@inproceedings{ddd9a3e4bd68494184abd70970679115,
title = "FCM: A fine-grained crowdsourcing model based on ontology in crowd-sensing",
abstract = "Crowd sensing between users with smart mobile devices is a new trend of development in Internet. In order to recommend the suitable service providers for crowd sensing requests, this paper presents a Fine-grained Crowdsourcing Model (FCM) based on Ontology theory that helps users to select appropriate service providers. First, the characteristic properties which extracted from the service request will be compared with the service provider based on ontology triple. Second, recommendation index of each service provider is calculated through similarity analysis and cluster analysis. Finally, the service decision tree is proposed to predict and recommend appropriate candidate users to participate in crowd sensing service. Experimental results show that this method provides more accurate recommendation than present recommendation systems and consumes less time to find the service provider through clustering algorithm.",
author = "Jian An and Ruobiao Wu and Lele Xiang and Xiaolin Gui and Zhenlong Peng",
note = "Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2016.; 13th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2016 ; Conference date: 28-10-2016 Through 29-10-2016",
year = "2016",
doi = "10.1007/978-3-319-47099-3\_14",
language = "英语",
isbn = "9783319470986",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "172--179",
editor = "Xinbo Gao and Barbara Chapman and Depei Qian and Wenguang Chen and Gao, \{Guang R.\}",
booktitle = "Network and Parallel Computing - 13th IFIP WG 10.3 International Conference, NPC 2016, Proceedings",
}