Research Article
An Intelligent Mobile Crowdsourcing Information Notification System for Developing Countries
@INPROCEEDINGS{10.1007/978-3-319-52730-7_14, author={Arun Singh and YueXin Li and Yu Sun and Qingquan Sun}, title={An Intelligent Mobile Crowdsourcing Information Notification System for Developing Countries}, proceedings={Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers}, proceedings_a={MLICOM}, year={2017}, month={2}, keywords={Mobile crowdsourcing Machine learning Information prediction Reliable communication}, doi={10.1007/978-3-319-52730-7_14} }
- Arun Singh
YueXin Li
Yu Sun
Qingquan Sun
Year: 2017
An Intelligent Mobile Crowdsourcing Information Notification System for Developing Countries
MLICOM
Springer
DOI: 10.1007/978-3-319-52730-7_14
Abstract
Crowdsourcing is an important computing technique that taps into the collective intelligence of the public at large to complete business-related tasks and solve many real-time problems. It is changing the way we work, hire, research, make and market. Many developing nations are trying to take advantage of crowdsourcing for information notification to make cost effective system, like real-time transit system, disaster notification system and other services which are available to the masses. However, many of them are still not able to completely benefit from it compared to developed nations. In this paper, we have identified a series of limitations of using crowdsourcing for information gathering and providing real-time notification in developing countries due to their unstable electronic communication infrastructure, their lack of contribution, lack of crowdsource (participating people), less exposure to English language, and unawareness of crowdsourcing. We proposed, and demonstrated, a solution to overcome these limitations by developing a prototype which uses SMS as a reliable method for providing real-time notification and information gathering. Our prototype uses prediction algorithms to fill the gaps in real-time notification. It also uses the prediction of a user’s behavior to provide a better reward and motivational platform, as well as good usability.