Cokriging for cross-attribute fusion in sensor networks

Optimisation of the number of required measurement points and their location is an important research topic in sensor networks. Finding the optimal positions increases spatial coverage and reduces deployment costs. This paper presents an approach for the case that two attributes have to be measured with a different number of available sensors. The proposed cokriging method performs cross-attribute fusion in sensor networks by being based on the analysis of multi-variable spatial correlations. To the best of our knowledge, this scientific work is the first one considering kriging and cokriging interpolations as IF methods. The single-variable ordinary kriging and bi-variable methods were applied to experimental data. The combination of humidity and temperature data in a refrigerated container is used as exemplary case, humidity measurements are considered to be the expensive attribute to measure. The average estimation error for intermediate points was estimated as a function of the number of humidity sensors. When variability is high, data fusion using the bi-variable method produced results as accurate as the single-variable one, without the necessity of deploying a large number of humidity measuring points, by complementing the estimation with temperature measurements.