Reputation Management In Participatory Sensing System

In this thesis we consider how reputation management affects the quality of the information sensing of the participation in the system.As information about the participant’s Reputation influences the attitude of the participant interaction and the quality of the sensing system.How reputation mechanisms can provide an incentive for honest behavior and help people make decisions about who to trust.How to easily remove malware and unstable participants without spending additional time.In this thesis we propose a reputation system using a Bayesian model another name for Bayesian Reputation Model(BRM)to calculate and display the reputation of the quality and accuracy of data.We evaluate the effectiveness of our BRM in the context of three real collaborative sensing using real data of taxi drivers.We decide to compare the reputation system already available.Such as,we propose a reputation system,which uses the Gompertz function for evaluating the contribution made by the participating units and to carry out the scheme reputation Real World participation sensing to monitor the application of noise pollution in the urban environment.This reputation system is vulnerable under a more developed and planned the attack.In principle,like any system.Our results show that the proposed reputation system BRM is flexibility and simplicity,and the grounds on the theory of statistics.Validations are done to show the quality of the BRM and accurately assign reputation to participants of different categories.