Research On Incentive Mechanism For Crowdsensing System Based On Contract Theory

Internet and Internet of things influence more industries with the rapid development of it.At the same time,the type and performance of the mobile device's sensor also greatly improve,making its sensing,computing,storage and communication capacity gradually enhanced.These factors also promote the development of the"human-centered" new sensing model crowdsensing.Due to the birth of crowdsensing technology,many complex,large and social sensor tasks can be completed in low cost and short time.This emerging technology,however,still confronts with many problems and challenges,such as the quality and quantity of sensor data can not meet the requirements of task publisher.So incentive mechanism for crowdsensing has important research significance and realistic function.In this paper,we use contract theory to design incentive mechanism according to the unobservability of user effort and type respectively under the symmetric information scenario and asymmetric information scenario,which is aimed to make sensor task publisher gain maximum utility and attract task executor to participate in the task.The main contributions of this paper are as follows:1.Incentive mechanism under condition in unobservability of effort.The unobservability of user effort in crowdsensing is modeled as the problem of moral hazard in contract theory.First,the task distribution process is described by modeling the utility function of task publisher and executors in the process of crowdsensing.Secondly,the incentive mechanism is obtained by solving the optimal contract with the constrained maximization problem.Finally,It proves the optimality of incentive mechanism and discusses the influence of external factors including noise,user's cost and boundary on the publisher and task executors' utility and the optimal effort through the computer simulation.2.Incentive mechanism under condition in unobservability of type.The unobservability of type in crowdsensing is modeled as the problem of adverse selection in contract theory.First of all,The transmission rate model of two kind of tasks in the platform is described.Secondly,the contract is solved by the maximization problem with constraints,using the revelation principle of contract theory,and the optimal scheme discriminant inequality is derived.And the influence of external factors on task publisher is analyzed combined with simulation and the simulation results show that the method is feasible,reasonable and consistent.3.Improvement of the incentive mechanism based on the actual situation.On one hand,combining the difference of effort with type,task executors'are classified.And the contract scheme is derived to enables the task publisher to obtain the maximum utility,on the other hand,differring from the previously unobservability of effort condition which is that only two kinds of results are discussed.We remove restrictions of only two kinds to make it more truthful.We solve the mathematical problem and the opt:imal solution by Lagrange multiplier method.Combined with the simulation,the influence of result,cost and probability on the decision-making is analyzed from the theoretical and practical point of view.