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Knowledge Discovery for Sensor Network Comprehension

Knowledge Discovery for Sensor Network Comprehension

Pedro Pereira Rodrigues, João Gama, Luís Lopes
Copyright: © 2010 |Pages: 18
ISBN13: 9781605663289|ISBN10: 160566328X|ISBN13 Softcover: 9781616922092|EISBN13: 9781605663296
DOI: 10.4018/978-1-60566-328-9.ch006
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MLA

Rodrigues, Pedro Pereira, et al. "Knowledge Discovery for Sensor Network Comprehension." Intelligent Techniques for Warehousing and Mining Sensor Network Data, edited by Alfredo Cuzzocrea, IGI Global, 2010, pp. 118-135. https://doi.org/10.4018/978-1-60566-328-9.ch006

APA

Rodrigues, P. P., Gama, J., & Lopes, L. (2010). Knowledge Discovery for Sensor Network Comprehension. In A. Cuzzocrea (Ed.), Intelligent Techniques for Warehousing and Mining Sensor Network Data (pp. 118-135). IGI Global. https://doi.org/10.4018/978-1-60566-328-9.ch006

Chicago

Rodrigues, Pedro Pereira, João Gama, and Luís Lopes. "Knowledge Discovery for Sensor Network Comprehension." In Intelligent Techniques for Warehousing and Mining Sensor Network Data, edited by Alfredo Cuzzocrea, 118-135. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-328-9.ch006

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Abstract

In this chapter we explore different characteristics of sensor networks which define new requirements for knowledge discovery, with the common goal of extracting some kind of comprehension about sensor data and sensor networks, focusing on clustering techniques which provide useful information about sensor networks as it represents the interactions between sensors. This network comprehension ability is related with sensor data clustering and clustering of the data streams produced by the sensors. A wide range of techniques already exists to assess these interactions in centralized scenarios, but the seizable processing abilities of sensors in distributed algorithms present several benefits that shall be considered in future designs. Also, sensors produce data at high rate. Often, human experts need to inspect these data streams visually in order to decide on some corrective or proactive operations (Rodrigues & Gama, 2008). Visualization of data streams, and of data mining results, is therefore extremely relevant to sensor data management, and can enhance sensor network comprehension, and should be addressed in future works.

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