This paper evaluates the performance of classical time series models in forecasting Bitcoin prices, focusing on ARIMA, SARIMA, GARCH, and EGARCH. Daily price data from 2010 to 2020 were analyzed, with models trained on the first 90 percent and tested on the final 10 percent. Forecast accuracy was assessed using MAE, RMSE, AIC, and BIC. The results show that ARIMA provided the strongest forecasts for short-run log-price dynamics, while EGARCH offered the best fit for volatility by capturing asymmetry in responses to shocks. These findings suggest that despite Bitcoin's extreme volatility, classical time series models remain valuable for short-run forecasting. The study contributes to understanding cryptocurrency predictability and sets the stage for future work integrating machine learning and macroeconomic variables.
The Internet of Things (IoT) generates massive volumes of patient and healthcare data every day. Providing the required accuracy for data classification, processing time, and analyzing the vast volumes of data from IoT devices and sensors are said to be the main challenges in IoT. Cloud computing is widely utilized as the foundation for the technologies required to secure healthcare. The healthcare industry has the most promise for blockchain technology since it can be used to integrate fragmented systems, the standard of electronic medical records should be raised, and take a more patient-centric approach to healthcare systems. The objective is to protect medical data, enable patients to use it to support their medical care, and provide reliable consent protocols for data exchange between various institutions and apps. Provide a blockchain-based architecture that verifies user identity using the Secure Hash Algorithm (SHA256) and Proof of Stake (POS) cryptography consensus technique to guarantee EHR sharing across many electronic healthcare systems.In this study, we assessed the performance of our proposed architecture using several metrics, and found that blockchain is a reliable security solution for the upcoming IoT network.
Introducing Reliablocks, an on-chain reliability index for non-finalized blocks in Optimistic Rollups. This was built during the EigenLayer Infinite Hackathon at the Infinite Hacker House at DevCon 2024. As part of this research, we delivered a working Layer AVS WASMI component, a working Eigen Layer AVS component, EigenLayer Solidity smart contracts that work with the AVS component, a UI dashboard illustrating the reliability score and a derived interest rate for further utilization.
The current surge in supply chain finance has significantly alleviated the "capital challenges" faced by domestic related enterprises, enabling enterprises upstream and subsequent stages of the industrial chain to achieve effective circulation of financing services in the supply chain based on the credit of core enterprises. By gathering essential information from the heart of the supply chain, supply chain financing enables efficient resource distribution and aids all stakeholders in making well-informed choices. However, supply chain finance in China still faces numerous obstacles, such as information asymmetry and inefficient credit transmission chains, hindering its long-term development. This paper designs an operational framework for supply chain finance incorporating blockchain technology, clearly defines the participating entities, and analyzes their business relationships. Based upon evolutionary game theory, a supply chain finance financing game model incorporating blockchain technology is constructed. A comparative analysis of the model's equilibrium points and their stability is conducted. The choices of evolutionary equilibrium strategies adopted by small and medium-sized enterprises, key players, and financing entities within this framework are explored, and the influence of blockchain technology on the prerequisites for completing supply chain finance transactions is investigated.
Ejiro U, Osiobe, Waleed A., Hammood, Safia, Malallah, Nyore E., Osiobe ¡ 6 authors
Quantum mechanics principles underpin quantum computing, signaling a major shift in how we process information. While it offers immense processing power and potential advantages, it also presents significant challenges for the cryptocurrency industry. This sector has grown rapidly, supporting decentralized finance and empowering users worldwide, but it also attracts malicious actors looking to exploit its vulnerabilities. Traditional cryptography remains strong, yet increasingly sophisticated computational attacks threaten security. As the cryptocurrency market expands, quantum computing offers both opportunities, such as improved transaction security, and risks, like easier decryption for hackers. Understanding quantum technologyâs benefits and challenges is crucial as it develops. Currently, data is protected by traditional cryptography, but future, more powerful quantum computers could weaken this security. This article explores potential uses of quantum computing in daily life and business, explains its functions simply, and discusses societal impacts. Its goal is to help students and general readers understand how quantum technology might transform our world through clear language and real-life examples. Topics include the basics of quantum computing, its present and future applications across industries, and its societal effects. We provide a thorough analysis of how quantum computing could reshape society through mathematical insights, practical examples, and future perspectives.
Cloud storage uses proofs of ownership to avoid redundant uploads while keeping file contents secret. Many existing schemes need extra round trips, or rely on predictable sampling. These choices reduce security when an adversary knows part of the file. We present MiS-PoW, a zero knowledge and non-interactive proof of ownership. The protocol derives a synchronized challenge seed from the existing HTTPS/TLS session. The seed binds a discretized time window and the file identifier. Both parties compute the same challenges locally, and the protocol adds no new messages. MiS-PoW samples blocks with a stratified policy without duplicates. The policy enforces coverage across partitions and reduces the advantage of contiguous knowledge and near duplicate files. The proof layer uses STARKs with simple AIR constraints. The constraints check that indices come from the seed, lie in range, are unique, and meet per partition counts. We analyze security and show seed unpredictability, resistance to replay, and bounds under partial knowledge with limited grinding. A prototype shows that verification time does not grow with file size, and proof and bandwidth costs remain modest. MiS-PoW is deployable, privacy preserving, and scalable for cloud storage.
Ihunanya Udodiri Ajakwe, Victor Ikenna Kanu, Simeon Okechukwu Ajakwe, DongâSeong Kim
The Korean Emission Trading Scheme (K-ETS) is vital for reducing carbon emissions in South Korea. However, issues in transparency, security, and computational overhead limit its effectiveness. This work proposes an energy-efficient blockchain-based framework (eBCTC) to enhance the system with a decentralized blockchain architecture, Purechain. The framework leverages an improved consensus mechanism, the Proof of Authority and Association (PoA 2 ). This is to address key challenges in the current K-ETS, such as centralization, lack of transparency, and high energy consumption. The PoA 2 significantly reduces gas usage, with experimental results showing a 22 % reduction in gas consumption compared to traditional Proof of Work (PoW) and Proof of Authority (PoA) mechanisms. Also, PoA 2 recorded a Ă6 and Ă2 reduction in gas price compared to PoW and PoA. The system also achieves faster transaction finality and lower computational costs, with transaction costs reduced by up to 83 % across the key K-ETS activities, including emissions reporting, credit allocation, and trading. Also, the system achieved moderate throughput, high latency, doubling scalability, high reliability, and a high success rate compared with DPoS and PBFT based on transaction stress validation tests. With an improved smart contract, intelligent automation of key functions, the system achieved a high energy gain for improved incentives. The proposed framework not only enhances the scalability and transparency of K-ETS but also aligns with South Korea's carbon neutrality goals by minimizing the environmental impact of blockchain operations. This study provides a solid foundation for sustainable carbon trading systems and an accountable carbon economy, contributing to global efforts to combat climate change in achieving the 2050 net-zero carbon emissions goal. ⢠Purechain PoA2 enables secure, low-energy carbon trading in K-ETS. ⢠22 % less gas usage and 83 % lower costs than PoW and PoA. ⢠Smart contracts automate K-ETS compliance and incentives. ⢠Improves scalability, transparency, and network reliability. ⢠Supports South Korea's 2050 net-zero carbon goal.
Artificial intelligence (AI) agents are increasingly capable of initiating financial transactions on behalf of users or other agents. This evolution introduces a fundamental challenge: verifying both the authenticity of an autonomous agent and the true intent behind its transactions in a decentralized, trustless environment. Traditional payment systems assume human authorization, but autonomous, agent-led payments remove that safeguard. This paper presents a blockchain-based framework that cryptographically authenticates and verifies the intent of every AI-initiated transaction. The proposed system leverages decentralized identity (DID) standards and verifiable credentials to establish agent identities, on-chain intent proofs to record user authorization, and zero-knowledge proofs (ZKPs) to preserve privacy while ensuring policy compliance. Additionally, secure execution environments (TEE-based attestations) guarantee the integrity of agent reasoning and execution. The hybrid on-chain/off-chain architecture provides an immutable audit trail linking user intent to payment outcome. Through qualitative analysis, the framework demonstrates strong resistance to impersonation, unauthorized transactions, and misalignment of intent. This work lays the foundation for secure, auditable, and intent-aware autonomous economic agents, enabling a future of verifiable trust and accountability in AI-driven financial ecosystems.
Thanassis Tiropanis, George Roussos, Mohammad Bahrani, Mohamed Ragab
The growing demand for data ownership and privacy is reshaping how information is accessed, managed, integrated, and recommended. Building on the inaugural DESERE workshop at The Web Conference 2024, this second edition advances research on Decentralised Search and Recommendation platforms such as Personal Online Datastores (PODs), where users retain control of their data and explicitly manage permissions. As ecosystems decentralise, traditional information retrieval must be revisited while standards for new techniques and system designs are developed to ensure efficient, accurate, and privacy-preserving search. The Second DESERE workshop at CIKM 2025 focuses on infrastructures and retrieval algorithms for user-controlled data. It convenes a cross-disciplinary community spanning data retrieval, management and integration, semantic technologies, recommendation systems, privacy-aware computing, and search efficiency to explore approaches that prioritize user agency, data ownership, and scalable retrieval across PODs and related architectures. Through paper presentations, panels, and interactive sessions, the workshop will highlight challenges, opportunities, and solutions for privacy-preserving IR. These discussions are especially relevant to domains where user-centric design and data stewardship are critical-such as personal finance, education, and high-stakes areas like criminal justice and health.
D-POAF: A Decentralized Proof Oriented AI Framework Traceable and Autonomous Software Engineering (Paper) English Version The D-POAF Framework (Decentralized Proof Oriented AI Framework) is an AI-native, Proof-driven, Secure-by-Design, and Sovereign framework designed to reinvent software creation, supervision, and security. Key Features: - Decentralized governance model for managing complex software ecosystems.- Dynamic decentralized ledger mechanisms for multi-project orchestration and transparent delivery cycles.- Horizontal, power-free organizational structure based on dynamic, evolving laws.- Secure-by-Design principles with Proof (PoD, PoV, PoR) ensuring sovereignty, privacy, and compliance by default. This release contains the official D-POAF white paper, offering a comprehensive ecosystem, core principles, and practical tools to implement the framework in academic, personal, and professional environments. --- Usage Terms: This guide and all associated materials of the D-POAFÂŽ framework are distributed under the terms of the Apache License, Version 2.0 (Open Source) (the âLicenseâ);you may not use this work except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0 for contact please visit: https://www.d-poaf.org or : contact@inovionix.com --- Š 2025 Inovionix.
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Azhar Hussain Mozumder, M. John Basha, Chayapathi A. R
With more and more existing networks being transformed to Software-Defined Networking (SDN), they need to be more secure and demand smarter ways of traffic control. This work, SmartSecChain-SDN, is a platform that combines machine learning based intrusion detection, blockchain-based storage of logs, and application-awareness-based priority in SDN networks. To detect network intrusions in a real-time, precision and low-false positives setup, the framework utilizes the application of advanced machine learning algorithms, namely Random Forest, XGBoost, CatBoost, and CNN-BiLSTM. SmartSecChain-SDN is based on the Hyperledger Fabric, which is a permissioned blockchain technology, to provide secure, scalable, and privacy-preserving storage and, thus, guarantee that the Intrusion Detection System (IDS) records cannot be altered and can be analyzed comprehensively. The system also has Quality of Service (QoS) rules and traffic shaping based on applications, which enables prioritization of critical services, such as VoIP, video conferencing, and business applications, as well as de-prioritization of non-essential traffic, such as downloads and updates. Mininet can simulate real-time SDN scenarios because it is used to prototype whole architectures. It is also compatible with controllers OpenDaylight and Ryu. It has tested the framework using the InSDN dataset and proved that it can identify different kinds of cyberattacks and handle bandwidth allocation efficiently under circumstances of resource constraints. SmartSecChain-SDN comprehensively addresses SDN system protection, securing and enhancing. The proposed study offers an innovative, extensible way to improve cybersecurity, regulatory compliance, and the administration of next-generation programmable networks.
Prof. S. H. Thengil, Tanmay Sadanshiv, A. M. Patil, Shreyash Trimbake ¡ 5 authors
Abstract - With the increasing volume of digital evidence in law-enforcement and judicial processes, ensuring integrity, traceability and tamper-resistance has become paramount. This paper presents the Blockchain Evidence Archive System (BEAS), a decentralized application that leverages blockchain technology, smart contracts and the InterPlanetary File System (IPFS) to provide a secure, immutable and transparent evidence- management platform. Evidence metadata is stored on an Ethereum-based blockchain while the associated large files (images, videos, documents) are stored on IPFS with their cryptographic hashes recorded on-chain. Role-based access control ensures only authorized users such as police officers and court officials can upload, verify or access evidence. We describe the system architecture, implementation details, security features and evaluate the performance of the system in terms of upload time, verification latency and resistance to tampering. The results demonstrate that BEAS significantly improves evidence integrity and auditability when compared to conventional centralized systems. We conclude with a discussion on future enhancements including biometric integration, mobile accessibility and enterprise-scale deployment. l Key Words: Blockchain Technology, IPFS, Digital EvidenceManagement, Decentralized Application, Smart Contracts, Ethereum Network, Cryptographic Hashing, Data Integrity, Tamper- Proof Storage, Role-Based Access Control, Chain of Custody, Evidence Verification, Immutable Ledger, Secure File Storage, Decentralized Architecture, Forensics Technology, Law Enforcement Data Security, Distributed Ledger Technology
Mohd. Sultan Ahammad, Maisha Maliha, Nilufa Easmin Nila, Md Shofiqul Islam
Blockchain technology is revolutionizing industries by fundamentally transforming data management and storage practices. Traditional banking systems, however, continue to face challenges such as dependency on intermediaries, lack of transparency, vulnerability to fraud, and restricted accessibility. To overcome this limitation, we propose an innovative blockchain framework built on the Ethereum platform to enhance security and efficiency in banking. The proposed system eliminates intermediaries by using Ethereum-based smart contracts to enable secure, automated peer-to-peer (P2P) deposits, withdrawals, and transfers while incorporating a user-friendly interface with MetaMask and custom wallets for accessibility. The architecture was implemented and tested on the Sepolia Ethereum Testnet using Solidity, Ether.js, and React.js, ensuring seamless interaction between the smart contract and the user interface. Our experimental evaluation demonstrated significant improvements in transaction speed, transparency, and operational efficiency compared to traditional systems, with near real-time processing and automated verification. Performance benchmarking showed competitive latency and throughput, while gas cost analysis highlighted trade-offs in transaction expenses compared to conventional banking. These findings suggest that our blockchain framework has strong potential to address long-standing inefficiencies in the financial sector. While challenges remain, including scalability and regulatory considerations, this work offers a concrete and impactful step toward the practical adoption of blockchain in mainstream banking.
This systematic review explores the potential applications of blockchain technology within the financial operations of the UK's National Health Service (NHS), specifically focusing on its impact on general ledgers. The NHS, a complex and vast healthcare system, faces significant financial management challenges, including data fragmentation, inefficiencies in transaction processing, and issues with transparency and auditability. Blockchain, with its inherent characteristics of decentralization, immutability, and cryptographic security, offers a promising paradigm for addressing these issues. This paper systematically reviews the opportunities that blockchain presents for enhancing financial transparency, streamlining payment processes, improving data integrity, and reducing administrative overhead in NHS general ledgers. Concurrently, it critically examines the significant challenges to its adoption, including regulatory hurdles, interoperability concerns, scalability limitations, and the substantial investment required for implementation and training. By synthesizing current literature and identifying key themes, this review aims to provide a comprehensive understanding for policymakers, financial managers, and technology innovators within the NHS regarding the strategic implications of integrating blockchain into healthcare finance.
Two parallel but potentially opposing forces in the financial landscape: the worldwide development of central bank digital currencies (CBDCs) and rapid development of privately offered tokenized assets and decentralized finance (DeFi). Though with more than 130 countries now looking into CBDCs and the tokenized asset market forecast at $16 trillion by 2030, the dynamic between public and private digital monies is shaping up to be the lifeblood of the future direction of payments, lending and monetary policy. This document examines whether or not these systems can work together, or whether their structural differences will create fragmentation, regulatory friction, and systemic risk. Employing a mixed-methods design that ranges from on-chain data analysis, comparative case studies related to major CBDC initiatives (for example, digital euro, digital yuan, Project mBridge, Project Aurum), to interviews with professionalsâcentral bankers, DeFi developers and regulatorsâwe address three crucial questions: (1) Under what circumstances can CBDCs be used as settlement infrastructure for tokenized assets without compromising monetary policy autonomy? (2) What are the implications of design decisions for CBDCs, in particular, programmability, privacy and access, for alignment with DeFi ecosystems? (3) Which governance mechanisms might align both public and private digital money to support inclusive, efficient and resilient financial markets? Our results favour a âlayered monetary architecture,â with CBDCs to serve as reliable, low-risk, secure settlement anchors and private tokenized finance to facilitate innovation, access and user-friendly services and innovative user experiences. In this paper, we present the Monetary Layer Compatibility Framework which, as we have mentioned, is a diagnostic instrument for tracking the alignment on five aspects of these criteria: settlement finality, programmability scope, privacy guarantees, access permissions, and regulatory hooks. Based on both empirical evidence and policy analysis, we suggest a âpublic anchor, private innovationâ model that maintains monetary sovereignty and benefits from the productivity gain from tokenization. The paper ends with practical policy recommendations to central banks and international institutions to promote interoperability, reduce disintermediation vulnerabilities and halt financial balkanization.
Jeremy EngâTuck Cheah, Thong Dao, Hung Do, Tapas Mishra
ABSTRACT This paper investigates the stability and coâmovement of cryptocurrency assets in Decentralized Finance (DeFi), with a focus on the Speed of Adjustment (SA), the rate at which shocks dissipate, and prices revert to longârun equilibrium. SA provides a critical measure of market efficiency and portfolio allocation in a highly volatile DeFi environment. We extend conventional cointegration analysis by applying a Fractionally Cointegrated Vector Autoregressive framework, which captures slow error corrections. Rolling estimations generate a timeâvarying series of SA, allowing examination of its evolution and crossâasset spillovers. The results reveal multiple cointegrating relationships, heterogeneous adjustment speeds, and strong contagion effects among DeFi assets. For instance, RPL exhibits rapid yet volatile adjustment, while LDO, BAL, and SNX revert more slowly, reflecting distinct riskâreturn tradeâoffs. Spillover analysis highlights high systemic interconnectedness, underscoring challenges for diversification and contagion management. Overall, dynamic SA emerges as a valuable forwardâlooking indicator of stability in digital asset markets.
Blockchain interoperability is increasingly recognized as the centerpiece for robust interactions among decentralized services. Blockchain ledgers are generally tamper-proof and thus enforce non-repudiation for transactions recorded within the same network. However, such a guarantee does not hold for cross blockchain transactions. When disruptions occur due to malicious activities or system failures within one blockchain network, foreign networks can take advantage by denying legitimate claims or mounting fraudulent liabilities against the defenseless network. In response, this paper introduces InterSnap, a novel blockchain snapshot archival methodology, for enabling auditability of cross blockchain transactions, enforcing non-repudiation. InterSnap introduces cross-chain transaction receipts that ensure their irrefutability. Snapshots of ledger data along with these receipts are utilized as non-repudiable proof of bilateral agreements among different networks. InterSnap enhances system resilience through a distributed snapshot generation process, need-based snapshot scheduling process, and archival storage and sharing via decentralized platforms. Through a prototype implementation based on Hyperledger Fabric, we conducted experiments using on-premise machines, AWS public cloud instances, as well as a private cloud infrastructure. We establish that InterSnap can recover from malicious attacks while preserving cross chain transaction receipts. Additionally, our proposed solution demonstrates adaptability to increasing loads while securely transferring snapshot archives with minimal overhead.
This study addresses key challenges in tax governance for multinational enterprises, including data silos, delayed risk identification, and insufficient privacy protection. It proposes a collaborative governance framework that integrates blockchain-based smart contracts and multimodal machine learning, termed the Blockchain-Enhanced Multimodal Risk Assessment Framework. The framework employs a dual-layer architecture that combines blockchain technology with federated feature engineering. At the data processing layer, it utilizes a multi-chain coordination mechanism based on Adaptive Proof of Stake, enhanced by a node reputation-based dynamic evaluation algorithm to significantly improve consensus efficiency and data transmission security. At the risk prediction layer, it integrates temporal dependency mining with heterogeneous graph neural networks, applying a composite embedding method to extract 89 highly discriminative features from an initial 1,423-dimensional feature space.
Although many unification programs exist in modern physics (GR, QED/QCD, SMEFT, Einstein--Aether theories, Kaluza--Klein models, and string frameworks), none contain the geometric, ledger-based structures developed in this work. This paper introduces several new advances not present in mainstream theories: A new geometric unit of physics: the double-loxodromic Aether cell \(S^{2}\!\times\!\mathbb{T}_{\mathrm{lox}}\) with fixed flux \(\int\omega_{st}=16\pi^{2}\). No existing theory employs this bipartite, distributed-charge geometry. A ledger-based unification principle: \[ A_u\,\mathrm{curl}={F_q}^{2}{\lambda_C}^{2},\]linking magnetic stiffness, square-charge circulation, and Compton-scale kinematics. No standard theory equates these invariants or interprets them as topological constants. Two-geometry representation of charge: electrostatic charge on \(S^{2}\) and magnetic/strong charge on a loxodromic ribbon. This decomposition does not exist in U(1) or SU(3). A new weak interaction: the es--mag overlap scalar \(\chi_x\), whose equation of motion generates the holonomy factor \(H(Z)\). This geometric weak channel is unlike the SU(2) electroweak theory. A parameter-free birefringent invariant: \[ \beta_0=\frac{1}{16\pi^{2}},\]controlling polarization transport in Aether modes; there is no analogue in the Standard Model or in GR. A geometric derivation of the proton mass ratio: \[ \frac{m_p}{m_e}=6\pi^{5},\]arising from the loxodromic packing constant. No mainstream framework provides an analytic expression for this ratio. A unified action with no empirical coefficients: all sector Lagrangians derive from the Aether cell geometry and QMU ledger identities. A new metrological program: independent realizations of \(A_u\,\mathrm{curl}\), and \({F_q}^{2}{\lambda_C}^{2}\) forming the falsifiable closure\[ \mathcal{C}=\frac{A_u\,\mathrm{curl}}{{F_q}^{2}{\lambda_C}^{2}}-1.\] These features constitute a genuinely new unification program: geometric, topological, and metrological, with no free scales and no reliance on SI or empirical fitting.
Property valuation, a foundational method for governments, financial institutions, and insurers to gauge economic stability, remains hindered by opaque, fragmented data practices. Despite technological advancements like Artificial Inteligence (AI) and Web3, valuation processes rely on siloed, non-standardized data that institutions rarely shareâeven internally. This paper identifies systemic barriers to global transparency and proposes a Proptech framework to resolve this disconnect. Unlike market valuation, which leverages AI and algorithms to predict prices, housing valuation depends on manual audits and confidential metrics. This lack of transparency limits governments' capacity to preempt real estate crises or curb speculative risks. By integrating blockchain-enabled data sharing and AI analytics, a decentralized Proptech platform, sharing a global network, could standardize and democratize valuation data, enabling real-time insights for crisis management and evidence-based policymaking. The study highlights how such innovation could transform urban planning, financial markets, and economic resilience, positioning Proptech as a catalyst for equitable, transparent valuation ecosystems. Received: 7 April 2025 | Revised: 29 July 2025 | Accepted: 14 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Pedro Faria: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing â original draft, Writing â review & editing, Visualization, Supervision, Project administration. Peter Finn: Conceptualization, Validation, Investigation, Resources, Writing â review & editing. Tiago Navarro: Conceptualization, Resources, Writing â review & editing, Visualization, Supervision.
This research examines deep-learning and machine-learning models for cryptocurrency price prediction, with a keen focus on Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Solana (SOL). Cryptocurrencies exhibit high volatility, non-linear behavior and are able to react strongly to exogenous events, making their prediction and forecasting challenging. The primary aim of this research is to determine which predictive models yield optimal performance in characterizing these complexities and to provide empirical guidance on real-life investment and risk-management applications. Four approaches were used for this forecasting: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), a combination of LSTM-GRU models, and Stochastic Gradient Descent (SGD) regression. The daily historical data were used to train and test each model on different forecast horizons, and performance was measured accordingly by Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. As shown in the results, it can be observed that GRU exhibited the lowest error rates in the majority of the assets, particularly in short-term predictions. LSTM demonstrated a promising ability to capture long dependencies, whereas the hybrid LSTM-GRU system showed a similar performance proficiency by combining the relative superiorities of the two respective models. On the other hand, the conventional SGD regression was the worst among all the deep-learning algorithms, thereby demonstrating the extreme capability of these algorithms in modelling non-linear time sequences. The results confirm GRU as the most viable model for AI-powered crypto prediction and demonstrate the potential of hybrid architecture, at least in certain situations. This study will contribute to the existing debates about the role of deep learning in predicting financial outcomes and provide valuable insights to traders, analysts, and researchers navigating the uncertainties of the digital asset world.
Wencheng Chen, Jun Wang, JengâShyang Pan, R. Simon Sherratt ¡ 5 authors
With the rapid expansion of sensor networks across domains such as environmental monitoring, industrial automation, and smart healthcare, ensuring secure and reliable data storage in resource-constrained environments has become a critical challenge. Traditional centralized storage systems struggle with data tampering, privacy leakage, and vulnerability to collusion among nodes. Blockchain technology, characterized by decentralization, immutability, and traceability, provides a promising foundation for trustworthy sensor data management. Among various consensus mechanisms, Delegated Proof of Stake (DPoS) has been recognized for its efficiency and low energy consumption, yet it faces two critical issues: limited incentives for ordinary sensor nodes to participate in voting and the risk of collusion that undermines fairness and stability. To overcome these limitations, this study proposes a blockchain-enabled sensor data storage framework incorporating a four-party evolutionary game model. The model explicitly captures the strategic interactions among cluster head nodes, ordinary sensor nodes, competing gateway nodes, and supervisory nodes, while integrating reputation evaluation, penalty enforcement, and supervisory oversight. Through evolutionary game analysis, the proposed framework reveals the stability conditions of node behaviors and identifies strategies that promote fair and secure consensus. Simulation results verify that the mechanism enhances node participation, suppresses collusion, accelerates consensus convergence, and achieves superior throughput and fault tolerance compared with existing schemes. This research provides theoretical insights and practical guidance for designing secure, efficient, and scalable blockchain-enabled sensor network data storage systems.
The security of blockchain systems relies on the honest majority assumption. However, strategic mining threatens this assumption, because selfish miners can gain more block rewards than honest miners by attacks such as withholding blocks. Due to its significant implication, blockchain mining games have been studied in PoW and PoS under various settings using different methods. Nonetheless, this paper argues that the practical limitation of random beacons has not been exploited in strategic mining in PoS blockchains. Current PoS blockchains use random beacons to randomly select validators for each slot. However, the randomness is usually fixed for multiple slots, due to the latency of distributed random beacon protocols. This indicates that validators actually know some information about the election result in the future, which contrasts with the Markov process models in previous analysis. Using this information, this paper presents a close to optimal mining strategy based on an optimal interval scheduling algorithm for each epoch. For proof-of-stake protocols with no propagation delay, we show that a validator with arbitrary proportion of stake can strictly benefit from strategic mining and get significantly higher block rewards than the previous strategies.