Perceived utility maximization-based crowd perception motivation method

Abstract

The invention discloses a perceived utility maximization-based crowd perception motivation method. The method comprises the following steps of: S1, sending a perception task set to a perception service platform by a task publisher; S2, publishing a message to each user in a target user side set in a perception area by the perception service platform after receiving the task set; S3, judging whether the tasks can be completed or not by the users, and if the judging result is positive, returning bidding prices to the perception service platform; S4, selecting the users that win the bidding by the perception service platform on the basis of a motivation model, and distributing the perception task set to the corresponding users that win the bidding; S5, executing perception tasks and sending perception results to the perception service platforms by the users that win the bidding; and S6, integrating the received perception results by the perception service platform, returning the perception results to the task publisher and paying rewards to the users that win the bidding. The method is capable of recruiting more users in a wider range under the condition that the budget constraints and user costs are non-uniformly distributed, so that the overall quality of perception services is improved.

Description

technical field [0001] The invention relates to the field of group intelligence sensing, in particular to a method for motivating group intelligence sensing based on the maximization of perception utility. Background technique [0002] In recent years, with the continuous development of the emerging technology of crowdsensing (Mobile crowdsensing, MCS), more and more applications based on crowdsensing have emerged. Crowdsensing collects data (such as pictures, sounds, locations, etc.) through a large number of ordinary mobile phone users to complete the sensing task. Using the collected sensing data, researchers can implement a variety of sensing applications that meet people's daily needs, including traffic monitoring, environmental monitoring, location-based services, and indoor positioning. However, the realization of crowd sensing depends on a large number of mobile phone users and the improvement of mobile phone perception and communication technologies. On the one ha...

AI Technical Summary

Problems solved by technology

While most of the existing work on MCS assumes that there is a global utility function in the perception area to optimize the platform, this optimization ignores that users in different areas may have heterogeneous costs

In this case, if the traditional mechanism is adopted, trying to recruit a group of users according to the objective function in terms of unit marginal contribution, the recruited users may be highly unevenly distributed among different regions.

The lack of data collected in these regions limits the overall quality of service of the MCS application, even though other regions can receive sufficient data

[0004] Therefore, considering that smartphone users have heterogeneous costs in perception regions, for example, users in different regions have different cost distributions, traditional mechanisms may generate perception loopholes and recruit users in some regions is not enough, resulting in unsatisfactory service quality

In the new situation, the traditional method is no longer applicable, so it is urgent to design a new incentive mechanism to solve this situation

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