Energy-Effective Frequency-Based Adaptive Sampling Algorithm for Clustered Wireless Sensor Network

The objective of wireless sensor networks is to extract the synoptic structures (spatiotemporal sequence) of the phenomena of ROI (region of interest) in order to make effective predictive and analytical characterizations. Energy limitation is one of the main obstacles to the universal application of wireless sensor networks. Recently, adaptive sampling strategy is regarded as a much promising method for improving energy efficiency. In this paper, we dedicate to investigating how to regulate sampling frequency of sensor nodes in different clusters dynamically following the change of signal frequency. The adaptive frequency-based sampling (FAS) algorithm proposed in this literature is to measure periodic signal frequency online in different clustered region, afterwards regulate signal sampling frequency following with minimal necessary frequency criterion; as a result, the previous desired level of accuracy is achieved, and the energy consumption is decreased. The simulation results are compared with that of fixed sampling rate approach with respect to energy conservation.