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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bruneo, D., Distefano, S., Longo, F., Merlino, G.: An IOT testbed for the software defined city vision: the # smartme project. In: 2016 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–6. IEEE (2016)
Consoli, S., Mongiovic, M., Nuzzolese, A.G., Peroni, S., Presutti, V., Reforgiato Recupero, D., Spampinato, D.: A smart city data model based on semantics best practice and principles. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1395–1400. ACM (2015)
Han, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco (2005)
Kojima, K.: Prediction of individual thermal sensation using unspecified sensors in sensor networks. In: International Conference on Control, Automation and Systems, ICCAS 2008, pp. 123–126. IEEE (2008)
Longo, F., Bruneo, D., Distefano, S., Merlino, G., Puliafito, A.: Stack4things: a sensing-and-actuation-as-a-service framework for IOT and cloud integration. Ann. Telecommun. 72, 1–18 (2016)
Maddox, T.: Smart cities: 6 essential technologies (2016). http://www.techrepublic.com/article/smart-cities-6-essential-technologies/. Accessed 26 Mar 2017
Neuhaus, H., Compton, M.: The semantic sensor network ontology. In: AGILE Workshop on Challenges in Geospatial Data Harmonisation, Hannover, Germany, pp. 1–33 (2009)
Pfisterer, D., Romer, K., Bimschas, D., Kleine, O., Mietz, R., Truong, C., Hasemann, H., Kröller, A., Pagel, M., Hauswirth, M., et al.: Spitfire: toward a semantic web of things. IEEE Commun. Mag. 49(11), 40–48 (2011)
Ploennigs, J., Schumann, A., Lécué, F.: Adapting semantic sensor networks for smart building diagnosis. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 308–323. Springer, Cham (2014). doi:10.1007/978-3-319-11915-1_20
Ploennigs, J., Schumann, A., Lecue, F.: Extending semantic sensor networks for automatically tackling smart building problems. In: Proceedings of the Twenty-first European Conference on Artificial Intelligence, pp. 1211–1212. IOS Press (2014)
Sheth, A., Henson, C., Sahoo, S.S.: Semantic sensor web. IEEE Internet Comput. 12(4), 78–83 (2008)
Wei, W., Barnaghi, P.: Semantic annotation and reasoning for sensor data. In: European Conference on Smart Sensing and Context, pp. 66–76. Springer (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Kushwaha, N., Merlino, G., Francesco, L., Dario, B., Puliafito, A., Vyas, O.P. (2018). Providing Sensor Services by Data Correlation: The #SmartME Approach. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_82
Download citation
DOI: https://doi.org/10.1007/978-3-319-61566-0_82
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61565-3
Online ISBN: 978-3-319-61566-0
eBook Packages: EngineeringEngineering (R0)