High Quality Participant Recruitment of Mobile Crowdsourcing over Big Data
- Publication Type:
- Conference Proceeding
- 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, 2018
- Issue Date:
|High Quality Participant Recruitment of Mobile Crowdsourcing over Big Data.pdf||Published version||182.95 kB|
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2018 IEEE. With the rich set of embedded sensors installed in smart-phones, an increasing number of applications have been designed based on these mobile sensors rather than on static sensors in urban areas. In Mobile Crowdsourcing (MCS), participant selection is promoted to save energy and entire incentives. Nevertheless, most of the current researches on this problem assume that the system should get the entire information about the participants. As a result, the suitable tasks are always not allocated to the suitable participants. This practice contributes an inaccurate match between a task and participants, which leads to energy and incentives waste. In view of this challenge, we aim to select participants under a more accurate prediction model, rather than assuming that the information of each participant should be obtained in advance. The prediction model is enabled by the big data of participants' historic evaluation, which are used to predict the user action. Furthermore, a greedy method based on an improved singular value decomposition (SVD), named as SVD-G, is proposed to solve this problem. Finally, the proposed SVD-G method is validated by using the large-scale dataset collected from a real-world project (DaZhongDianPing APP).
Please use this identifier to cite or link to this item: