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Title EFFICIENT INCENTIVE MANAGEMENT IN REPUTATION-AWARE MOBILE CROWD SENSING

Author(s) Sehrish Khan

Abstract Title: Efficient Incentive Management in Reputation-Aware Mobile Crowd Sensing The revolution in internet of things (IOT) technology have made possible crowdsourcing-based content sharing such as mobile crowd sensing (MCS), which aims to collect content from mass users and share it with participants. The content sharing is especially attractive because, users act as both content provider and user while shared content help in service providing or gaining. In previous researches, the main problem identified is that MWs may give false reporting by sharing low-quality reported data to reduce the effort required and gain reputation. Task related false reporting improved by hiring enough MWs for a task to evaluate the truth worthiness and acceptance of information but there are budget constraints on it. The monetary rewards are used to motivate the data collectors and to encourage the participants to take part in the network activities. As mobile workers are, the main entity to provide services so rewards are given based on reputation system also made mobile workers work efficiency more important in Mobile crowd sensing (MCS). The incentives given to mobile workers (MW) based on reputation play a dramatic increase in service usage and provide a motivation to mobile workers, and build a trust to use the service. In the underlying research, we identified that they have not considered the difficulty level of a task that result in to good reputation on performing a number of easy tasks. While a person, performing difficult task may gain less score for reputation. For this, we proposed four difficulty levels of tasks (DLT) for reputation evaluation on a crowd-sensing network, on which the MW reputation will be evaluated.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2023-01-19

Subject Computer Science

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