We focus on dense networks of wireless sensors used for distributed sampling and interpolation. Sensors periodically sample a physical quantity of interest, e.g. temperature, and report their measurements to a data center. A continuous spatial estimate of the quantity can then be constructed through interpolation. In these cases, density in deployment can be exploited to achieve longer operational time for the network. The challenge is how to select multiple disjoint subsets of sensors such that each of them is individually capable of meeting the quality demands of the monitoring application. In this paper we present an efficient sensor selection scheme for the min-max monitoring application, i.e., minimizing the maximum distortion incurred over space through interpolation. Evaluation with synthetic sensor network data shows that significant reductions in the number of active sensors and energy consumption are possible compared to simpler selection methods.
['Periklis Liaskovitis', 'Curt Schurgers']
Energy Efficient Min-Max Spatial Monitoring with Wireless Sensor Networks