A NEURAL DATA-DRIVEN ALGORITHM FOR SMART SAMPLING

Wireless sensor network (WSN) have gained much more attention from researchers. WSN makes the use of sensor nodes generally battery-operated.Their prevalence is threatened by a number of technical difficulties, especially the shortage of energy. To overcome this problem, we propose a smart reduction in data communication by sensors. In order to reduce the measurements, we present a data prediction method based on neural networks which performs an adaptive, data-driven, and non-uniform sampling. Evidently, the amount of possible reduction in required samples is bounded by the extent to which the sensed data is stationary. The proposed method is validated on simulated and experimental data. The results show that it leads to a considerable reduction of the number of samples required (and hence also a power saving) while still providing a good approximation of the data