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Title Incentive Mechanisms for Cooperative tasks in mobile crowdsensing

Author(s) Mehreen Ishaq

Abstract Nowadays Mobile Crowdsensing System (MCS) is served as a building block for the emerging Internet of Things (IoT) applications. A lot of work has been done in the designing of Incentive Mechanism. However, in existing mechanism the performance of workers neglected during recruitment of new workers or termination of existing workers which lack the interest of worker. The main problem is that rewards paid to the participants of performing sensing task are fixed whether the task is easy or extensive. It enhances the interest of participants to earn same amount with easy task and avoid the extensive tasks which results in less competition due to less number of participants for extensive task. In this work, an incentive mechanism based on Reverse Auction with Dynamic Price, in which the total cost of performing task is not fixed and participants awarded with rewards on the basis of difficulty level of task present. System also selects the potential candidate on the basis of their contribution to the system. Firstly, the auctioneer gives bids then the platform selects the lowest bidder. The aim of this design is to give reward of existing workers in the form of lottery or bonus. The proposed design also gives inner lottery to the terminating workers on the basis of their contributions to the system. Present detailed design of Dynamic Price incentive mechanism which is based on Reverse Auction Process. The performance of proposed system model is examining by developing testbed for evaluating and analyzing the datasets and a simulation performs for the collection of data from sensing devices, with respect to metrics such as number of workers, rewards, level of tasks and rank of users. Results are compared for participation of workers in each cell, rewards and the total cost for the task with the existing RAB and RADP scheme.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2021-09-09

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