Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field.
This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes' coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks.
CyberGIS Center for Advanced Digital and Spatial Studies,University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. mhjeong@illinois.edu.
Myeong-Hun
MH
Jeong
School of Mathematical and Geospatial Sciences, RMIT University, Melbourne,Victoria 3000, Australia. matt@duckham.org.
Matt
M
Duckham
coordinate-free algorithm; critical points; decentralized spatial computing; environmental monitoring; geosensor networks; spatial event detection; surface networks