Collaboration adaptive filtering model for data reduction in wireless sensor networks

Wireless sensor networks (WSNs) are collecting data periodically by randomly dispersed sensors (motes) that typically exploit high energy in monitoring a specified application. Furthermore, dissemination mode in WSN is resulting noisy or missing information that affects the behaviour of WSN. So, 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, in this article, a distributed data-reduction model (DDRM) is proposed to prolong the network lifetime by decreasing the energy consumption of sensor nodes. It is built upon a distributive clustering model for predicting diffusion-faults in WSN. The proposed model is developed using the RLS adaptive filter integrated with a FIR filter for minimising the amount of transmitted data and provide high convergence of the signals. A dataset of atmospheric changes was handled. The results clarify that DDRM reduced the rate of data transmission to ~20%. Also, it depressed the energy consumption to ~95% throughout the dataset sample. DDRM effectively upgraded the performance of the sensory network by about 19.5%, and hence extend its lifetime.