Mobile crowd sensing user large-scale rapid recruitment method considering social network

Abstract

The invention provides a mobile crowd sensing user large-scale rapid recruitment method considering a social network. The method comprises the following steps: constructing a dynamic incentive tree model according to the social network of an actual user; designing a corresponding winner selection and reward distribution method by taking acquisition of a winner user set with high activeness and high quality as a target; and updating the reputation value of each user according to the comparison between the quality of the data reported by each winner user and the quality of the finally actually submitted data, and identifying malicious users based on the reputation value. The method is verified, analyzed and considered through theories and simulations, and enough users can be rapidly recruited to participate in perceptual activities on a large scale by considering the social network. The mobile crowd sensing user recruitment method based on the reverse auction is designed by considering the social network relationship of the users, and the problems that the number of sensing users is insufficient and the quality of sensing data is not high due to the insufficient number of the sensing users are solved.

Description

technical field

[0001] The invention belongs to the technical field of intelligent information communication, and relates to a large-scale rapid recruitment method of mobile group intelligence sensing users based on reverse auction, which considers the social network relationship of users, and is especially suitable for application scenarios that require a large number of users to participate in data collection or analysis. Background technique

[0002] With the popularization of various intelligent embedded devices and the development of the Internet of Things, social perception activities based on the construction of smart cities have become a hot research topic in the field of information and communication. Commonly used traditional sensing models use systems based on wireless sensor networks for information collection. However, there are many problems in this method, such as insufficient node coverage, inability to obtain information in real time, high installation and m...

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