Design And Optimization Of Social Fairness-aware Incentive Mechanism In Mobile Crowdsensing

With the rapid development of sensor technology and wireless communication,as well as the proliferating of the hand-held mobile device,mobile crowdsensing has been catalyzed and evolved.The success of mobile crowdsensing often depends on the active participation of a large number of participants and high quality of sensing data contributed by them.However,completing the collection of sensing data often consumes a high cost in terms of resource consumption and even exposes participants to potential privacy risks,which greatly inhibits the enthusiasm of the participants.In addition,if users in mobile crowdsensing are treated unfairly,users who suffer from unfair treatment resulting in impaired individual utilities will take the initiative to leave the platform,which will seriously affect the availability of the platform,and even the leaving users exceed a certain threshold will lead to paralysis of the platform.Hence,fair and effective incentive mechanisms are of great importance to promote the sustainable development of mobile crowdsensing by taking the different needs of mobile crowdsensing system,social fairness and appropriate incentive method into consideration.Aiming at the service exchange contest dilemma in crowdsensing,a game-theoretic framework of selection fairness-aware multi-level two-sided rating protocol was designed to stimulate users to cooperate with each other,balance the service request and service provision between users and maximize social utility.We model the interaction process in service exchange applications using all-pay contests as an asymmetric sequential game consists of three-stage and analyze the equilibrium results.A multi-level two-sided rating protocol is proposed consisting of multi-level rating labels and a two-sided rating update rule,where multi-level means that the rating label representing the social status of users is discrete multivariate rather than simply binary,and two-sided means that the update of rating is applied on both the requesters and the participants.By integrating the one-shot deviation principle and the principle of fairness to quantify necessary and sufficient conditions for a sustainable multi-level two-sided rating protocol,we formulate the problem of selecting the optimal design parameters to maximize the social utility among all sustainable multi-level two-sided rating protocols,and design a low-complexity algorithm to select optimal design parameters via a two-stage procedure in an alternate manner.Extensive evaluation results demonstrate how intrinsic parameters impact on the recommended strategy,design parameters,as well as the performance gain of our proposed multi-level two-sided rating protocol.Aiming at the dilemma that tasks are constantly emerging and participant resources are relatively limited in mobile crowdsensing,a Max-min fairness-aware incentive mechanism was designed to stimulate participants to improve their sensing level and balance the requesters' utility distribution.We introduce the concept of Max-min fairness and formalize the Max-min fairness-aware multi-task allocation problem under the constraint of the tasks' sensing time threshold.The interaction process between requesters and participants is modeled as a Stackelberg game consisting of multi-leader and multi-follower,and then transform the fairness-aware multi-task allocation problem to the Stackelberg-game fairnessaware incentive mechanism design problem.We compute the unique Nash equilibrium for the sensing plan game of participants and the reward declaration game of requesters,and analyze the Stackelberg equilibrium point that the utilities of both requesters and participants are maximized.A greedy Max-min fairness-aware multi-task allocation algorithm is designed to solve a fair multi-task allocation solution meeting the sensing time threshold.Simulation results further demonstrate the impact of intrinsic parameters on social utility and price of fairness,as well as the feasibility and effectiveness of our proposed Max-min fairness-aware incentive mechanism.