Collective unsupervised data mining for heterogeneous wireless integrated network sensor arrays
Erik W. Johnson, Weiyun Huang, Krishnamoorthy Sivakumar, Hillol Kargupta · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
Wireless integrated sensor networks involve information processing over large wired and wireless networks with limited bandwidth. Moreover, the computing capabilities of sensing devices are usually limited because of design restrictions, limited power supply, and other mission specific requirements. Analyzing data sets collected over such sensor networks usually requires downloading voluminous data sets to a central site. This fundamentally impairs the scalability and the overall response time of the application. In a mission critical applications, data analysis in such networks must deliver results within a certain time frame and often slower response completely defeats the purpose of analyzing sensor data. This paper presents a framework for collective data analysis from distributed heterogeneous data that calls for a fundamentally different perspective. This approach analyzes data in a distributed fashion without downloading everything to a central site. We examine several of the unsupervised Collective Data Mining algorithms for performing tasks associated with this extraction of useful information from sensor arrays. We further present a manner in which these algorithms may be incorporated into the knowledge extraction process from the sensor networks, and also propose an architecture geared towards the use of these algorithms.