A High Performance Neurocomputing Algorithm for Prediction Tasks in Wireless Sensor Networks

The impact of power efficient wireless sensor networks (WSN) is getting more and more important, as it is built of battery driven sensor nodes (SN). Beside common low power techniques like voltage scaling, variable-rate sampling (VRS) has been exposed as an adequate possibility to minimize the transceiver activity [1]. In this paper a high performance algorithm based on an artificial neural network structure (ANN) for WSN applications is presented which delivers adequate function course prediction, necessary for most precise sampling interval adjustment as described in [2]. Our approach is based on approximation by means of adjustment theory in detail linear regression [3] and algorithm adaption to the underlying low power TelosB SN hardware [4]. It is further implemented in the efficient fixed-point number format, and its experimental results are compared to common prediction algorithms.