Providing sensor services by data correlation: The #SmartME approach

TitleProviding sensor services by data correlation: The #SmartME approach
Publication TypeConference Proceedings
Year of Conference2018
AuthorsKushwaha, N., G. Merlino, F. Longo, D. Bruneo, A. Puliafito, and O.P.. Vyas
Conference NameAdvances in Intelligent Systems and Computing
Volume611
Pagination864-874
PublisherSpringer Verlag
Conference LocationTorino, Italy - 10-12 July 2017
ISBN Number9783319615653
ISBN21945357
KeywordsBuilding applications, Complex networks, Correlations, Data correlations, Data mining, Digital storage, Environmental parameter, Real time sensors, Semantic representation, Semantic Web, sensor networks, SmartME, Stack4Things
Abstract

In the current era Internet is the most used medium for sharing and retrieving the information for building applications which are commonly developed for enhancing the user experience in terms of comfort, communication. For this, the need of real-time sensor data gains importance. The data collected from the physical objects should be easily available for different applications. Semantic representation of the sensor data directly addresses the problem of storing it in logical, easily accessible and extensible manner. Our paper works towards converting the already collected sensor data of the #SmartME project into semantic format and also proposes real-time storage of semantically enriched sensor data. To build applications using these sensor data the authors consider mainly three kinds of sensors, i.e., Temperature, Humidity, Pressure. Predicting the observed value of any sensor data is the main aim of this work. The analysis leverages other sensors & environmental parameters such as Date, Time, Longitude, Latitude, Altitude etc. Correlation among these parameters and the accuracy of the predicted results showed the suitability of our proposed idea. © Springer International Publishing AG 2018.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85026317817&doi=10.1007%2f978-3-319-61566-0_82&partnerID=40&md5=7b4cb65c4b09ea03df77bbea893f4002
DOI10.1007/978-3-319-61566-0_82