Carlos Melo, Glauber Gonçalves, Francisco Airton Silva, Iure Fé · 10 authors
Blockchain, often integrated with distributed systems and security enhancements, has significant potential in various industries. However, environmental concerns and the efficiency of consortia-controlled permissioned networks remain critical issues. We use a Stochastic Petri Net model to analyze transaction flows in Hyperledger Fabric networks, achieving a 95% confidence interval for response times. This model enables administrators to assess the impact of system changes on resource utilization. Sensitivity analysis reveals major factors influencing response times and throughput. Our case studies demonstrate that block size can alter throughput and response times by up to 200%, underscoring the need for performance optimization with resource efficiency.
David Lee Kuo Chuen, Yang Li, Weibiao Xu, Willy Zhao
Proponents of blockchains believe that this technology will revolutionize e-commerce. To evaluate this belief, we invite several groups of students to transact on a decentralized peer-to-peer marketplace built on the platform provided by Origin Protocol Inc., and then we conduct a survey about their experience of usage. Based on our survey results, we find that 33% of respondents play tricks on others, which implies that this undesirable result may hinder the widespread adoption of blockchain technologies. We also attempt to propose a conceptual mechanism to mitigate fraudulent behaviors. In the event of disputation, a trusted authority is entitled to the right to downgrade the fraudulent side's credit record, which is stored by a permissioned blockchain accessed only by the authority. Such a punishment can effectively decrease agents' incentives to sell counterfeits and leave fake ratings. In sum, we must distinguish what we proposed blockchains will do and what blockchains can do before enabling this technology in e-commerce.
Ethereum has been a cornerstone of the decentralized ecosystem, with rollup-based scaling solutions like Arbitrum and Optimism significantly expanding its capabilities. These rollups enhance scalability and foster innovation, but their rapid proliferation has introduced \emph{liquidity fragmentation}. Specifically, tokens distributed on multiple rollups fragment the liquidity of users, complicating participation in trading and lending activities bound by minimum liquidity thresholds. This paper proposes UAT20, a universal abstract token standard, to address liquidity fragmentation across rollups. Leveraging Conflict-free Replicated Data Types (CRDTs), UAT20 ensures consistent states across multiple rollups. We introduce a two-phase commit protocol to resolve transaction conflicts, enabling seamless and secure liquidity unification. Finally, our empirical analysis demonstrated the necessity and effectiveness of UAT20 in mitigating liquidity fragmentation within Rollups.
Amir Hasan Hamadeh, Rasha Mohammad Nouraldeen, Rasha Mahboub, Mohamed Hashem
The purpose of this study is to examine the effect of the two determinants of the technology acceptance model (TAM3), perceived ease of use (PEOU), and perceived usefulness (PU) on auditors’ intention to adopt and use blockchain technology (BT) in Lebanon. This study also aims to investigate the moderating role of age on these associations to determine the antecedents of PU and PEOU. A sample of 332 auditors working in Lebanon was used to collect data and the analysis was conducted using the third version of partial least squares structural equation modeling (PLS3-SEM). Results show that perception of external control and computer self-efficacy significantly affect the PEOU. Job relevance and output quality are PU antecedents and positively influence the variable. In addition, PEOU and PU have a significant positive impact on auditors’ intention to adopt BT. This shows that auditors in Lebanon are more inclined to adopt BT if they feel that BT does not require substantial effort and that BT provides tangible benefits to their work. According to the researchers’ knowledge, this study is the first to examine auditors’ perception of using BT in one of the Middle Eastern countries, Lebanon, and the first to investigate the moderating role of age on the relationship between TAM3 determinants and auditors’ intention to adopt BT. In addition, this study highlights the practical implications of adopting BT in auditing in Lebanon by pinpointing the need for training programs, collaboration between auditors and other departments, developing regulatory frameworks to enhance efficiency, and organizing awareness and educational campaigns. Additionally, investments in infrastructure are critical to facilitate the smooth implementation and adoption of BT. Furthermore, audit firms should organize workshops to educate auditors on the application and the benefits of BT, invest in upgrading the IT systems to be compatible with BT platforms, and provide case studies and pilot projects to promote confidence in BT adoption.
Feng Chen, Chunjiang Zhao, Xinting Yang, Na Luo · 5 authors
Agri-food safety issues have received widespread attention globally. The emergence of blockchain technology (BCT) effectively addresses trust issues in the agri-food supply chain traceability system (AFSCTS). However, the append-only feature of blockchain has led to continuous linear data growth in BCT-based AFSCTSs, which increases the equipment requirements and has become a bottleneck for BCT-based AFSCTS applications. The storage capacity required by BCT-based AFSCTSs can be effectively reduced by deleting expired data, thereby reducing the storage pressure on blockchain devices and lowering the device requirements. In this paper, we propose an AFSCTS architecture that incorporates redactable blockchain and InterPlanetary file system (IPFS) technologies to achieve traceability with low storage pressure, using the wheat supply chain as a proof of concept. Firstly, the key links were analyzed in agri-food traceability and the demand was proposed for agri-food blockchain traceability based on the timeliness of traceability data. Secondly, a lightweight accountable parallel blockchain architecture called LAP-chain is proposed. This architecture utilizes redactable blockchain technology to offload expired agri-food traceability data to IPFS, thereby reducing the storage pressure on blockchain devices and ensuring data accountability through IPFS. Finally, we evaluate the correctness, collision resistance, and storage performance of the LAP-chain built on the Ethereum private chain. The results show that when expired agri-food traceability data are permanently retained, the storage capacity of the proposed architecture is only 52.38% of that of the traditional blockchain traceability architecture, after running continuously for 36 months. When traceability data of expired agri-food are deleted in accordance with the food laws and regulations of various countries, the storage capacity of the proposed architecture can be reduced from a linear level to a constant level compared to the traditional blockchain traceability architecture. The proposed architecture has the potential to contribute to improving the safety and quality of agri-food.
The advent of quantum computing poses an existential threat to contemporary cryptographic standards, particularly those securing decentralized blockchain networks and cloud infrastructures.Classical public-key cryptosystems such as RSA, ECC, and DH, which rely on factorization and discrete logarithm problems, are rendered obsolete by Shor's algorithm, necessitating the transition toward post-quantum cryptographic (PQC) solutions.This study explores the integration of PQC algorithms, including lattice-based, hash-based, code-based, multivariate, and isogeny-based cryptographic mechanisms, within blockchain-ledger technologies and cloud architectures to ensure long-term security against quantum adversaries.A comparative analysis is conducted to evaluate computational efficiency, key size implications, communication overhead, and security resilience under quantum attack models.The research highlights the adaptation of PQC within blockchain consensus mechanisms, smart contract execution, and cryptographic primitives such as digital signatures, zero-knowledge proofs, and secure multi-party computation (MPC).Additionally, it examines the impact of PQC on cloud security, addressing challenges in quantum-safe key exchange protocols, homomorphic encryption for secure computations, and cross-platform interoperability within hybrid quantum-classical cloud ecosystems.Real-world implementations and benchmarking data provide insights into the feasibility of large-scale adoption, shedding light on standardization efforts by NIST and industry consortia.The study concludes with future directions, emphasizing the need for efficient PQC algorithm optimization, lightweight cryptographic frameworks for IoT-driven blockchain applications, and scalable post-quantum identity management systems.By establishing quantum-resistant security frameworks, this research underscores the imperative need for early adoption to mitigate cryptographic vulnerabilities in the impending post-quantum era.
Optimizing the tax business environment is of crucial significance for enhancing enterprise innovation efficiency and fostering sustainable development. This study utilizes the dataset of Chinese A-share listed companies from 2013 to 2022. By leveraging the quasi-natural experiment of the “Decentralization, Management and Service” reform pilot in the tax system, it adopts the multi-period difference-in-differences method to empirically investigate the impact of tax business environment optimization on enterprise innovation efficiency. The research reveals that the “Decentralization, Management, and Service” reform in the tax system exerts a significantly positive influence on enterprise innovation efficiency, and this result remains robust after a series of robustness tests. This optimization promotes enterprise innovation efficiency through three main channels: alleviating financing constraints, reducing transaction costs, and enhancing digitalization levels. The promoting effect is more pronounced for enterprises in mid-western regions, non-state-owned enterprises, large-scale enterprises, and those with high innovation endowments. Furthermore, it further contributes to the improvement of enterprise total factor productivity and the expansion of business scale, achieving the coordinated objectives of enhancing enterprise quality and efficiency and strengthening development momentum. These findings deepen our understanding of the economic implications of optimizing the tax business environment and offer empirical evidence for creating a conducive tax environment for the development of market entities.
Cross-border transactions are critical to global trade and economic integration, yet traditional financial systems face significant challenges, including high costs, lengthy processing times, lack of transparency, and security risks. This study explores the role of blockchain in enhancing cross-border transactions, offering a comparative analysis between emerging and developed markets. By leveraging decentralized ledger technology (DLT), smart contracts, and cryptographic security, blockchain presents a transformative solution to address inefficiencies in international payments. Using a mixed-methods research approach, this study analyses primary data from industry experts and financial institutions alongside secondary data from regulatory reports, academic literature, and blockchain adoption case studies. Findings indicate that blockchain reduces transaction times from multiple days to minutes, with transaction fees dropping from 10% to less than 2%. The technology also enhances security by reducing fraud risks and eliminating chargeback disputes. However, blockchain adoption varies across economies. Developed markets prioritize institutional integration and regulatory compliance, while emerging markets focus on financial inclusion, remittances, and decentralized finance (DeFi) solutions. Despite its advantages, regulatory uncertainty, scalability limitations, and cybersecurity concerns hinder widespread adoption. Moreover, the emergence of Central Bank Digital Currencies (CBDCs) presents a competing framework for digital payments. This study concludes that blockchain has the potential to revolutionize cross-border transactions, provided that regulatory harmonization, interoperability, and cybersecurity risks are effectively managed. Future research should explore the impact of CBDCs, blockchain scalability, and privacy regulations to ensure sustainable adoption in the global financial landscape.
Fani Budi Kartika, Muhammad Ihsan, Bambang Indra Gunawan, Muhsin Lambok Ilvira
The Non-Fungible Token (NFT) market has grown rapidly as one of the innovations in the digital economy, opening up new opportunities for creators to trade their works globally. However, this development also presents significant challenges related to copyright protection and dispute resolution in blockchain-based digital transactions. This study aims to analyze Indonesia's legal regulations in overseeing the NFT market, with a focus on copyright protection and dispute resolution mechanisms. The research method used is a normative juridical approach with a literature analysis of existing regulations, such as Law No. 28 of 2014 on Copyright. The results show that legal regulations in Indonesia have not fully accommodated blockchain technology and NFT transactions, thus creating legal loopholes that increase the risk of copyright infringement. In addition, the existing oversight and dispute resolution mechanisms are still conventional in nature, which is less effective for handling the complexity of the NFT market. To address these challenges, this study recommends the establishment of specific regulations governing copyright protection in the NFT market, the development of a technology-based surveillance system, and the implementation of a digital dispute resolution mechanism. The implication of this research is the importance of collaboration between the government, creator community, and technology platforms to create a healthy, safe, and competitive NFT market ecosystem. The findings are expected to be the basis for strengthening regulations and increasing legal protection on copyrighted works transactions in the NFT market in Indonesia.
With the use of IoT devices and blockchain technologies, consumers can purchase and sell energy straight from the grid instead of going via retailers. Here, existing research proposed a various model but it has several limitations such as scalability, less optimal, and efficiency. Therefore, this research proposed a novel Decentralized Energy Trading Framework via Block-chain Smart Contracts. Initially, we obtain a dataset, which consists of total incoming energy (from producers), total outgoing energy (to consumers), price for incoming energy, and price for outgoing energy. Then, this research proposed a Concatenate Hash approach, to generate private and public keys to enhance security and efficiency. Following that, this research proposed a Weighted Scoring Technique for energy trading here Deputized Proof of Stake (DPOS) is used to verify the integrity of blocks which increases scalability and efficiency by reducing the number of participants in the consensus process. Then, for energy trading the consumer could calculate a score for each prosumer using a weighted average considering both energy availability and price. Moreover, Profit generating is the primary factor used to encourage customers and buyers to take part in suggested P2P energy trading. Consequently, Income Generation based on an active pricing strategy was proposed by this research. As a result, our proposed approach has less latency, higher throughput, and security.
The medical supply chain faces challenges like counterfeit drugs, lack of transparency, and inefficient traceability. Blockchain technology, with its decentralized and secure architecture, offers a promising solution. By implementing customized smart contracts, stakeholders can ensure data integrity, improve trust, and streamline supply chain operations, enhancing overall efficiency and reliability. Counterfeit medicines and limited visibility across the medical supply chain pose significant risks to patient safety and public health. Traditional systems lack robust mechanisms to verify authenticity or ensure transparency. The need for a secure, efficient, and scalable system to trace and validate medicines in real-time has become crucial. A blockchain-based framework was developed using customized smart contracts for traceability. Each medicine is registered with unique identifiers stored on the blockchain. QR codes link physical products to the digital ledger, enabling real-time tracking and user verification. The system uses consensus mechanisms to validate transactions and ensure authenticity. Proof of Work (PoW) ensures robust security by requiring significant computational effort for transaction validation, deterring malicious activities. Proof of Stake (PoS) enhances scalability and energy efficiency, validating transactions based on a node's stake in the network. Together, these algorithms provide a balance of security, speed, and sustainability. The proposed system significantly improves traceability and transparency in the medical supply chain. Counterfeit detection rates increased, and transaction validation times decreased due to optimized algorithms. User feedback confirmed enhanced trust and reliability. The solution demonstrates scalability and adaptability for broader applications in healthcare and beyond.
The proliferation of counterfeit academic certificates has engendered unethical practices, thereby depriving meritorious candidates of potential opportunities; this situation subsequently renders conventional document ver- ification methodologies ineffective due to their inherent time-consuming nature, high costs, and susceptibility to manipulation. In response to these pressing challenges, this paper advocates for a blockchain-based decentralized document verification framework that leverages the InterPlanetary File System (IPFS) and Ethereum blockchain, thereby enhancing security, transparency, and operational efficiency. The system adheres to a meticulously struc- tured methodology whereby applicants initially submit their credentials, which are subsequently authenticated by educational institutions prior to their storage within the IPFS for decentralized file management; concurrently, only the hash of these credentials is retained on the blockchain, effectively reducing costs and augmenting scalability. To assess the system’s efficacy, it was subjected to rigorous testing employing multiple concurrent mechanisms, including Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance, with findings demonstrating that Proof of Stake offers the most optimal equilibrium between velocity and security. The prototype that was developed showcased significant improvements, with verification accuracy, reduced the processing time also minimized the manual effort, furthermore, making the process significantly more efficient than traditional methods. Furthermore, the system accomplished a transaction throughput of 1000 transactions per second, accompanied by an average confirmation time of 5 seconds, thus significantly enhancing operational efficiency for employers and institutions engaged in credential verification. Additionally, a thorough comparative analysis was conducted against traditional methodologies concerning security, processing velocity, and cost-effectiveness, ensuring validation through tamper- proof mechanisms and the mitigation of fraud risks associated with certificates. This research not only bolsters the reliability of document verification but also lays the groundwork for future advancements, including cross-chain integration, AI-driven fraud detection systems, and mobile-based verification applications, all of which aspire to further optimize efficiency and accessibility within academic credential verification processes.
Ajo Dian Yusandika, Alamgir Hossain Bhuiyan, Partho Sarothi Laskar
The rapid expansion of decentralized exchanges (DEXs) has reshaped cryptocurrency trading, yet comparative cross-chain evaluations remain scarce, particularly in transaction efficiency, liquidity sustainability, and economic viability. This study conducts an on-chain comparative analysis of Uniswap (Ethereum), Raydium (Solana), and PancakeSwap (BSC) by examining transaction count, trade volume, protocol revenue, and total value locked (TVL). Using real-time blockchain data from DefiLlama, Dune Analytics, Artemis, Token Terminal, and Arkham, this research applies quantitative cross-chain modeling to assess the performance of each ecosystem. Findings reveal that Ethereum leads in trade volume and liquidity depth, driven by institutional adoption despite high transaction costs. Solana exhibits superior transaction efficiency, attracting high-frequency traders, though its lower TVL suggests liquidity retention challenges. BSC, once a major DeFi player, now faces stagnation, with declining trade volume and fragmented liquidity. Unlike prior studies focusing solely on transaction metrics, this research integrates protocol revenue as a sustainability indicator, offering a broader understanding of DEX viability. The results underscore the need for cross-chain liquidity bridges and Layer-2 scaling solutions to mitigate liquidity fragmentation and enhance DeFi efficiency. This study contributes to both academia and industry by providing empirical insights for blockchain developers, investors, and policymakers, emphasizing the importance of scalability, liquidity incentives, and revenue optimization. The findings support future research on multi-chain liquidity integration, AI-driven market-making, and governance frameworks, guiding the next evolution of decentralized financial ecosystems.
Carlos Melo, Glauber Gonçalves, Francisco A. Silva, Leonel Feitosa · 9 authors
Hyperledger Fabric stands as a leading framework for permissioned blockchain systems, ensuring data security and auditability for enterprise applications. As applications on this platform grow, understanding its complex configuration concerning various blockchain parameters becomes vital. These configurations significantly affect the system's performance and cost. In this research, we introduce a Stochastic Petri Net (SPN) model to analyze Hyperledger Fabric's performance, considering variations in blockchain parameters, computational resources, and transaction rates. We provide case studies to validate the utility of our model, aiding blockchain administrators in determining optimal configurations for their applications. A key observation from our model highlights the block size's role in system response time. We noted an increased mean response time, between 1 to 25 seconds, due to variations in transaction arrival rates.
Carlos Melo, Glauber Gonçalves, Francisco A. Silva, André Soares
Blockchain is a paradigm derived from distributed systems, protocols, and security concepts. However, can blockchain applications provide services in industrial environments, especially concerning performance issues? In blockchains, long response times can impair both user and service experience, and intensive resource use may increase the costs of service provision. The proposed paper tries to answer this question by evaluating the performance of one of the most popular permissioned blockchain platforms, the Hyperledger Fabric (HLF). We provide a framework for performance evaluation based on modeling and experimentation. The results indicate that block size and arrival rate can compromise throughput (by -70%), latency (by +1,500%), and environment utilization (by +28%) and that multiple gateways can reduce latency (by -75%), and throughput (by -60%)
Recent research in consensus has often focussed on protocols for State-Machine-Replication (SMR) that can handle high throughputs. Such state-of-the-art protocols (generally DAG-based) induce undue overhead when the needed throughput is low, or else exhibit unnecessarily-poor latency and communication complexity during periods of low throughput. Here we present Morpheus Consensus, which naturally morphs from a quiescent low-throughput leaderless blockchain protocol to a high-throughput leader-based DAG protocol and back, excelling in latency and complexity in both settings. During high-throughout, Morpheus pars with state-of-the-art DAG-based protocols, including Autobahn. During low-throughput, Morpheus exhibits competitive complexity and lower latency than standard protocols such as PBFT and Tendermint, which in turn do not perform well during high-throughput. The key idea of Morpheus is that as long as blocks do not conflict (due to Byzantine behaviour, network delays, or high-throughput simultaneous production) it produces a forkless blockchain, promptly finalizing each block upon arrival. It assigns a leader only if one is needed to resolve conflicts, in a manner and with performance not unlike Autobahn.
Price Trend Prediction (PTP) based on Limit Order Book (LOB) data is a fundamental challenge in financial markets. Despite advances in deep learning, existing models fail to generalize across different market conditions and assets. Surprisingly, by adapting a simple MLP-based architecture to LOB, we show that we surpass SoTA performance; thus, challenging the necessity of complex architectures. Unlike past work that shows robustness issues, we propose TLOB, a transformer-based model that uses a dual attention mechanism to capture spatial and temporal dependencies in LOB data. This allows it to adaptively focus on the market microstructure, making it particularly effective for longer-horizon predictions and volatile market conditions. We also introduce a new labeling method that improves on previous ones, removing the horizon bias. We evaluate TLOB's effectiveness across four horizons, using the established FI-2010 benchmark, a NASDAQ and a Bitcoin dataset. TLOB outperforms SoTA methods in every dataset and horizon. Additionally, we empirically show how stock price predictability has declined over time, -6.68 in F1-score, highlighting the growing market efficiency. Predictability must be considered in relation to transaction costs, so we experimented with defining trends using an average spread, reflecting the primary transaction cost. The resulting performance deterioration underscores the complexity of translating trend classification into profitable trading strategies. We argue that our work provides new insights into the evolving landscape of stock price trend prediction and sets a strong foundation for future advancements in financial AI. We release the code at https://github.com/LeonardoBerti00/TLOB.
Jihye Choi, Sai Rahul Rachuri, Ke Wang, Somesh Jha · 5 authors
Federated Learning (FL) enables collaborative model training while keeping client data private. However, exposing individual client updates makes FL vulnerable to reconstruction attacks. Secure aggregation mitigates such privacy risks but prevents the server from verifying the validity of each client update, creating a privacy-robustness tradeoff. Recent efforts attempt to address this tradeoff by enforcing checks on client updates using zero-knowledge proofs, but they support limited predicates and often depend on public validation data. We propose SLVR, a general framework that securely leverages clients' private data through secure multi-party computation. By utilizing clients' data, SLVR not only eliminates the need for public validation data, but also enables a wider range of checks for robustness, including cross-client accuracy validation. It also adapts naturally to distribution shifts in client data as it can securely refresh its validation data up-to-date. Our empirical evaluations show that SLVR improves robustness against model poisoning attacks, particularly outperforming existing methods by up to 50% under adaptive attacks. Additionally, SLVR demonstrates effective adaptability and stable convergence under various distribution shift scenarios.
The quality control of investment casting remains a critical challenge due to defect detection, real-time processing, and data traceability inefficiencies. This study presents an innovative Blockchain-integrated IoT system for advanced inspection of casting defects, combining a ResNet-based deep learning model for defect detection and dimensional measurement with Blockchain technology to ensure data integrity and traceability. The system demonstrated a significant improvement in defect detection accuracy, achieving an F1-score of 0.94, alongside high data integrity (0.99) and traceability (0.98) metrics. Additionally, it processes each casting in an average of 2.3 s, supporting a throughput of 26 castings per minute. By addressing critical challenges in smart manufacturing, this approach enhances operational efficiency, regulatory compliance, and user confidence. While scalability and energy efficiency remain areas for improvement, the proposed method provides a transformative solution for Industry 4.0, fostering transparency and reliability in manufacturing processes.
Blockchain technologies are increasingly recognized as a transformative force across industries, offering potential solutions for information management, data security, and operational efficiency improvement. However, integration into the healthcare sector faces significant barriers, ranging from technical challenges to organizational resistance. In this study, a methodology is proposed that examines these critical barriers through a comprehensive analysis of 3265 academic papers and derives actionable solutions from 1566 patents published between 2016 and 2023. This approach bridges the gap between identifying challenges and implementing solutions. Using the “Stepwise Weight Assessment Ratio Analysis (SWARA)”, twelve critical adoption challenges are analyzed, while Top2Vec-based topic modeling identifies innovations that best address the ranked barriers. In addition to this, the proposed ‘healthcare ecosphere’ knowledge map serves as a comprehensive tool to analyze key stakeholders, their interactions, and the alignment of adoption barriers in solution spaces. The findings show that innovations in blockchain technologies are heavily concentrated in areas such as data security and application functionalities, whereas other critical domains—such as consensus mechanisms, governance, and regulatory frameworks—remain underexplored, pointing to opportunities for growth and development. The mapping of barriers to solutions provides practical guidance for healthcare providers, policymakers, and technologists seeking to implement the blockchain technologies effectively. • Identified and ranked twelve key adoption barriers from technological and social contexts using SWARA methodology. • Analyzed trends in blockchain patent grants to strategically address the barriers. • Patent documents are clustered and mapped to address barriers using machine learning approach through Top2Vec. • Noting patent grants are concentrated in specific areas of applications. • Highlighted opportunities for innovation in under-explored blockchain technology layers.
This systematic review examines the integration of directed acyclic graph (DAG)-based blockchain technology in smart mobility ecosystems, focusing on electric vehicles (EVs), robotic systems, and drone swarms. Adhering to PRISMA guidelines, we conducted a comprehensive literature search across Web of Science, Scopus, IEEE Xplore, and ACM Digital Library, screening 1248 records to identify 47 eligible studies. Our analysis demonstrates that DAG-based blockchain addresses critical limitations of traditional blockchains by enabling parallel transaction processing, achieving high throughput (>1000 TPS), and reducing latency (<1 s), which are essential for real-time applications like autonomous vehicle coordination and microtransactions in EV charging. Key technical challenges include consensus mechanism complexity, probabilistic finality, and vulnerabilities to attacks such as double-spending and Sybil attacks. This study identifies five research priorities: (1) standardized performance benchmarks, (2) formal security proofs for DAG protocols, (3) hybrid consensus models combining DAG with Byzantine fault tolerance, (4) privacy-preserving cryptographic techniques, and (5) optimization of feeless microtransactions. These advancements are critical for deploying robust, scalable DAG-based solutions in smart mobility, and fostering secure and efficient urban transportation networks.
Bitcoin merupakan salah satu aset digital yang saat ini memiliki fluktuasi harga yang sangat signifikan serta berpotensi untuk terus berkembang. Pada tahun 2024 harga bitcoin mencetak rekor baru, namun tantangan regulasi dan skalabilitas tetap menjadi perhatian khusus yang harus diatasi. Harga bitcoin dapat naik tinggi namun juga dapat turun dibawah, oleh karena itu diperlukan metode yang sekiranya mendekati tepat untuk memprediksi harga bitcoin di masa depan. Tujuan dari penelitian ini yaitu mencari model prediksi dengan analisis time series. Data historis harga bitcoin dianalisa menggunakan orange data mining untuk membangun model ARIMA yang optimal. Berdasarkan evaluasi model dengan parameter ARMA (1,0,0) dan dengan jumlah lipatan validasi sebanyak 10 didapatkan hasil RMSE sebesar 1627,0 MAE sebesar 1162,6, dan MAPE sebesar 0,019. Hasil ini juga tidak jauh berbeda dengan parameters ARMA (1,0,0) (in-sample), sehingga model ARIMA ini dapat digunakan untuk memprediksi harga bitcoin di masa depan. Kata Kunci: Prediksi, Harga Bitcoin, ARIMA, Time Series
We present the design and implementation of a fourth-year undergraduate computer science elective course on cryptocurrency. The course introduces the basics of cryptocurrencies and blockchains using Bitcoin. Programming distributed applications for blockchains is explored using Ethereum via the Solidity programming language. Advanced theoretical concepts are discussed, using different cryptocurrencies to demonstrate how they are implemented in practice. Cryptocurrency applications are presented, along with a responsible examination of present-day cryptocurrency scams. The assignments build upon each other, with the students implementing a web-based blockchain application by the end of the semester. The course was well received, and student feedback was positive. All of the course materials are available online under public copyright licenses.
This paper provides a comprehensive analysis of cryptocurrency as a financial instrument, examining its underlying mechanisms, market structure, and risk characteristics. The study begins with an overview of cryptocurrency fundamentals, including blockchain technology and the evolution of the cryptocurrency market. Through quantitative analysis of daily returns for six cryptocurrencies and six traditional assets between January 2018 and December 2021, the research demonstrates cryptocurrency's distinctive risk-return profile. Principal Component Analysis reveals three major risk factors driving cryptocurrency returns, while clustering analysis identifies meaningful groupings among cryptocurrencies. The findings indicate that cryptocurrencies exhibit significantly higher volatility and tail risk compared to traditional assets but provide substantial diversification benefits when incorporated into conventional portfolios. Tangency portfolio analysis shows that adding cryptocurrencies to traditional assets substantially improves risk-adjusted returns, with the combined portfolio achieving a Sharpe ratio of 2.72, compared to 0.39 for traditional assets alone. The study further examines regulatory challenges, tax implications, and emerging frameworks, particularly within the European Union. This research contributes to understanding cryptocurrency's role in modern investment portfolios while highlighting the unique risks and regulatory considerations that accompany this emerging asset class.