Task Diffusion And User Compatibility Oriented Incentive Mechanism Design In Mobile Crowdsensing

The method of data acquisition based on human-centered sensing and computation has become a new application mode and development trend of Internet,and can be used to solve many large-scale data processing problems.Crowdsening is well known as an important data acquisition model for big data.The mobile crowdsening applications rely on the participation and intelligence of a large number of smartphone users.To perform the crowdsensing tasks,the participants have to consume a variety of resources,such as traffic,energy,and memory resources.Thus incentive mechanism design is very necessary and important for most mobile crowdsensing systems.The main contributions of this thesis are as follows:Many incentive mechanisms for crowdsourcing systems have been proposed.However,most of existing mechanisms assume that there are enough participants to perform the crowdsourcing tasks.This assumption may not be true in large-scale task crowdsourcing scenarios.To address the insufficient participation issue,we diffuse the crowdsourcing tasks in the social network.We study two task diffusion models and formulate the problem of minimizing the total cost such that all large-scale tasks can be completed in expectation as the Social Optimization Task Diffusion(SOTD)problem for each model.Two influence estimation methods according to the limited knowledge of social network are presented in this thesis.We design two sealed reverse auction based truthful incentive mechanisms,incentive Mechanisms for Task Diffusion in the Linear Model(MTD-L)and incentive Mechanisms for Task Diffusion in the Independent Cascade Model(MTD-IC).Through both rigorous theoretical analysis and extensive simulations,we demonstrate that the proposed mechanisms achieve computational efficiency,individual rationality,truthfulness,and guaranteed approximation.None of existing work takes into consideration the cooperative compatibility of users for multiple cooperative tasks.In this thesis,we design truthful incentive mechanisms to minimize the social cost such that each of the cooperative tasks can be completed by a group of compatible users.We consider that the mobile crowdsensing is launched in an online community.We study two bid models and formulated the Social Optimization Compatible User Selection(SOCUS)problem for each model.We also define three compatibility models and use real-life relationships from social networks to model the compatibility relationships.We design two reverse auction based incentive mechanisms,Multiple Cooperative Tasks in the Multi-bid model(MCT-M)and Multiple Cooperative Tasks in the Single model(MCT-S).Both of them consist of two steps: compatible user grouping and reverse auction.Through both rigorous theoretical analysis and extensive simulations,we demonstrate that the proposed mechanisms achieve computational efficiency,individual rationality and truthfulness.In addition,MCT-M can output the optimal solution.We then present a user grouping method through neural network model and clustering algorithm,the proposed incentive mechanisms can reduce the social cost and overpayment ratio further with less grouping time.