This study aims to improve execution time, CPU utilization, network efficiency, and scalability while upholding robust security measures. The IOTA-DLT-based RA-WRW algorithm is developed in Python, considering node resources and transaction weights for optimal tip selection—verification procedures confirming tip authenticity and transaction validity. The algorithm significantly improves IOTA network transaction processing efficiency tips exhibit high authenticity and consistency, affirming the algorithm's effectiveness. The research presents the innovative IOTA-DLT RA-WRW algorithm, which integrates Resource Allocation (RA) and Weighted Random Walk strategies. This novel approach tackles challenges like lazy tip selection, network congestion, and double spending. By performance parameter and the tip selection process, the algorithm improves comparative analyses against existing methods and confirms the superior performance of our model, boasting high accuracy, f-measure, recall, precision, and scalability in distributed ledger transactions, significantly enhancing the IOTA network's transaction processing capabilities. DOI:https://doi.org/10.52783/pst.1006
The IOTA Tangle, a Directed Acyclic Graph (DAG)-based distributed ledger, is popular for its scalability and suitability for IoT applications, offering fee-less transactions. A critical component of IOTA’s architecture is the Cumulative Weight Calculation (CWC), essential for its tip selection mechanism. This paper introduces an optimization of the IOTA Reference Implementation (IRI) CWC process originally implemented using Breadth-First Search (BFS) by employing Depth-First Search (DFS) and Iterative Deepening Search (IDS) algorithms. We present a comparative analysis of these methods, demonstrating that DFS and IDS provide significant improvements in computational efficiency, particularly beneficial for IoT devices with limited processing capabilities. Our findings are substantiated through a series of experiments on a Tangle snapshot, highlighting the enhanced performance and reduced resource utilization of the proposed methods. This study contributes to the ongoing development of DAG-based distributed ledgers, offering insights into more efficient algorithmic solutions for large-scale, decentralized networks.
Bitcoin is a kind of Cryptocurrency and now is one of type of investment on the stock market.Stock markets are influenced by many risks of factor.And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes sudden fall without knowing influence behind it on the stock market.Because it's fluctuations, there's a need and automation tool to predict bitcoin on the stock market.This research study learns how to create model prediction bitcoin stock market prediction using LSTM, LSTM (Long Short Term Memory) is another type of module provided for RNN later developed and popularized by many researchers, like RNN, the LSTM also consists of modules with recurrent consistency.The contribution of this study is providing a new forecasting framework for bitcoin price prediction can overcome and improve the problem of input variables selection in LSTM without strict assumptions of data assumption.The results revealed its possible applicability in various cryptocurrencies prediction, industry instances such as medical data or financial timeseries data.The Method that we apply on this research, also technique and tools to predict Bitcoin on stock market yahoo finance can predict the result above $ 12600 USD for next days after prediction, in the last section we make conclusions and discuss future works.The proposed methodology is then applied to train a simple Long Short Term Memory (LSTM) model to predict the bitcoin price for the upcoming 5 days.When the LSTM model is trained with a suitable data chunk, thus identified, sustainable results are found for the prediction.In the end of this paper, the work culminates with future improvements.
ABSTRACT The Ethereum block chain as a decentralized platform is so successful that many applications deployed on it. However, for the inherent transparency properties and the lack of privacy, deploying a financial application on top of it is always a challenge. In this paper, we tackle this challenge and propose an anonymous sealed-bid auction protocol based on time-released encryption atop Consortium Block chain. We adopt a strict digital certificate-based identity mechanism of the consortium block chain to permit legitimate participants, and utilize the blind signature based on elliptic curve technology to allowing anonymous participation. Moreover, a timed release public key encryption algorithm is adopted to encrypt bids and prevent auctioneer from colluding with bidders. This is completely different from the method (zero-knowledge proof) used in other papers to prevent collusion between auctioneer and bidder. We provide a specific analysis of our protocol, which shows that our protocol meets anonymity and applicability. KEYWORDS Consortium Block chain, Smart Contract, Sealed-Bid Auction, Time-Released Encryption, Blind signature
The Ethereum block chain as a decentralized platform is so successful that many applications deployed on it. However, for the inherent transparency properties and the lack of privacy, deploying a financial application on top of it is always a challenge. In this paper, we tackle this challenge and propose an anonymous sealed-bid auction protocol based on time-released encryption atop Consortium Block chain. We adopt a strict digital certificate-based identity mechanism of the consortium block chain to permit legitimate participants, and utilize the blind signature based on elliptic curve technology to allowing anonymous participation. Moreover, a timed release public key encryption algorithm is adopted to encrypt bids and prevent auctioneer from colluding with bidders. This is completely different from the method (zero-knowledge proof) used in other papers to prevent collusion between auctioneer and bidder. We provide a specific analysis of our protocol, which shows that our protocol meets anonymity and applicability.