Short-Term Sensory Data Prediction in Ambient Intelligence Scenarios

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3 Citazioni (Scopus)

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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteAdvances onto the Internet of Things
Pagine89-103
Numero di pagine15
Stato di pubblicazionePublished - 2014

Serie di pubblicazioni

NomeADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cita questo

Milazzo, F. (2014). Short-Term Sensory Data Prediction in Ambient Intelligence Scenarios. In Advances onto the Internet of Things (pagg. 89-103). (ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING).