is conducted for code reviews, and a pre-trained DistilBERT model is utilized for priority classification. Finally, the model is trained and evaluated using code reviews of smart contracts. Results: Our evaluation demonstrates significant improvement over state-of-the-art baselines and commonly used pre-trained models (e.g. T5) for similar classification tasks, with 4.82\%-27.94\% increase in F-measure, precision, and recall. Conclusion: By leveraging PrAIoritize, practitioners can efficiently prioritize smart contract code weaknesses, addressing critical code weaknesses promptly and reducing the time and effort required for manual triage.

Jiachi Chen, Mingyuan Huang, Zewei Lin, Peilin Zheng · 5 authors

With the increasing popularity of cryptocurrencies and blockchain technologies, smart contracts have become a prominent feature in developing decentralized applications. However, these smart contracts are susceptible to vulnerabilities that hackers can exploit, resulting in significant financial losses. In response to this growing concern, various initiatives have emerged. Notably, the Smart Contract Weakness Classification (SWC) list plays an important role in raising awareness and understanding of smart contract weaknesses. However, the SWC list lacks maintenance and has not been updated with new vulnerabilities since 2020. To address this gap, this paper introduces the Smart Contract Weakness Enumeration (SWE), a comprehensive and practical vulnerability list up until 2023. We collect 273 vulnerability descriptions from 86 top conference papers and journal papers, employing the open card-sorting method to deduplicate and categorize these descriptions. This process results in the identification of 40 common contract weaknesses, which are further classified into 20 sub-research fields through thorough discussion and analysis. The SWE provides a systematic and comprehensive list of smart contract vulnerabilities, covering existing and emerging vulnerabilities in the last few years. Moreover, the SWE is a scalable and continuously iterative program. We propose two update mechanisms for the maintenance of the SWE. Regular updates involve the inclusion of new vulnerabilities from future top papers, while irregular updates enable individuals to report new weaknesses for review and potential addition to the SWE.

İbrahim Yağlı, Özkan HAYKIR

The study aims to investigate the causality relationship between investor happiness and cryptocurrency returns. The study is focused on the five largest cryptocurrencies, specifically Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Ripple (XRP), and Cardano (ADA). Twitter-based Happiness Index is used to measure investor happiness. The sample period covers the period between January 1, 2019, and October 2, 2021. The Zivot-Andrews test is employed to detect stationary of covariates. After ensuring that all variables are stationary at levels, the Granger causality test is adopted to understand the relationship between the happiness index and cryptocurrency returns. The impulse-response functions are illustrated. The results indicate that there is a uni-directional relationship from BTC to Happiness Index, and Happiness Index to ETH. Considering that the causal relationship between cryptocurrency returns and investor happiness differs between cryptocurrencies, it is thought that investors should closely monitor the happiness index and make adjustments in their portfolios in response to changes in investor happiness.

Hutomo Sakti Kartiko, Tedy Rismawan, Ikhwan Ruslianto

Blockchain merupakan teknologi buku besar yang bersifat decentralized. Blockchain memiliki protokol consensus sebagai kesepakatan bersama dalam pengelolaan basis data. Contoh penerapan blockchain yaitu ethereum. Kelebihan ethereum yaitu dapat menjalankan program atau aturan yang disebut sebagai smart contract. Proses perubahan data pada ethereum memerlukan biaya transaksi atau gas fee. Nilai gas fee ini fluktuatif menyesuaikan gas fee terendah saat ini, kepadatan jaringan dan kompleksitas transaksi. Smart contract ethereum tidak efisien untuk menyimpan data yang berukuran besar karena semakin besar data yang disimpan maka semakin kompleks transaksi yang perlu dilakukan. Untuk meningkatkan efisiensi gas fee smart contract maka dilakukan sebuah penelitian dengan menerapkan InterPlanetary File System (IPFS). Teknik yang digunakan yaitu mengkombinasikan teknologi IPFS dengan smart contract ethereum untuk mengurangi kompleksitas transaksi ketika proses penyimpanan data penggalangan dana ke smart contract ethereum. Penerapan IPFS pada aplikasi penggalangan dana membutuhkan gas fee 0,00311847-0,003379868 ETH dengan kecepatan transaksi 12-36 detik. Berdasarkan pengujian sebanyak 40 kali dengan data yang berbeda, penerapan IPFS dapat menurunkan gas fee dengan rata-rata hingga 94,39% dan kecepatan transaksi sistem yang menerapkan IPFS lebih besar 13,55% dari sistem yang tidak menerapkan IPFS.

Birglang Bargayary, Nabajyoti Medhi

Abstract SDN revolutionises network management by providing a centralised controller that enables flexible and effortless configuration of networks. However, this flexibility also leads to a vulnerability that enables the adversary to trick the security system into allowing the installation of unauthorised flow rules in the switches. Blockchain provides us with a way to protect against malicious tampering with flow rules by storing them in the distributed ledger. In this work, we propose FTISCON, a mechanism to preserve the integrity of the OpenFlow flow table that utilizes blockchain technology. We employ the Ethereum Private Blockchain to implement the proof-of-concept and conduct a comparative analysis of the proposed scheme and existing related schemes, evaluating their performance in terms of delay, computation time, transaction cost, and detection rate. The proposed work is found to perform better in each of these. The study results suggest that the proposed approach offers a practical and efficient remedy to prevent flow modification attacks within SDN networks.

Yevhen Bublyk, Olena Borzenko, Anna Hlazova

The rapid spread of cryptocurrencies is one of the most relevant trends today. One of the significant risks of their spread is the increase in energy consumption, which has a negative impact on the environment due to carbon emissions. This requires the development of a scientific toolkit for assessing relationships and predicting the impact of cryptocurrencies on energy consumption, which is the aim of this paper.With the correlational regression analysis, the model of the dependence of spending on IT sector, energy consumption of Bitcoin, Ethereum and global capitalization of the cryptocurrency market was conducted, based on statistical data from Statista.com, Сoinmarketcap.com and International Data Corporation. To check the possible relationship, tests for the adequacy of the results obtained (Fisher’s test, Student’s t-test) confirmed the correctness of coefficients for independent variables.The results showed a significant direct correlation (Multiple R is 95%) of spending on IT sector, energy consumption and global capitalization of the cryptocurrency market. The established relationships allowed predicting that Bitcoin energy consumption may reach 142 Terawatt hours per year in 2026. And its impact on environment by mining in 2022 was at least 27.4 Mt of CO2 emission.As a proposal, a conclusion was made on the expediency of linking mining to the use of certain sources of electricity production, such as “residual” natural gas, nuclear power, renewable energy sources. The obtained results and conclusions may be used as a basis for political decisions in the field of energy efficiency and climate change mitigation.

ANDREI BOGDAN STANESCU, CATALIN VAJAIALA, Dragoş Cocîrlea

Abstract In the current digital world, ensuring efficient storage capabilities and increased data privacy, as well as high data availability and redundancy, are critical key performance indicators for organizations striving for customer excellence. To achieve these, a modern two-layer technical architecture is proposed in this study. The core layer of the solution is an InterPlanetary File System (IPFS) Cluster that leverages the distributed storage concept, and the second is an Ethereum-based blockchain that leverages privacy and immutability mechanisms. Next, the two-layer architecture is implemented and deployed to enhance data protection, as well as optimize data storage and access for a mid-size organization. The results reveal the enhancement of IPFS to overcome its privacy concerns via role-based access and cryptographic techniques. Moreover, the benefits of utilizing IPFS for data redundancy, efficient storage, and transfer through its distributed nature were reported. Finally, the integration of the IPFS Cluster with the Ethereum-based blockchain, as well as the overall benefits, we described.

Osama Liaqat, Kehkashan Nizam, Jahanzaib Alvi, Arbab Muhammad Jehandad

This research analyzes the price-setting behaviors of Ethereum, a digital currency and alternative payment system that has gained significant attention from investors. By employing symmetric and asymmetric causality tests, the study identifies the link between Ethereum's return, price, and volume from August 7, 2015, to November 30, 2020. The data obtained from CoinMarketCap.com consists of 1,940 observations suitable for analysis. The research employs ARDL, ARDL bound tests, and VECM to achieve its objectives. The findings indicate a significant and positive association between Ethereum's return, price, and trading volume in the short and long run. This suggests that as Ethereum's price and trading volume increase, it attracts more investors and leads to higher returns.

Kia Jahanbin, Mohammed Ali Zare Chahooki

OBJECTIVES: With the expansion of social networks such as Twitter, many experts share their opinions on various topics. The opinions of experts, who are also known as influencers, can be very influential. Combining these tweets and the historical prices of cryptocurrencies makes it possible to predict their price trends accurately. A Hybrid of RoBERTa deep neural network and BiGRU has been used for Sentiment Analysis (SA). Sentiments of tweets can be of great help to investors to understand the future behavior of the market and manage the stock portfolio. Unlike the tweets that are only extracted using the cryptocurrency name hashtag, the tweets of this dataset have specialized opinions and can determine the market trend. DATA DESCRIPTION: The dataset created in this research concerns the opinions of more than 52 influencers (persons or companies) regarding eight cryptocurrencies. This dataset was collected through the Apify Twitter API for eight months, from February 2021 to June 2023. This dataset contains five Excel files and tweets, compound score, importance coefficient of each tweet, sentiment polarity, and historical prices of four cryptocurrencies: Bitcoin, Ethereum, Binance, and other information. These tweets cover the opinions of 52 influencers on more than 300 cryptocurrencies, although most comments are related to Bitcoin, Ethereum, and Binance. For this reason, three Excel files containing the historical prices of polarity and compound sentiment related to Bitcoin, Ethereum, and Binance cryptocurrencies have been placed separately in the dataset. The polarity of sentiment in these Excel shows the maximum number of polarities by applying the importance coefficient, which determines the dominant polarity of sentiment related to a particular day for the cryptocurrency.

Eisuke Tomiyama, Hiroshi Esaki, Hideya Ochiai

In recent years, machine learning models have evolved, and the training of these models requires large amounts of data. However, the training data often contains sensitive information, raising privacy concerns. Federated Learning has been proposed as a solution to mitigate privacy risks. Despite its advantages, Federated Learning still faces challenges such as the aggregator being a single point of failure, the existence of malicious participants, and the lack of incentives. Combining Federated Learning with blockchain technology could potentially address these challenges. In this study, we propose a new method for asynchronous Federated Learning using blockchain smart contracts. Our proposed method operates autonomously and in a decentralized manner without the need to trust any central organization, making it trustless. We propose an algorithm that motivates workers to submit high-quality models as quickly as possible. Workers’ behaviors are driven by incentive mechanisms. We deployed a smart contract on a local Ethereum blockchain and executed multiple workers. Our evaluation results demonstrate that learning converges and achieves accuracy comparable to conventional Federated Learning, indicating the effectiveness of our proposed method.

Md. Mamunur Rashid, Piljoo Choi, Suk‐Hwan Lee, Jan Platoš · 6 authors

The Internet of Things (IoT) has become a game-changing technology, bridging the gap between the real and virtual worlds and allowing for smooth data transfer and communication between linked objects. The other two potential technologies are blockchain (BC) and artificial intelligence (AI), whose application areas are incredibly diverse and which may perform best when combined. Since some traditional machine learning (ML) techniques have limitations, this article proposed using distributed machine learning techniques such as federated learning and blockchain to build a more reliable and secure IoT network that will be better protected and less susceptible to outside intrusions. As an alternative to centralized cloud storage, we also recommended using decentralized data storage techniques like the InterPlanetary File System (IPFS) and Hyperledger Fabric (HLF). Additionally, as a proof of concept, we deployed our model using Ethereum Smart Contracts (SC). Using the well-known cybersecurity dataset known as Edge-IIoTset, we utilized both centralized and federated machine learning models to evaluate the efficiency of the suggested approach. The experimental results and successful deployment of Smart Contracts demonstrate that employing Blockchain and distributed storage systems is preferable for safeguarding IoT networks.

Cheick Tidiane Bâ, Matteo Zignani, Sabrina Gaito

The emergence of the Web3 paradigm has led to more and more systems built on blockchain technology and relying on cryptocurrency tokens – both fungible and non-fungible – to sustain themselves and generate profit. The growth and success of these platforms are strongly dependent on the growth and evolution of the trade relationships among users. In this context, it is of paramount importance to understand the mechanism behind the evolution and growth dynamics of these economic ties: however, in these systems the trade relationships are strictly intertwined with social dynamics, posing significant challenges in the analysis. One of the most important mechanisms behind the evolution of social networks is the triadic closure principle: given the strict link between social and economic spheres, the mechanism emerges as a potential candidate among mechanisms in literature. Therefore in this work, we extend the existing methodology for triadic closure studies and adapt it to directed networks. We performed an analysis centered around 3-node subgraphs known as “triads” and statistically significant triads referred to as “triadic motifs”, both from a static and temporal perspective. The methodology was applied to various decentralized socio-economic networks with distinct levels of social components. These networks include currency transfers from the blockchain-based online social media platform Steemit, trade relationships among NFT sellers and buyers on the Ethereum blockchain, and a blockchain-based currency designed for humanitarian aid called Sarafu. Our measurements show how triadic closure is relevant during the evolution of these platforms and, for a few aspects, more impactful than centralized online social networks, where triadic closure is also incentivized by recommendation systems. Moreover, we are able to highlight both similarities and differences across networks with different levels of social components, both from a static and temporal standpoint. Overall our work presents strong evidence that triadic closure is an important evolutionary mechanism in decentralized socio-economic networks. Our findings provide a stepping stone in the study of decentralized socio-economic networks. Understanding the evolution of other decentralized networks, not following the same Web3 paradigm or with different social components will provide valuable insight into the understanding of dynamics in decentralized systems and potentially improve their design process.

Tri Nguyen, Huong Nguyen, Juha Partala, Susanna Pirttikangas

Mobility-as-a-Service (MaaS) is an advanced Intelligent Transport System (ITS) that integrates various modes of transportation to meet the demands of travellers. The system relies on frequent communication for data exchange between the MaaS provider and transport service providers. Ideally, such communication would utilize trust technologies between these entities. However, current MaaS systems lack transparency and reliability, and their centralized nature creates a single point of failure for the entire service. To address these issues, this paper proposes a blockchain-based MaaS, which includes an architecture and smart contract functionalities. The solutions are built on permissioned and permissionless Hyperledger Fabric and Ethereum blockchain platforms, respectively, for a realistic deployment of network architecture and proposed smart contracts. Additionally, the paper presents a framework derived from comparing these two blockchain platforms. Finally, the framework is evaluated, and open questions and challenges are analyzed.

Adeyinka Adediran, Bola Babajide, Nataliia Osina

No abstract is available for this record.

Burak Öz, Filip Rezabek, Jonas Gebele, Felix Hoops · 5 authors

Maximal Extractable Value (MEV) has become a significant incentive on blockchain networks, referring to the value captured through the manipulation of transaction execution order and strategic issuance of profit-generation transactions. We argue that transaction ordering techniques used for MEV extraction in blockchains where fees can influence the execution order do not directly apply to blockchains where the order is determined based on transactions' arrival times. Such blockchains' First-Come-First-Served (FCFS) nature can yield different optimization strategies for entities seeking MEV, known as searchers, requiring further study. This paper explores the applicability of MEV extraction techniques observed on Ethereum, a fee-based blockchain, to Algorand, an FCFS blockchain. Our results show the prevalence of arbitrage MEV getting extracted through backruns on pending transactions in the network, uniformly distributed to block positions. However, on-chain data do not reveal latency optimizations between specific MEV searchers and Algorand block proposers. We also study network clogging attacks and argue how searchers can exploit them as a viable ordering technique for MEV extraction in FCFS networks.

Javier Arcenegui, Rosario Arjona, Iluminada Baturone

The rental of houses is a common economic activity. However, there are many inconveniences that arise when renting a property. The lack of trust between the landlord and the tenant due to fraud or squatters makes it necessary to involve third parties to minimize risk. A blockchain (such as Ethereum) provides an ideal solution to act as a low-cost intermediary. This paper proposes the use of non-fungible tokens (NFTs) based on ERC-4519 for smart home tokenization. The ERC-4519 is an Ethereum standard for describing NFTs tied to physical assets, allowing smart homes (assets) to be linked to NFTs so that the smart homes can interact with the blockchain and perform transactions, know their landlord (owner) and assigned tenant (user), whether they are authenticated or not, and know their operating mode (NFT state). The payments associated with the rental process are made using the NFT, eliminating the need for additional fungible tokens and simplifying the process. The entire rental process is described and illustrated with a proof of concept using a Pycom Wipy 3.0 as a smart home gateway and a smart contract programmed in Solidity, which is deployed on the Goerli Testnet for Ethereum. Experimental results show that the smart home gateway takes a few tens of milliseconds to complete a transaction, and the transaction costs of the relevant functions of the smart contract are quite affordable.

Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne

No abstract is available for this record.

Bilel Sanhaji, Julien Chevallier

Using the capital asset pricing model, this article critically assesses the relative importance of computing ‘realized’ betas from high-frequency returns for Bitcoin and Ethereum—the two major cryptocurrencies—against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas < 1 for Bitcoin and Ethereum with respect to the CRIX market index, which could enhance portfolio diversification (at the expense of maximizing returns). We flag the minimal tracking errors at the hourly and daily frequencies. The dispersion of rolling betas is higher for the weekly frequency and is concentrated towards values of β > 0.8 for BTC (β > 0.65 for ETH). The weekly frequency is thus revealed as being less precise for capturing the ‘pure’ systematic risk for Bitcoin and Ethereum. For Ethereum in particular, the availability of high-frequency data tends to produce, on average, a more reliable inference. In the age of financial data feed immediacy, our results strongly suggest to pension fund managers, hedge fund traders, and investment bankers to include ‘realized’ versions of CAPM betas in their dashboard of indicators for portfolio risk estimation. Sensitivity analyses cover jump detection in BTC/ETH high-frequency data (up to 25%). We also include several jump-robust estimators of realized volatility, where realized quadpower volatility prevails.

Abhay Pratap Singh, A. Sri Ganesh, Rutuja Rajendra Patil, Sumit Kumar · 6 authors

Voting is a democratic process that allows individuals to choose their leaders and voice their opinions. However, the current situation with physical voting involves long queues, paper-based ballots, and security challenges. Blockchain-based voting models have appeared as a method to address the limitations of traditional voting methods. As blockchain is distributed and decentralized, which uses hash functions for securing transactions, it dramatically improves the existing voting system. These digital platforms eliminate the need for physical presence, reduce paperwork, and ensure the integrity of votes through transparent and tamper-proof blockchain technology. This paper introduces a blockchain-based voting model to enhance accessibility, security, and efficiency in the voting process. The research focuses on developing a robust and user-friendly voting system by leveraging the advantages of decentralized technology. The proposed model employs Ethereum as the underlying blockchain platform through an innovative and iterative approach. The model uses Smart contracts to record and validate votes, while AI-based facial recognition technology is integrated to verify the identity of voters. Rigorous testing and analysis are conducted to validate the effectiveness and reliability of the proposed blockchain-based voting model. The system underwent extensive simulation scenarios and stress tests to evaluate its performance, security, and usability.

T.B. Tommaso Baldo, Mauro Migliardi

Abstract Online presence is becoming an important part of everyday's life and online communities may represent a significant source of engagement for the elderlies. Nevertheless, many may struggle to be online due to a lack of expertise, and a decentralised architecture may provide a solution by removing intermediaries, such as a webmaster, while not requiring expensive cloud solutions. However, issues concerning accessibility, security, and user experience have to be tackled. The paper focuses mainly on three issues: providing a human‐readable domain, moderating content, and creating a reward system based on user reputation. An architecture is proposed based on Ethereum and Swarm. Smart contracts provide an automated set of rules to handle enterprise registration, content creation, and decision‐making process, while Swarm serves both as distributed storage and the web host. Besides, in combination with Ethereum Name Service, Swarm provides a secure, distributed, and human‐readable point of access to the web interface. The paper also describes an innovative two‐token system where one token is meant to be a trustworthy reputation metre and the other is a spendable coin to get rewards. The final result is a fully decentralised, authenticated and moderated platform where users can aggregate and share their content presentations on the Internet.