Data Reduction Using Integrated Adaptive Filters for Energy-Efficient in the Clusters of Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) are collecting data periodically through randomly dispersed sensors (motes) that typically exploit high energy in monitoring a specified application. Furthermore, dissemination mode in WSN usually produces noisy or missing information that affects the behavior of WSN. Data prediction-based filtering is an important approach to reduce redundant data transmissions, conserve node energy, and overcome the defects resulted from data dissemination. Therefore, this letter introduced a novel model was based on a finite impulse response filter integrated with the recursive least squares adaptive filter for improving the signals transferring function by canceling the unwanted noise and reflections accompanying of the transmitted signal and providing high convergence of the transferred signals. The proposed distributed data predictive model (DDPM) was built upon a distributive clustering model for minimizing the amount of transmitted data aimed to decrease the energy consumption in WSN sensor nodes. The results clarified that DDPM reduced the rate of data transmission to ~20%. Also, it depressed the energy consumption to ~95% throughout the dataset sample. DDPM effectively upgraded the performance of the sensory network by about 19%, and hence extend its lifetime.