A wireless sensor network can be used to collect and process environmental data, which is often of multivariate nature. This work proposes a multivariate sampling algorithm based on component analysis techniques in wireless sensor networks. To improve the sampling, the algorithm uses component analysis techniques to rank the data. Once ranked, the most representative data is retained. Simulation results show that our technique reduces the data keeping its representativeness. In addition, the energy consumption and delay to deliver the data on the network are reduced.

In wireless sensor networks, energy consumption is generally associated with the amount of sent data once communication is the activity of the network that consumes more energy. This work proposes an algorithm based on “Principal Component Analysis” to perform multivariate data reduction. It is considered air quality monitoring scenario as case study. The results show that, using the proposed technique, we can reduce the data sent preserving its representativeness. Moreover, we show that the energy consumption and delay are reduced proportionally to the amount of reduced data.