Mobile crowd sensing system reward distribution method based on reputation prediction

Abstract

The invention relates to a mobile crowd sensing system reward distribution method based on reputation prediction, belongs to the technical field of mobile crowd sensing. The core of the method is thateach data provider on a task platform, namely a "worker", is endowed with a reputation which is based on the behavior of the "worker" in a task in the past. A future behavior of the "worker" is predicted based on the reputation, and basic salaries are distributed to the "worker" prior to the start of the next task. 'Workers' continuously efforts to buy a better reputation, thereby obtaining the priority to be subject to the right to be subject, and thus obtaining more rewards. Meanwhile, after the worker actually participates in the task, the difference between the actual performance of the worker and the reputation prediction value is measured, and reward and punishment are conducted on reward on the basis of existing salary. According to the method, the quality of workers can be accurately monitored and evaluated, the reputation and the quality are combined, an incentive mechanism has reward optimality, and attacks of new adders and switch attacks are effectively prevented.

Description

technical field [0001] The invention relates to an incentive method for a mobile crowd sensing system, in particular to a reward distribution method for a mobile crowd sensing system based on reputation prediction, and belongs to the technical field of mobile crowd sensing. Background technique [0002] In recent years, with the rapid development of wireless communication and sensor technology and the rapid popularization of wireless mobile intelligent terminal equipment, smartphones and tablets on the market have integrated global positioning system (global positioning system, GPS), accelerometer, gyroscope, microphone, etc. Perception modules with powerful computing and perception functions such as cameras and cameras enable people to perceive and obtain surrounding environment information anytime and anywhere. A large number of applications based on perception information are also emerging, such as: environmental monitoring, traffic monitoring and social networking applic...

AI Technical Summary

Problems solved by technology

However, because some high-precision data is worthless, it is not what the data requester needs, and even the data itself is wrong, so the restriction is too strong

At the same time, some of these erroneous data are caused by sensor errors, and some are caused by malicious behaviors of users.

Therefore, it is not accurate enough to control the data quality simply by the error value. Malicious data should be detected and eliminated in time.

Images