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Modeling spatially-correlated data of sensor networks with irregular topologies
2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005.
The physical phenomena monitored by sensor networks, e.g. forest temperature, usually yield sensed data that are strongly correlated in space. We have recently introduced a mathematical model for such data, and used it to generate synthetic traces and study the performance of algorithms whose behavior depends on this spatial correlation [1]. That work studied sensor networks with grid topologies. This work extends our modeling methodology to sensor networks with irregular topologies. We
doi:10.1109/sahcn.2005.1557085
dblp:conf/secon/JindalP05
fatcat:lfoecaef65btlmbr3siolyaevi