Research On Task Scheduling For Minimizing Data Delay With Privacy Protection In Mobile Crowdsensing

With the rapid development of mobile Internet applications,mobile crowdsensing has emerged as a new paradigm for data collection and processing.Mobile crowdsensing makes use of the wisdom and efforts of the distributed crowds equipped with various mobile devices to collect and process data,which plays an important role in many fields.However,the mobile crowdsensing system relies on the participation of a large number of mobile intelligent device users.In order to motivate mobile intelligent device users to join the paradigm,the privacy protection and incentive mechanism are proved to be the most important problems to be solved.There are a lot of research efforts that focus on privacy protection and incentive mechanism,it is however difficult to combine privacy protection with incentive mechanism without affecting the performance.As a result,many researchers consider privacy protection and incentive mechanism together,and focus on privacy-preserving incentive mechanisms.However,most of existing privacy-preserving incentive mechanisms rely on the trusted third-party,which puts users’ privacy at risk of disclosure when there is no such institution that meets the requirement,or the third-party is attacked.In this paper,we propose a privacy-preserving incentive mechanism PPIM(Privacy-Preserving Incentive Mechanism),which only relies on a third-party that may not be trusted.PPIM uses the auction model to modify the employment between the platform and users and the competition between users.In PPIM,users communicate indirectly with the platform through the third-party.With the help of the third-party,users process their bidding information and then interact with the platform,which ensures that users’ privacy information is hidden,but the platform can extract useful information,so as to achieve the purpose of protecting users’ privacy.We also prove that PPIM satisfies computational efficiency,individual rationality and truthfulness,which is verified byextensive simulation experiments.Privacy-preserving incentive mechanism PPIM determines the reward that each task performer should receive according to the bidding information submitted by users,which means PPIM determines the cost of the platform for performing each task by recruiting mobile device users.However,it is a common problem that the task owner has a monetary budget constraint in a mobile crowdsensing system,and many applications of mobile crowdsensing rely on real-time data,which requires timeliness of data.We consider such scenario,establish a measure of the timeliness of data,and study the task scheduling problem from the perspective of data timeliness under budget constraints.However,the problem is NP-complete,which means there is no polynomial time complexity solution for this problem unless NPP(28),so we turn to the idea of the brute-force algorithm,the beam search algorithm and the heuristic algorithm,we propose several solutions of the problem,analyze the advantages and disadvantages of each solution,and compare the performance of the solutions through extensive simulations.