User incentive method in mobile crowd-sensing network based on reverse auction

Abstract

The invention claims a user incentive method in a mobile crowd-sensing network based on reverse auction. The method utilizes a reverse auction framework to motivate users to participate in location-related sensing activities, and considers random exit situations in the process of user sensing. The scheme aims to increase the enthusiasm of users to participate in the sensing activities with the goal of maximizing user utility under the premise of a feasible budget. First, a task-centric approach is used to select users as bid winners of sensing tasks to ensure high task coverage. Secondly, according to the completion of the tasks of the bid winners, the users are paid according to a time proportion sharing rule to ensure the authenticity of the incentive. The user incentive method in the mobile crowd-sensing network based on reverse auction has the characteristics of computational validity, individual rationality, budget balance, authenticity and the like. The user incentive method of the invention can enable the users to obtain maximum utility, motivate the users to participate in the tasks, and improve the enthusiasm of the users to participate.

Description

technical field [0001] The invention belongs to the technical field of crowd sensing, and in particular relates to a user incentive method in a mobile crowd sensing network based on reverse auction. Background technique [0002] Currently, smartphones have integrated many sensors, such as GPS, accelerometer, gyroscope, microphone, camera, etc. These sensors can jointly monitor human activities and the surrounding environment, so that people can perceive and obtain information about the surrounding environment anytime, anywhere. Utilizing ubiquitous smartphone users to perceive and collect large-scale data has become a new type of perception. At present, some projects have realized different applications in the fields of environmental monitoring, intelligent transportation, behavior monitoring, and indoor positioning based on mobile crowd sensing. [0003] The data acquisition of mobile crowd sensing applications requires the use of a large number of mobile user terminals fo...

AI Technical Summary

Problems solved by technology

[0005] However, due to the selfishness and randomness of human beings, the design of the mobile crowd sensing incentive method based on the reverse auction model faces certain challenges.

[0006] Problems caused by selfishness: Most participants are motivated by payment incentives from the crowdsensing platform to participate in the sensing task. In the crowdsensing system, user income is a major factor affecting user participation, that is, the higher the user utility, the more motivated they are to participate in perception. higher

[0007] Problems caused by randomness: The random movement of people or unexpected situations make the user leave the perceived location during the execution of the task, resulting in the incomplete task

Zhang Xiang et al. proposed SS-Model (Single-requesterSingle-bid), SM-Model (Single-requester Multiple-bid) and MM-Model (Multiple-requester Multiple-bid) three incentive models, among which SM-Model is the general form of SS-Model, and MM-Model considers the competition between multiple crowd sensing platforms and the competition between multiple users. However, the three incentive models do not fully consider the problem that the winner quits perception with random probability in the process of performing the task. They all assume that the winner can complete the winning perception task. However, the above assumptions are not realistic.

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