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Intelligent Acquisition Techniques for Sensor Network Data

Intelligent Acquisition Techniques for Sensor Network Data

Elena Baralis, Tania Cerquitelli, Vincenzo D’Elia
Copyright: © 2010 |Pages: 20
ISBN13: 9781605663289|ISBN10: 160566328X|EISBN13: 9781605663296
DOI: 10.4018/978-1-60566-328-9.ch008
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MLA

Baralis, Elena, et al. "Intelligent Acquisition Techniques for Sensor Network Data." Intelligent Techniques for Warehousing and Mining Sensor Network Data, edited by Alfredo Cuzzocrea, IGI Global, 2010, pp. 159-178. https://doi.org/10.4018/978-1-60566-328-9.ch008

APA

Baralis, E., Cerquitelli, T., & D’Elia, V. (2010). Intelligent Acquisition Techniques for Sensor Network Data. In A. Cuzzocrea (Ed.), Intelligent Techniques for Warehousing and Mining Sensor Network Data (pp. 159-178). IGI Global. https://doi.org/10.4018/978-1-60566-328-9.ch008

Chicago

Baralis, Elena, Tania Cerquitelli, and Vincenzo D’Elia. "Intelligent Acquisition Techniques for Sensor Network Data." In Intelligent Techniques for Warehousing and Mining Sensor Network Data, edited by Alfredo Cuzzocrea, 159-178. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-328-9.ch008

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Abstract

After the metaphor “the sensor network is a database,” wireless sensor networks have become an important research topic in the database research community. Sensing technologies have developed new smart wireless devices which integrate sensing, processing, storage and communication capabilities. Smart sensors can programmatically measure physical quantities, perform simple computations, store, receive and transmit data. Querying the network entails the (frequent) acquisition of the appropriate sensor measurements. Since sensors are battery-powered and communication is the main source of power consumption, an important issue in this context is energy saving during data collection. This chapter thoroughly describes different clustering algorithms to efficiently discover spatial and temporal correlation among sensors and sensor readings. Discovered correlations allow the selection of a subset of good quality representatives of the whole network. Rather than directly querying all network nodes, only the representative sensors are queried to reduce the communication, computation and power consumption costs. Experiments with different clustering algorithms show the adaptability and the effectiveness of the proposed approach.

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