In this study, an ensemble neural network (ENN) for Ethereum time series prediction was optimized using particle swarm optimization and genetic algorithms. Additionally, Type-1, Type-2, and Type-3 fuzzy inference systems, of both Mamdani and Sugeno types, were designed for achieving the prediction. The integration performed with these fuzzy systems is achieved by utilizing the results from optimizing the ENN with each optimization algorithm. In this case, the Ethereum data is the series being used for testing the proposal. This approach aims to minimize prediction error by combining the responses of the ENN with Type-1, Type-2, and Type-3 fuzzy systems, each consisting of five inputs and consequently 32 fuzzy rules are utilized. The results show that the Type-1, Type-2, and Type-3 fuzzy system approach yields an accurate prediction of the Ethereum series, as further validated by statistical tests on the results of the fuzzy systems.
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.
Zero-knowledge proof (ZKP) is a popular cryptographic strategy for building a trusted environment, which can be applied to blockchain, electronic voting, and other scenarios. However, ZKP involves a number of computationally intensive operations that limit its widespread adoption in time-sensitive practical applications. The multi-scalar multiplication (MSM) dominates the computations and takes over 70% of the total computation time. This paper proposes a GPU-based acceleration method for ZKP by designing several optimization techniques for MSM. First, this paper constructs a formal mathematical formula of the Pippenger algorithm, which provides a theoretical optimization framework for MSM. Second, by parallelizing the prefix sum, the time complexity of the bucket reduction part of MSM is reduced from $\mathcal{O}\left( {3 \times {2^C}} \right)$ to $\mathcal{O}\left( {2 \times {2^C}} \right)$. Finally, this paper also analyzes the influence of group size on the final calculation time under different data scales and gives a suitable range of group sizes. Compared to the state-of-the-art method, our method can achieve 1.01× to 1.12× for throughput.
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.
Industrial Internet of Things (IoT) depends on a vast array of sensors, processing elements, and actuators to automate data collection, processing, and response. One of the major challenges in the stage of data collection is the accuracy of electromechanical sensors. The concept of Proof-of-Stake (PoS), which is popular in Ethereum blockchain platform, can be used to improve the robustness of data collection. PoS works by selecting a subset of nodes based on their holdings called “stakes”, to perform a specific task. This algorithm is incorporated into IIoT by assigning a stake value to each node internally. The stake is assigned a predefined value during installation and changes periodically based on accuracy. A stake threshold is set so that only those nodes with stake above that value are considered contributors and others act as validators. During the initial setup, everyone validates every other node. Beyond that, the selection of contributors is repeated periodically. At every time interval, validators take the global average of contributors and find the deviation from the observed value. This is compared with a deviation limit. If none are within the limit, it indicates outliers, and the system invalidates itself. Otherwise, the node with the least deviation will emerge victoriously and send its value across to the processing unit. Those nodes with high deviation will have a part of their stake transferred to those with less deviation. Hence, the total stake remains the same, whereas the distribution keeps changing. The advantage is that this process is entirely distributed once all nodes multicast their readings to the validator group. This also ensures that sensors with persistent errors are singled out because they cannot recover their stake.
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.
Based on the reliability mechanism of biological system, the design theories and methods of high reliabe power electronic system, includingautonomous decentralized control,redundancy design, intelligent design, self-reproducion and self-repair, self-adaptive and self-organization control, are firstly described in detail. The research results show that the reliabi1ity of complex power electronic systems is improved markedly in these bionic methods. The power electronics technology can draw some research inspiration from living being. And bionics will have wide applications in the field of power electronics. As a concept Autonomous Cell (AC) and Autonomous Decentralized Power Electric System (ADPES) are defined. It is pointed that AC is autonomous controllable and autonomous cooperative. Based on AC, ADPES is built.A novel autonomous decentralized single-phase inverter of high reliability is inverstigated in the method of bionic. Bionic design methods of power electronics system are original from methodological point of view in this paper. Theresearch in this paper is a practical guide to the applications of bionics on power electronics, especial on design of high reliable power electronics systems.