Temporally staggered sensing for field estimation with quantized data in wireless sensor networks

In this paper, we present an optimal sensor staggering strategy to estimate a spatially and temporally varying field using quantized sensor data in wireless sensor networks. In order to predict the field intensity at a particular field point of interest, we first extend ordinary kriging to the case of quantized sensor data. Then, we derive the Average Quantized Kriging Error Variance (AQKEV) of the field as a performance metric which is then numerically minimized to find each sensors optimal sampling instant. Simulation results show that, the proposed sensor staggering strategy which is a function of the temporal correlation of the field yields better AQKEV as compared to the non-staggered and uniformly staggered strategies.