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.
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Communication dans un congrès
Guang R. Gao; Depei Qian; Xinbo Gao; Barbara Chapman; Wenguang Chen. 13th IFIP International Conference on Network and Parallel Computing (NPC), Oct 2016, Xi'an, China. Springer International Publishing, Lecture Notes in Computer Science, LNCS-9966, pp.172-179, 2016, Network and Parallel Computing. 〈10.1007/978-3-319-47099-3_14〉
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Jian An, Ruobiao Wu, Lele Xiang, Xiaolin Gui, Zhenlong Peng. FCM: A Fine-Grained Crowdsourcing Model Based on Ontology in Crowd-Sensing. Guang R. Gao; Depei Qian; Xinbo Gao; Barbara Chapman; Wenguang Chen. 13th IFIP International Conference on Network and Parallel Computing (NPC), Oct 2016, Xi'an, China. Springer International Publishing, Lecture Notes in Computer Science, LNCS-9966, pp.172-179, 2016, Network and Parallel Computing. 〈10.1007/978-3-319-47099-3_14〉. 〈hal-01648003〉

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