Optimum Sampling in a Spatial-Temporally Correlated Wireless Sensor Network

The optimum space and time sampling in a wireless sensor network (WSN) with spatial-temporally correlated data is studied in this paper. The impacts of the node density in the space domain and the sampling rate in the time domain on the network performance are investigated asymptotically by considering a large network with infinite area, infinite time period, but finite node density and finite temporal sampling rate. Two cases are studied under the constraint of fixed power per unit area. The first case investigates the estimations of the space-time samples collected by the sensors, and the samples are discrete in both the space and time domains. The second case estimates an arbitrary data point on the space-time plane, by interpolating the discrete samples collected by the sensors. The interactions among the various network parameters and their impacts on the system performance are quantitatively identified with exact analytical expressions.