I WAYAN SUMARJAYA, RENOVAR JOJOR DELIMA SIMANULLANG, RATNA SARI WIDIASTUTI
Forecasting is the process of estimating future events using past data. Financial time series forecasting often prioritizes stock price variables. Apart from the stock price variable, inter-transaction time or duration is also an important variable to predict, because the timing of changes in financial prices cannot be predicted. Duration modeling and forecasting can be done using the autoregressive conditional duration (ACD) model. In this research, modeling and forecasting using the ACD model was carried out on Ethereum. This research aims to predict the duration of Ethereum in order to help traders know the time needed to reach the next price change. Several ACD models with four distributions, i.e., exponential, Weibull, Burr, and generalized gamma were fit to the Ethereum duration. The research results suggest that the Burr-ACD model produces the smallest AIC value compared to other distributed ACD models. However, the forecast results using the Burr-ACD models show increasing duration and hence are less accurate. The generalized gamma-ACD (2,2) model was then chosen as an alternative for forecasting Ethereum duration, showing that Ethereum duration forecast results are less than one second, which indicates the high frequency of transactions that occur on Ethereum.
The integration of blockchain technology into healthcare systems has emerged as a technical solution for enhancing data security, protecting privacy, and improving interoperability. Blockchain-based smart contracts offer reliability, transparency, and efficiency in healthcare services, making them a focal point of many studies. However, challenges such as scalability, regulatory compliance, and interoperability continue to limit their widespread adoption. This study conducts a comprehensive literature review to assess blockchain-driven health data management, focusing on the classification of blockchain-based smart contracts in health policy and the health protocols and standards applicable to blockchain-based smart contracts. This review includes 80 core studies published between 2019 and 2025, identified through searches in PubMed, Scopus, and Web of Science using the PRISMA method. Risk of bias and methodological quality were assessed using the Joanna Briggs Institute tool. The findings highlight the potential of blockchain-enabled smart contracts in health policy management, emphasizing their advantages, limitations, and implementation challenges. Additionally, the research underscores their transformative impact on digital health policies in ensuring data integrity, enhancing patient autonomy, and fostering a more resilient healthcare ecosystem. Recent advancements in quantum technologies are also considered as they present both novel opportunities and emerging threats to the future security and design of healthcare blockchain systems.
Pardomuan Pardosi, Tussi Sulistyowati, Khairil Anwar, Maria Yovita R Pandin · 5 authors
Background. This research explores global studies on crypto asset audits in Decentralized Finance (DeFi) from 2021 to 2025 through a systematic literature review (SLR) approach, highlighting technological advancements like machine learning and hybrid analytics that enhance audit accuracy, fraud detection, and scalability. Purpose. Auditing practices have expanded to include smart contracts, compliance, security, and environmental audits. However, challenges persist, such as the lack of global regulatory standards, decentralized control, security risks, and instability within DeFi protocols. Method. Despite advancements, effective audits in DeFi require aligning technological innovation with adaptable regulatory frameworks to ensure sustainability and trust. Results. Managerially, DeFi platforms should integrate emerging technologies into auditing practices and collaborate with regulators to address compliance gaps, particularly in anti-money laundering (AML) and transparency. Conclusion. Future research should focus on developing global DeFi regulations, exploring decentralized auditing methods, and investigating the impact of new financial systems like the metaverse on auditing practices.
By Elizabeth Enkin, University of Nebraska-Lincoln DOI: https://www.doi.org/10.69732/CKTL9913 As language teachers, we are keenly aware of the important benefits that Web 2.0, the collaborative web, has brought to language teaching. From social media to audio and visual tools, Web 2.0
Paul Griffiths, Nuno Fernades Crespo, Carlos J. Costa
ABSTRACT Non‐fungible tokens (NFTs) are digital artifacts built on blockchain technology that have achieved notoriety for their rapid consumer adoption, technical sophistication, and dramatic price swings. This paper synthesizes contemporary academic research on NFT consumer behavior to better understand the current state of the field, to explore its focus and quality, and to identify the authors and subjects that are driving the research. Applying the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR‐4‐SLR) protocol and using an ACO‐TCM framework systematic review of literature, this study of 53 curated articles organizes the existing body of research on NFT consumer behavior to identify current themes and gaps in the academic literature, finding ample opportunities for further research. Finally, this paper proposes areas for further study based on the emerging opportunities in research streams, both in depth and in breadth, concerning NFT consumer behavior.
This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use these forecasts to optimize portfolios of stocks and cryptocurrencies. Our analysis presents results across different combinations of observation windows and rebalancing periods to compare performances of classical and deep learning variance-covariance estimation methods. The conclusions of the study are that although the strategies (portfolios) performance differed significantly between different combinations of parameters, generally the best results in terms of the information ratio and annualized returns are obtained using the LSTM-RNN models. Moreover, longer observation windows translate into better performance of the deep learning models indicating that these methods require longer windows to be able to efficiently capture the long-term dependencies of the variance-covariance matrix structure. Strategies with less frequent rebalancing typically perform better than these with the shortest rebalancing windows across all considered methods.
Krishnendu Chatterjee, Jan Matyáš Křišťan, Stefan Schmid, Jakub Svoboda · 5 authors
Payment channel networks (PCNs) are a promising technology that alleviates blockchain scalability by shifting the transaction load from the blockchain to the PCN. Nevertheless, the network topology has to be carefully designed to maximise the transaction throughput in PCNs. Additionally, users in PCNs also have to make optimal decisions on which transactions to forward and which to reject to prolong the lifetime of their channels. In this work, we consider an input sequence of transactions over $p$ parties. Each transaction consists of a transaction size, source, and target, and can be either accepted or rejected (entailing a cost). The goal is to design a PCN topology among the $p$ cooperating parties, along with the channel capacities, and then output a decision for each transaction in the sequence to minimise the cost of creating and augmenting channels, as well as the cost of rejecting transactions. Our main contribution is an $\mathcal{O}(p)$ approximation algorithm for the problem with $p$ parties. We further show that with some assumptions on the distribution of transactions, we can reduce the approximation ratio to $\mathcal{O}(\sqrt{p})$. We complement our theoretical analysis with an empirical study of our assumptions and approach in the context of the Lightning Network.
Vasileios Kouvakis, Stylianos E. Trevlakis, Alexandros-Apostolos A. Boulogeorgos, Hongwu Liu · 6 authors
Security has always been a priority, for researchers, service providers and network operators when it comes to radio access networks (RAN). One wireless access approach that has captured attention is blockchain enabled RAN (B-RAN) due to its secure nature. This research introduces a framework that integrates blockchain technology into RAN while also addressing the limitations of state-of-the-art models. The proposed framework utilizes queuing and Markov chain theory to model the aspects of B-RAN. An extensive evaluation of the models performance is provided, including an analysis of timing factors and a focused assessment of its security aspects. The results demonstrate reduced latency and comparable security making the presented framework suitable for diverse application scenarios.
Blockchain networks offer decentralization, transparency, and immutability for managing critical data but encounter scalability problems as the number of network members and transaction issuers grows. Sharding is considered a promising solution to enhance blockchain scalability. However, most existing blockchain sharding techniques prioritize performance at the cost of availability (e.g., a failure in a few servers holding a shard leads to data unavailability). In this paper, we propose PyloChain, a hierarchical sharded blockchain that balances availability and performance. PyloChain consists of multiple lower-level local chains and one higher-level main chain. Each local chain speculatively executes local transactions to achieve high parallelism across multiple local chains. The main chain leverages a directed-acyclic-graph (DAG)-based mempool to guarantee local block availability and to enable efficient Byzantine Fault Tolerance (BFT) consensus to execute global (or cross-shard) transactions within a collocated sharding. PyloChain speculatively executes local transactions across multiple local chains to achieve high parallelism. In order to reduce the number of aborted local transactions, PyloChain applies a simple scheduling technique to handle global transactions in the main chain. PyloChain provides a fine-grained auditing mechanism to mitigate faulty higher-level members by externalizing main chain operations to lower-level local members. We implemented and evaluated PyloChain, demonstrating its performance scalability with 1.49x higher throughput and 2.63x faster latency compared to the state-of-the-art balanced hierarchical sharded blockchain.
The FinTech revolution is changing the way banks work around the world by combining blockchain and artificial intelligence (AI) to make safe, efficient, and customer-focused financial environments. A systematic review of AI blockchain convergence in modern banking, emphasizing its transformative impact on security, operational efficiency, and financial innovation. AI enables intelligent decision-making through applications such as fraud detection, credit risk assessment, algorithmic trading, and predictive analytics, while blockchain provides decentralized, tamper-resistant, and auditable transaction infrastructure. Digital currencies, asset tokenization, decentralized finance (DeFi), smart contracts, and automated regulatory compliance are some of the new FinTech applications driven by their synergy. This integration also supports Environmental, Social, and Governance (ESG) by facilitating real-time fund allocation, sustainable investment tracking, and transparent auditing. Despite its significant potential persisting, including regulatory ambiguity, scalability limitations, cybersecurity risks, and data privacy concerns, which limit large-scale adoption in banking systems. By synthesizing and analyzing key technological trends, the current capabilities of AI–blockchain integration in FinTech that the synergistic convergence of AI, blockchain, and financial technologies is a critical enabler for next-generation digital banking, promoting financial inclusion, resilience, and sustainable economic growth
Shafique Ahmed Awan, Muazzam A. Khan, Anwar Ali Sathio, Haleem Farman · 7 authors
Blockchain scalability is a pressing challenge affecting blockchain throughput, latency, and energy consumption. This study proposes a dynamic block size optimization framework for private blockchain networks using hybrid heuristic algorithms—Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO)—integrated with Merkle trees and Directed Acyclic Graphs (DAGs). The core contribution is a novel block size adjustment strategy that relocates the Merkle root from the block header to a local buffer, reducing header size from 80 to 48 bytes and enabling higher transaction capacity. The framework achieved a 33.33% increase in transactions per block, a 30% reduction in latency, a 25% reduction in energy consumption, and a 60% improvement in transactions per kilobyte (KB). These results were validated using Python-based simulations, Ethereum transaction datasets, and defined experimental settings. The proposed solution is currently applicable to private blockchains, with future validation planned for public blockchain networks.
The cryptocurrency market, which is extremely volatile and has high price fluctuations, is transforming the financial ecosystems in the world.In contrast to traditional markets, cryptocurrencies are characterized by the unprecedented volatility due to the complicated interaction of speculative trading, regulatory changes, technological breakthroughs, and macroeconomic forces.The purpose of the current study is to build and test machine learning models to predict the price trend of cryptocurrencies, including the most popular ones, Bitcoin (BTC), Ethereum (ETH), and other top altcoins that are traded in the United States.The analysis is based on a large amount of data on historical prices at daily, hourly, and minute-by-minute intervals, including the detailed data on opening, closing, high, and low prices, and trading volumes that indicate the liquidity and the activity of investors.The most important technical indicators such as moving averages, Relative Strength Index (RSI) and Bollinger Bands are incorporated to identify the most important market signals and momentum.It uses three machine learning models, including Logistic Regression, Random Forest Classifier, and XGBoost Classifier.Directional prediction capability (upward or downward price movements) is evaluated by accuracy, precision, recall, and F1-score measures of model performance.Logistic Regression was the most accurate among the models that were tested, which highlights its comparative effectiveness in this application.The introduction of AI-based predictive analytics into cryptocurrency trading can be a great way to improve the process of decision-making by traders and institutional investors and help them comply with regulations in the U.S. financial system.This study sheds light on the transformational nature of machine learning in cryptocurrency prediction and also points out the research opportunities in the future, especially the use of deep learning models like the Long Short-Term Memory (LSTM) network in time-series analysis.
Dr. Asmita Khanna, Nidhi Khanna, Anushka Keshari, Pooja Singh
In recent years, environmental sustainability has emerged as a pressing global concern, calling for innovative financing mechanisms to support grassroots green initiatives. Crowdfunding, as a FinTech innovation, offers a decentralized and participatory platform to fund such projects by engaging the general public. This study investigates consumer perception regarding the role of crowdfunding in financing environmental projects, with a focus on awareness, trust, transparency, and willingness to contribute. The research adopts a quantitative approach using a structured questionnaire distributed among Indian consumers. A sample of 200 respondents was collected via online and offline surveys. The data was analyzed using SPSS, employing tools such as descriptive statistics, reliability tests (Cronbach’s alpha), Pearson’s Correlation, Chi-Square, Multiple Linear Regression analysis to identify key variables influencing consumer willingness to fund environmental initiatives. The findings indicate that trust in crowdfunding platforms, perceived impact of environmental projects, and transparency in fund utilization are the most significant predictors of willingness to contribute. While awareness of crowdfunding platforms is moderate, actual participation remains low, highlighting the gap between intention and action. The study offers valuable insights for crowdfunding platforms, environmental NGOs, and policymakers. It emphasizes the need for enhanced digital literacy, platform credibility, and transparent communication strategies to mobilize funds for environmental sustainability. This paper contributes to the limited empirical literature linking green crowdfunding and consumer behavior in the Indian context.
This paper presents a comprehensive framework for deploying a blockchain-based electronic voting system in Rwanda to address challenges of transparency, security, and public trust in electoral processes. Through detailed analysis of the current Rwandan electoral infrastructure and limitations, we propose a multilayered blockchain architecture that incorporates advanced cryptographic techniques, a national digital identity framework, and mobile accessibility features tailored to Rwanda's unique socio-economic landscape. Our proposed system leverages permissioned blockchain technology with a hybrid consensus mechanism to ensure the immutability of vote records while maintaining voter privacy through zero-knowledge proofs. The paper further discusses implementation challenges specific to Rwanda's context, including digital literacy (UNESCO, 2019), infrastructure limitations, and regulatory considerations. Our findings suggest that progressive, phased implementation of blockchain voting systems can significantly enhance electoral integrity while maintaining cultural and technological accessibility for Rwanda's diverse population.
Ana-Maria Istrate, Fausto Milletarì, Fabrizio Castrotorres, Jakub M. Tomczak · 7 authors
Abstract Reasoning models are typically trained against verification mechanisms in formally specified systems such as code or symbolic math. In open domains like biology, however, we lack exact rules to enable large-scale formal verification and instead often rely on lab experiments to test predictions. Such experiments are slow, costly, and cannot scale with computation. In this work, we show that world models of biology or other prior knowledge can serve as approximate oracles for soft verification , allowing reasoning systems to be trained without additional experimental data. We present two paradigms of training models with approximate verifiers: RLEMF : reinforcement learning with experimental model feedback and RLPK : reinforcement learning from prior knowledge. Using these paradigms, we introduce rbio1 , a reasoning model for biology post-trained from a pretrained LLM with reinforcement learning, using learned biological models for verification during training. We demonstrate that soft verification can distill biological world models into rbio1 , enabling it to achieve state-of-the-art performance on perturbation prediction in the PerturbQA benchmark. We further show that composing multiple AI-verifiers improves performance and that models trained with soft biological rewards transfer zero-shot to cross-domain tasks such as disease-state prediction. We present rbio1 as a proof of concept that predictions from biological models can train powerful reasoning systems using simulations rather than experimental data, offering a new paradigm for model training.
Roxana Maria Druță, Elena Simina Lakatos, Radu Munteanu, Lucian–Ionel Cioca · 7 authors
This study explores public perceptions, involvement, and barriers to energy communities in Romania, a country where decentralized renewable energy initiatives are still in their infancy. Data were collected through a nationwide online survey based on a quantitative research design, involving 118 respondents from all four macro-regions in Romania. The survey assessed awareness of the concept of energy communities, perceived benefits, technological and regulatory challenges, and willingness to participate or invest. The results show that the perception of energy communities is generally positive, with solar and environmental benefits being the most important. However, significant barriers remain, particularly in terms of financing, institutional support, and regulatory complexity. Urban, involved, and female respondents consistently rated benefits higher and identified more barriers than rural, non-participants and male respondents. Statistical differences between groups were confirmed using the Mann–Whitney U-test. These results highlight the importance of targeted communication, improved policy frameworks, and educational initiatives to ensure broader public involvement and inclusive development of renewable energy systems in Romania.
Supply chain finance (SCF) plays a key role in easing financing difficulties for small and medium-sized enterprises, but it also comes with risks such as information asymmetry, fraud involving pledged assets, and delays in credit evaluation.In this study, we introduce a dynamic risk management framework driven by IoT and enhanced by the integration of multiple technologies.Built on a four-layer IoT structure, comprising perception, network, processing, and application layers, the framework combines blockchain for secure and trusted data sharing, federated learning for collaborative data processing, and digital twin models for real-time risk simulation.At the perception level, 5th-Generation Mobile Communication Technology (5G)enabled low-power sensors ensure comprehensive and tamper-proof data collection.The network layer uses blockchain techniques such as sharding and zero-knowledge proofs to safeguard data privacy and institutional trust.In the processing layer, federated learning combined with edge and cloud computing enhances credit evaluation.On the other hand, the application layer employs smart contracts and feedback mechanisms to enable real-time responses and adaptive risk strategies.To put this framework into practice, we propose a phased approach: first building a real-time data ecosystem, then deploying secure risk control systems, optimizing distributed computing, and finally integrating a closed-loop risk control mechanism.This modular, collaborative strategy ensures that technological systems align with actual business needs.Ultimately, the research demonstrates how IoT, blockchain, and AI can work together to create a scalable and practical model for managing risk dynamically in SCF.
Introduction The initiation of blockchain has brought about revolutionary changes across multiple industries, including finance. This study analyzes published research related to existing financial reporting and audit practices relevant to the implementation and efficacy of blockchain technology. The decentralized, immutable, and transparent blockchain ledger is set to change traditional practices by enhancing accuracy, reducing fraud, and ensuring real-time data accessibility. Methods This study identifies and measures the factors influencing blockchain implementation in specific auditing areas, particularly financial reporting. This research analyzed accounting professionals’ awareness of information and communication technologies (ICT), data security, data privacy, and training among accounting professionals. Hence, the study conducted a survey that targeted accounting practitioners, chartered accountants, financial analysts, and auditors with the aim of analyzing and testing hypothesized relationships using structural equation modeling in AMOS software. Results It presents an empirical analysis that examines the extent to which these factors influence blockchain technology implementation in financial reporting and auditing. We found a significant influence of blockchain technology use on the practices of financial reporting and auditing, leading to enhanced accuracy and transparency, reduced audit time, and increased trust in financial reports. Key findings indicate that while blockchain technology offers significant advantages, widespread implementation faces hurdles such as regulatory compliance, technological integration, and stakeholder acceptance. Discussion Researchers can use these findings to determine potential areas for further research. In addition, this research provides valuable information to practitioners in the field, academics, industry professionals, and policymakers considering the integration of blockchain technology with financial reporting and auditing.
The purpose of Internet of Things (IoT) security is to ensure the availability, confidentiality, and integrity of IoT networks. However, due to the heterogeneity of IoT devices and the possibility of attacks of various kinds from both inside and outside the network, securing an IoT network is a difficult task. Handshake protocols are useful for achieving mutual authentication, which allows secure inclusion of devices into the network. By verifying that the information they receive is accurate and from a trusted source, mutual authentication minimizes the possibility that a malicious actor will compromise their connections. However, handshake protocols do not protect devices from attackers in the network. Use of autonomous anomaly detection and blacklisting prevents nodes with anomalous behavior from joining, re-joining, or remaining in the network. Similarly, trust scoring is another popular method that can be used to increase the resilience of the network against trust based system attacks. In view of the above, the contributions of this paper are three-fold. First, to ensure the security of the IoT network from outsider attacks in a zero-trust environment, we propose a new handshake protocol based on Physical Unclonable Functions that can be used in IoT device discovery and mutual authentication between the IoT device and the server. The proposed protocol is resilient to Man-in-the-Middle, replay and forgery attacks, as proven in our security analysis. Secondly, we propose a real-time intrusion and anomaly detection framework based on machine learning to prevent network-based attacks from insiders. Finally, we propose a trust system which utilizes feedback mechanisms based on smart contracts for managing the trust of a dynamic IoT network to increase resilience against behavioral attacks. Simulation results show that by using blacklisting, our trust management model provides greater resilience against trust-based attacks compared to similar blockchain-based trust models in the literature, and the proposed distributed IoT network security framework can secure an IoT network from both internal and external attacks, even in an environment where half of the devices in the network are compromised.
Brett Martin, Polymeros Chrysochou, Carolyn Strong, Adam J. Mills
ABSTRACT This research note reviews research published in Psychology & Marketing in response to our call for papers for cryptocurrency research. Cryptocurrency is an area worth trillions of dollars and it offers a rich field of potential research topics for consumer psychology. Based on the articles by scholars published in Psychology & Marketing , we present a synthesis of the literature surrounding consumer behavior and cryptocurrency and propose a conceptual model to guide future research. This conceptual model organizes the literature by antecedents, process, and outcomes. In addition, we present a summary table of contributions from the research and offer a range of future research opportunities to be explored.
This article proposes a novel blockchain-based architecture for cross-border payments that integrates self-sovereign identity (SSI) and zero-knowledge proofs (ZKPs) to address the fundamental challenges of traditional systems. The proposed framework enables near-instant settlement while preserving privacy and ensuring regulatory compliance by design. By layering an identity infrastructure with ZKP-gated smart-contract escrows and regulatory oracles, the system allows participants to prove compliance with jurisdiction-specific requirements without revealing sensitive personal data. The architecture comprises three interconnected layers — identity, value, and compliance — that work together to streamline remittances, business transactions, and international payroll processes. Comparative analysis demonstrates significant advantages over both correspondent banking and current blockchain networks in terms of settlement speed, transaction costs, fraud prevention, and automated compliance. While the approach faces challenges, including network adoption barriers, technical scalability, and governance complexity, this study outlines promising directions for future development, particularly in the context of emerging central bank digital currencies (CBDCs) and regulated stablecoins.
This article presents a comprehensive analysis of the impact of cryptocurrencies on the economic and environmental security of the G7 countries, exploring both the potential risks and prospects. The study focuses on the United States, Canada, the United Kingdom, France, Germany, Italy, and Japan, offering a detailed exploration of the increasing adoption of cryptocurrencies in these nations. Despite the benefits such as enhanced financial inclusion and cross-border transaction efficiency, cryptocurrencies pose significant challenges, including their use in illicit activities like money laundering and terrorism financing. The research critically examines the substantial energy consumption associated with certain cryptocurrency mining processes, particularly Proof-of-Work mechanisms, and their consequent environmental impacts, including carbon emissions, electronic waste, and air pollution. It investigates the corresponding energy policies and regulatory responses emerging within the G7 to address these concerns, alongside the development of more energy-efficient alternatives like Proof-of-Stake and the push for renewable energy in mining. The article critically examines these dual aspects, highlighting the measures implemented by regulators and policymakers to mitigate risks. It also delves into the evolving landscape of Central Bank Digital Currencies (CBDCs) and their potential role in enhancing financial system efficiency and security, including considerations for their energy footprint. The study employs a robust methodological framework, combining statistical analysis of market trends, case studies, and policy analysis to provide a balanced view of the current state and future trajectory of cryptocurrencies in the G7 countries. By offering a nuanced understanding of both the opportunities and threats posed by digital currencies, including their energy and environmental dimensions, this article contributes to the ongoing discourse on their integration into global financial systems and their implications for sustainable economic security.
Simeon Okechukwu Ajakwe, Igboanusi Ikechi Saviour, Jae‐Min Lee, Dong‐Seong Kim
Background: The increasing deployment of unmanned aerial vehicles (UAVs) for logistics in smart cities presents pressing challenges related to identity spoofing, unauthorized payload transport, and airspace security. Existing drone defense systems (DDSs) struggle to verify both drone identity and payload authenticity in real time, while blockchain-assisted solutions are often hindered by high latency and limited scalability. Methods: To address these challenges, we propose iBANDA, a blockchain- and AI-assisted DDS framework. The system integrates a lightweight You Only Look Once 5 small (YOLOv5s) object detection model with a Snowball-based Proof-of-Stake consensus mechanism to enable dual-layer authentication of drones and their attached payloads. Authentication processes are coordinated through an edge-deployable decentralized application (DApp). Results: The experimental evaluation demonstrates that iBANDA achieves a mean average precision of 99.5%, recall of 100%, and an F1-score of 99.8% at an inference time of 0.021 s, validating its suitability for edge devices. Blockchain integration achieved an average network latency of 97.7 ms and an end-to-end transaction latency of 1.6 s, outperforming Goerli, Sepolia, and Polygon Mumbai testnets in scalability and throughput. Adversarial testing further confirmed resilience to Sybil attacks and GPS spoofing, maintaining a false acceptance rate below 2.5% and continuity above 96%. Conclusions: iBANDA demonstrates that combining AI-based visual detection with blockchain consensus provides a secure, low-latency, and scalable authentication mechanism for UAV-based logistics. Future work will explore large-scale deployment in heterogeneous UAV networks and formal verification of smart contracts to strengthen resilience in safety-critical environments.
This paper presents a comprehensive comparative analysis of three prominent blockchain networks: Solana (SOL), Ethereum (ETH), and SUI. The study examines transaction speeds, costs, transaction volumes, and evaluates the benefits and disadvantages of each cryptocurrency in real-world applications. Through detailed analysis of technical specifications, market performance, and ecosystem development, this research provides insights into the relative strengths and weaknesses of these blockchain platforms as they compete for market dominance in 2025.