Knowledge Aquisition and Data Storage in Mobile GeoSensor Networks

  • Authors:

  • Peggy Agouris;Dimitrios Gunopulos;Vana Kalogeraki;Anthony Stefanidis

  • Affiliations:

  • Department of Geography and Geoinformation Sciences, George Mason University, Fairfax VA 22030;Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA 92521;Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA 92521;Department of Geography and Geoinformation Sciences, George Mason University, Fairfax VA 22030

  • Venue:
  • GeoSensor Networks
  • Year:
  • 2008

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Abstract

In this paper we address the issue of mobility in geosensor networks, inspired by the computational challenges imposed by modern surveillance applications. More specifically we consider networks of optical sensors (video and still cameras), and present a spatiotemporal framework for the management of information captured in them. In this context, mobility is addressed at two levels, considering mobile objects in the area monitored by a network, and mobile sensors observing such objects. Our interest lies on the data acquisition and storage problems that arise in this setting. We identify certain key issues behind the development of a general framework for knowledge acquisition and data storage in geosensor networks, namely: spatiotemporal object modeling; similarity metrics to compare spatiotemporal objects; storing and indexing spatiotemporal objects in a geosensor network; and network management using spatiotemporal techniques. We present some emerging approaches that address these key issues and thus outline a general framework for information and sensor management in mobile sensor networks.