Learning In Wireless Sensor Networks for Environmental Monitoring

Abstract

This paper brings two main contributions to the use of learning techniques in a sensor network. First, an approach which combines time series prediction and model selection for reducing the amount of communication called adaptive model selection, is to let the sensors determine in an automated manner a prediction model that does not only fits their measurements, but that also reduces the amount of transmitted data. The second main contribution is the design of a distributed approach for modeling sensed data, based on the principal component analysis. We first show that the sensor measurements can be transformed along a routing tree in such a way that (i) most of the variability in the measurements is retained, and (ii) the network load sustained by sensor nodes is reduced and more evenly distributed.