Abstract
Predicting data is a crucial ability for resource-constrained devices like the nodes of a Wireless Sensor Network. In the context of Ambient Intelligence scenarios, in particular, short-term sensory data prediction becomes a key enabler for more difficult tasks such as prolonging network lifetime, reducing the amount of communication required and improving user-environment interaction. In this chapter we propose a software module designed for clustered wireless sensor networks, able to predict various environmental quantities, namely temperature, humidity and light. The software module is supported by an ontology that describes the topology of the AmI scenario and the effects of the actuators on the environment. We applied our module to real data gathered from a public office at our department and obtained significant results in terms of prediction error even in presence of environmental actuators.
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Acknowledgments
This work has been partially supported by the PO FESR 2007/2013 grant G73F11000130004 funding the SmartBuildings project.
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Daidone, E., Milazzo, F. (2014). Short-Term Sensory Data Prediction in Ambient Intelligence Scenarios. In: Gaglio, S., Lo Re, G. (eds) Advances onto the Internet of Things. Advances in Intelligent Systems and Computing, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-319-03992-3_7
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DOI: https://doi.org/10.1007/978-3-319-03992-3_7
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