Ethereum ECCPoW

01/26/2021
by   Hyoungsung Kim, et al.
0

The error-correction code based proof-of-work (ECCPoW) algorithm is based on a low-density parity-check (LDPC) code. The ECCPoW is possible to impair ASIC with its time-varying capability of the parameters of LDPC code. Previous researches on the ECCPoW algorithm have presented its theory and implementation on Bitcoin. But they do not discuss how stable the block generation time is. A finite mean block generation time (BGT) and none heavy-tail BGT distribution are the ones of the focus in this study. In the ECCPoW algorithm, BGT may show a long-tailed distribution due to time-varying cryptographic puzzles. Thus, it is of interest to see if the BGT distribution is not heavy-tailed and if it shows a finite mean. If the distribution is heavy-tailed, then confirmation of a transaction cannot be guaranteed. We present implementation, simulation, and validation of ECCPoW Ethereum. In implementation, we explain how the ECCPoW algorithm is integrated into Ethereum 1.0 as a new consensus algorithm. In the simulation, we perform a multinode simulation to show that the ECCPoW Ethereum works well with automatic difficulty change. In the validation, we present the statistical results of the two-sample Anderson-Darling test to show that the distribution of BGT satisfies the necessary condition of the exponential distribution. Our implementation is downloadable at https://github.com/cryptoecc/ETH-ECC.

READ FULL TEXT
research
12/21/2022

Inference for Non-Stationary Heavy Tailed Time Series

We consider the problem of inference for non-stationary time series with...
research
10/06/2020

Testing Tail Weight of a Distribution Via Hazard Rate

Understanding the shape of a distribution of data is of interest to peop...
research
08/26/2018

Evolutionary dynamics of cryptocurrency transaction networks: An empirical study

Cryptocurrency is a well-developed blockchain technology application tha...
research
05/09/2014

Gaussian-Chain Filters for Heavy-Tailed Noise with Application to Detecting Big Buyers and Big Sellers in Stock Market

We propose a new heavy-tailed distribution --- Gaussian-Chain (GC) distr...
research
06/15/2023

Online Heavy-tailed Change-point detection

We study algorithms for online change-point detection (OCPD), where samp...
research
12/06/2021

Hypothesis Test of a Truncated Sample Mean for the Extremely Heavy-Tailed Distributions

This article deals with the hypothesis test for the extremely heavy-tail...
research
08/10/2018

A survey of data transfer and storage techniques in prevalent cryptocurrencies and suggested improvements

This thesis focuses on aspects related to the functioning of the gossip ...

Please sign up or login with your details

Forgot password? Click here to reset