Research On Scalable Crowdsourcing Model Based On Blockchain

Crowdsourcing is a problem-solving model that involves recruiting a large number of independent workers to collectively complete tasks.The traditional crowdsourcing model is generally based on the centralized central platform architecture,which has single point of failure,privacy disclosure,trust risk and other problems.Blockchain technology provides a solution to the problems of traditional crowdsourcing models based on centralized central platforms.The differences in the ability level of workers participating in crowdsourcing can also affect the quality of completing crowdsourcing tasks,so it is necessary to design a reasonable mechanism to measure the ability level of workers.At the same time,in order to better adapt to crowdsourcing trading scenarios,it is necessary to optimize the blockchain consensus mechanism to improve transaction throughput,reduce communication complexity,and improve crowdsourcing efficiency.This article conducts in-depth research on blockchain based crowdsourcing models and optimization algorithms for Practical Byzantine Fault Tolerance(PBFT).The main research contents are as follows:(1)Aiming at the centralization problem of crowdsourcing model and the uneven ability level of participants,this paper proposes a crowdsourcing mechanism BRCM(Blockchain and Recruitment based Crowdsourcing Model,BRCM)based on blockchain technology and reputation system,which eliminates the dependence of crowdsourcing model on the central platform and solves the problems caused by the central platform.BRCM crowdsourcing mechanism recruits third-party verifiers to be responsible for evaluating the quality of tasks completed by crowdsourcing workers,and uses peer prediction and logarithmic scoring rules to measure the honesty of verifier reports.On this basis,it establishes a reputation system,and proposes a worker and verifier selection mechanism based on reputation and reverse auction to encourage workers to submit high-quality task data and encourage verifiers to submit honest evaluation reports.Through theoretical analysis and simulation experiments,it has been proven that the model in this paper can screen out workers with good abilities to participate in crowdsourcing,and meets the properties of authenticity and individual rationality.(2)The traditional PBFT algorithm has problems such as high communication complexity,poor scalability,and arbitrary selection of main nodes,which affect the efficiency and security of crowdsourcing mechanisms.To address this issue,a consensus mechanism RG-PBFT based on node grouping is proposed.By designing node grouping algorithms and main node election algorithms based on node reputation values and verifiable random functions,different voting weights are set according to node reputation values.Simulation experiments show that the proposed scheme performs well in communication complexity,consensus delay,and throughput performance indicators,and can adapt to large-scale node scenarios in crowdsourcing models,with certain scalability.