Leveraging Machine Learning in IoT to Predict the Trustworthiness of Mobile Crowd Sensing Data-Reference-Cited by-同舟云学术

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Leveraging Machine Learning in IoT to Predict the Trustworthiness of Mobile Crowd Sensing Data

Published:2020 Issue: Volume: Page:235-244
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Loglisci Corrado,Zappatore Marco,Longo Antonella,Bochicchio Mario A.,Malerba Donato

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-59491-6_22

Reference14 articles.

1. Guo, B., et al.: Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM Comput. Surv. 48 (2015). https://doi.org/10.1145/2794400

2. Huang, K.L., Kanhere, S.S., Hu, W.: Are you contributing trustworthy data?: the case for a reputation system in participatory sensing. In: 13th International Symposium on Modeling Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2010, pp. 14–22 (2010). https://doi.org/10.1145/1868521.1868526

3. Karkouch, A., Mousannif, H., Moatassime, H.A., Noël, T.: Data quality in internet of things: a state-of-the-art survey. J. Netw. Comput. Appl. 73, 57–81 (2016). https://doi.org/10.1016/j.jnca.2016.08.002

4. Loglisci, C., Appice, A., Malerba, D.: Collective regression for handling autocorrelation of network data in a transductive setting. J. Intell. Inf. Syst. 46(3), 447–472 (2016). https://doi.org/10.1007/s10844-015-0361-8

5. Loglisci, C., Ceci, M., Malerba, D.: Discovering evolution chains in dynamic networks. In: New Frontiers in Mining Complex Patterns - Revised Selected Papers, pp. 185–199 (2012). https://doi.org/10.1007/978-3-642-37382-4_13

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