A novel clustering algorithm for dynamic base station in Wireless Sensor Networks based on DWT and SVD algorithms

Abstract

Wireless Sensor Networks (WSNs) contain many small nodes capable of sensing the environment information as well as processing, storing, and transmitting this information to the base stations via wireless communication. One of the most efficient methods that can be used to reduce the data transition volume and increase the sensor lifetime which leads to the energy reduction in WSN is information clustering. To increase the lifetime of WSN, we would choose those sensors which contain the total cluster information and turn off the rest of sensors in the corresponding cluster to decrease the data redundancy during the transmission. In addition, we should choose the optimal path for data transmission between the selected sensors and Base Stations (BSs). In WSN, the sensor nodes often are arranged into fragment, non-obsolete subgroups called clusters, to increase the transmission efficiency and obtain a better data gathering. In this paper we have applied the Discrete Wavelet Transform (DWT) as well as the Singular Value Decomposition (SVD), two mathematical tolls for the data compression and feature extraction, in addition with the data clustering to reduce the data transmission rate in which it decrease the energy consumption and increase the sensor lifetime in the WSN. To obtain the high accuracy and reduce the redundancy the correlation algorithm is applied once the data gathering process is finished and clustering algorithm is applied.