Incentive Mechanism For Mobile Crowd Sensing Based On Location Coverage With Budget

As a product of the Internet of Things era,mobile crowd sensing combines the idea of crowdsourcing and the capability of mobile sensing.Mobile crowd sensing relies on the unconscious collaboration of many users to accomplish their sensing tasks,and becomes a new data acquisition mode.As the mobile crowd sensing tasks usually require a large number of participants and high-quality data,there may be competitions among these participants.The participants and the crowdsensing platform usually want to maximize their own utilities,respectively.On the other hand,due to the fact that the sensing data is usually related to the locations,it is noteworthy to collect a certain number of data in multiple locations.Therefore,it is very important to design different incentive mechanisms for different sensing scenarios.The paper studys the task assignment and location selection between sensing platform and participants in mobile crowd sensing system.By using the game theory methods to design different task assignment mechanisms for different scenarios.The objectives are maximizing liquid welfare and location coverage for mobile crowd sensing.The sensing platform requires the perception of data from multiple locations,and participants need to collect data within the perceived location.In this paper,the assignment rights of tasks are handed over to the above two types of objects.First,the sensing platform will be charged a deposit when the tasks are assigned to the appropriate participants,participant uses auction methods to assign tasks to participants based on their value and budget,then gives the deposit they need to pay.The goal of this assignment mechanism is truthfulness,budget feasibility,assignment feasibility and achieves the approximate ratio of constant for liquid welfare.Then,for the winners who already have the tasks,they need to perform data acquisition within the sensning locations to accomplish the assigned tasks.We design a dynamic incentive mechanism to help the participants to select the locations before they perform the tasks,motivating the participants to disperse to various locations to perform the tasks in order to achieve the location coverage.Eventually,the Nash equilibrium will be achieved for location selection.Through both rigorous theoretical analyses and extensive simulations,the above mechanisms can meet the expected goals in meeting the relevant properties.