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Data-driven price trends prediction of Ethereum: A hybrid machine learning and signal processing approach

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100231

Ebenezer Fiifi Emire Atta Mills , Yuexin Liao , Zihui Deng

Due to recent fluctuations in cryptocurrency prices, Ethereum has gained recognition as an investment asset. Given its volatile nature, there is a significant demand for accurate predictions to guide investment choices. This paper examines the most influential features of the daily price trends of Ethereum using a novel approach that combines the Random Forest classifier and the ReliefF method. Integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Short-Time Fourier Transform (STFT) results in high accuracy and performance metrics for Ethereum price trend predictions. This method stands out from prior research, primarily based on time series analysis, by enhancing pattern recognition across time and frequency domains. This adaptability leads to better prediction capabilities with accuracy reaching 76.56% in a highly chaotic market such as cryptocurrency. The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.

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引用次数: 0

MatStick: Changing the material sensation of objects upon impact

Q1 Computer Science

Virtual Reality Intelligent Hardware

Pub Date : 2024-12-01 DOI: 10.1016/j.vrih.2024.11.001

Songxian Liu, Jian He, Shengsheng Jiang, Ziyan Zhang, Mengfei Lv

An increasing number of studies have focused on providing rich tactile feedback in virtual reality interactive scenarios. In this study, we addressed a tapping scenario in virtual reality by designing MatStick, a solution capable of offering diverse tapping sensations. MatStick utilizes a soft physical base to provide force feedback and modulates the instantaneous vibration of the base using a voice coil motor, thereby altering the perception of the base material. We conducted two psychophysical experiments and a subjective evaluation to assess the capabilities of MatStick. The results demonstrate that MatStick can deliver rich tapping sensations. Although users may find it challenging to directly correlate the tapping sensation with the actual physical material based solely on tactile feedback, in immersive scenarios combined with visual and auditory cues, MatStick significantly enhances the user's interaction experience.

引用次数: 0

Blockchain-enhanced hydrogen fuel production and distribution for sustainable energy management

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100229

Yash Madhwal , Yury Yanovich , Matteo Coveri , Ninoslav Marina

Renewable energy projects, particularly wind and solar farms, have garnered significant attention as a potential solution to global energy challenges. Despite the energy production obstacles, the steady availability of fossil fuels continues to compete due to established distribution systems, as societies increasingly rely on electricity. Hydrogen fuel has emerged as a promising avenue for energy production, storage, and distribution, involving converting surplus renewable electricity into hydrogen through electrolysis, storing it, and distributing it. Our novel approach leverages blockchain technology to enhance the efficiency and traceability of hydrogen fuel production, offering a unique synergy of transparency, security, and decentralized governance. We showcase its viability and effectiveness using the ERC-1155 token standard to tokenize renewable resources and convert them into hydrogen fuel. Within our tokenized fuel blockchain architecture, we simulate the forecasted growth in hydrogen production and vehicle demand, highlighting our approach's efficiency, traceability, and transparency. This integration showcases the potential for a sustainable hydrogen fuel ecosystem. The gas consumption data analysis indicates that the daily gas consumption remains below 194 million Gas for refilling 3245 vehicles (including the cost of one-time contract deployment), demonstrating the feasibility and efficiency of our approach.

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引用次数: 0

Understanding the market potential of crypto mining with quantum mechanics and golden cut-based picture fuzzy rough sets

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100230

Hasan Dincer , Serhat Yüksel , Gabor Pinter , Alexey Mikhaylov

Significant improvements should be made to increase the market potential of crypto mining. However, it is not financially feasible to make too many improvements because all actions lead to cost increases. In this context, it is necessary to determine the factors that most affect this process. Accordingly, the purpose of this study is to understand the main indicators that can improve the market potential of crypto mining activities. Therefore, the main research question of this study is to identify which factors should be prioritized while generating appropriate strategies to increase these activities. In this context, a new model has been constructed to answer this question. First, significant indicators are identified based on the evaluation of the literature. After that, these factors are weighted via quantum picture fuzzy rough set-based M-SWARA. The main contribution of this study is the generation of a new decision-making model to understand the key issues related to the market potential of crypto mining activities. The M-SWARA model is taken into consideration for criteria weighting. Owing to this issue, the causal relationships between the items can be identified. The findings demonstrate that reducing energy costs emerges as the most important factor for improving the market potential of the crypto mining industry. Furthermore, technological developments also play an important role in this regard.

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引用次数: 0

Integrating blockchain technology within an information ecosystem

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100225

Francesco Salzano , Lodovica Marchesi , Remo Pareschi , Roberto Tonelli

Context: Blockchain-based information ecosystems (BBIEs) are a type of information ecosystem in which blockchain technology is used to provide a trust mechanism among parties and to manage shared business logic, breaking the traditional scheme of information ecosystems dominated by a leading company and leveraging the decentralization of data management, information flow, and business logic.

Objective: In this paper, we'd like to propose an architecture and the technical aspects concerning creating a BBIE, underlining the supplied advantages and the logic decomposition among the business and storage components.

Method: The requirements are derived from the current needs of the collaborative business and the data collected by surveying practitioners. To meet these needs, we followed the Grounded Theory research approach. We validate our architectural schema against a case study on managing a wine supply chain involving different companies and supervision authorities.

Results: The proposed solution integrates blockchain-based applications with the existing information system as a module of the ecosystem, leveraging on the low costs, scalability, and high-level security because of the restricted access to the network.

Conclusion: We must go a long way in deepening and refining the possibilities offered by technology in supporting innovative multi-organizational business models. BBIEs can contribute substantially to paving the way in such a direction.

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引用次数: 0

Robust cooperative spectrum sensing in cognitive radio blockchain network using SHA-3 algorithm 基于SHA-3算法的认知无线电区块链网络鲁棒协同频谱感知

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100224

Evelyn Ezhilarasi I, J. Christopher Clement

Cognitive radio network (CRN) uses the available spectrum resources wisely. Spectrum sensing is the central element of a CRN. However, spectrum sensing is susceptible to multiple security breaches caused by malicious users (MUs). These attackers attempt to change the sensed result in order to decrease network performance. In our proposed approach, with the help of blockchain-based technology, the fusion center is able to detect and prevent such criminal activities. The method of our model makes use of blockchain-based MU detection with SHA-3 hashing and energy detection-based spectrum sensing. The detection strategy takes place in two stages: block updation phase and iron out phase. The simulation results of the proposed method demonstrate 3.125%, 6.5%, and 8.8% more detection probability at −5 dB signal-to-noise ratio (SNR) in the presence of MUs, when compared to other methods like equal gain combining (EGC), blockchain-based cooperative spectrum sensing (BCSS), and fault-tolerant cooperative spectrum sensing (FTCSS), respectively. Thus, the security of cognitive radio blockchain network is proved to be significantly improved.

认知无线电网络（Cognitive radio network， CRN）明智地利用可用的频谱资源。频谱感知是CRN的核心要素。然而，频谱感知容易受到恶意用户（MUs）的多重安全漏洞的影响。这些攻击者试图改变感知结果，以降低网络性能。在我们提出的方法中，借助基于区块链的技术，融合中心能够检测和预防此类犯罪活动。我们的模型方法利用基于区块链的MU检测与SHA-3哈希和基于能量检测的频谱感知。检测策略分为两个阶段：块更新阶段和清除阶段。仿真结果表明，与等增益组合（EGC）、基于区块链的合作频谱感知（BCSS）和容错合作频谱感知（FTCSS）等方法相比，该方法在−5 dB信噪比（SNR）下的检测概率分别提高了3.125%、6.5%和8.8%。从而证明认知无线电区块链网络的安全性得到了显著提高。

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引用次数: 0

InputJump: Augmented reality-facilitated cross-device input fusion based on spatial and semantic information

Q1 Computer Science

Virtual Reality Intelligent Hardware

Pub Date : 2024-12-01 DOI: 10.1016/j.vrih.2024.10.001

Xin Zeng , Xiaoyu Wang , Tengxiang Zhang , Yukang Yan , Yiqiang Chen

The proliferation of computing devices requires seamless cross-device interactions. Augmented reality (AR) headsets can facilitate interactions with existing computers owing to their user-centered views and natural inputs. In this study, we propose InputJump, a user-centered cross-device input fusion method that maps multi-modal cross-device inputs to interactive elements on graphical interfaces. The input jump calculates the spatial coordinates of the input target positions and the interactive elements within the coordinate system of the AR headset. It also extracts semantic descriptions of inputs and elements using large language models (LLMs). Two types of information from different inputs (e.g., gaze, gesture, mouse, and keyboard) were fused to map onto an interactive element. The proposed method is explained in detail and implemented on both an AR headset and a desktop PC. We then conducted a user study and extensive simulations to validate our proposed method. The results showed that InputJump can accurately associate a fused input with the target interactive element, enabling a more natural and flexible interaction experience.

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引用次数: 0

Looking for stability in proof-of-stake based consensus mechanisms 寻找基于股权证明的共识机制的稳定性

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100222

Alberto Leporati, Lorenzo Rovida

The Proof-of-Stake (PoS) consensus algorithm has been criticized in the literature and in several cryptocurrency communities, due to the so-called compounding effect: who is richer has more coins to stake, therefore a higher probability of being selected as a block validator and obtaining the corresponding rewards, thus becoming even richer. In this paper, we present a PoS simulator written in the Julia language that allows one to test several variants of PoS-based consensus algorithms, tweak their parameters, and observe how the distribution of cryptocurrency coins among users evolves over time. Such a tool can be used to investigate which combinations of parameter values allow to obtain a “fair” and stable consensus algorithm, in which, over the long term, no one gets richer or poorer by the mere act of validating blocks. Based on this investigation, we also introduce a new PoS-based consensus mechanism that allows the system to keep the wealth distribution stable even after a large number of epochs.

引用次数: 0

A deep decentralized privacy-preservation framework for online social networks

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100233

Samuel Akwasi Frimpong , Mu Han , Emmanuel Kwame Effah , Joseph Kwame Adjei , Isaac Hanson , Percy Brown

This paper addresses the critical challenge of privacy in Online Social Networks (OSNs), where centralized designs compromise user privacy. We propose a novel privacy-preservation framework that integrates blockchain technology with deep learning to overcome these vulnerabilities. Our methodology employs a two-tier architecture: the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm (ePSOGSA) for optimizing feature selection, while the second tier employs an enhanced Non-symmetric Deep Autoencoder (e-NDAE) for anomaly detection. Additionally, a blockchain network secures users’ data via smart contracts, ensuring robust data protection. When tested on the NSL-KDD dataset, our framework achieves 98.79% accuracy, a 10% false alarm rate, and a 98.99% detection rate, surpassing existing methods. The integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.

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引用次数: 0

A prototype model of zero trust architecture blockchain with EigenTrust-based practical Byzantine fault tolerance protocol to manage decentralized clinical trials

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100232

Ashok Kumar Peepliwal , Hari Mohan Pandey , Surya Prakash , Sudhinder Singh Chowhan , Vinesh Kumar , Rahul Sharma , Anand A. Mahajan

The COVID-19 pandemic necessitated the emergence of Decentralized Clinical Trials (DCTs) due to patient retention, accelerating trials, improving data accessibility, enabling virtual care, and facilitating seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the IoT has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done for immutability, privacy and security, mutual consensus, transparency, accountability, tracking and tracing, and temperature‒humidity control parameters.

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