Optimizing the Performance of Wireless Sensor Network Based on Software Defined Network and Gaussian Filter

Abstract

This paper presents software defining network SDN control in wireless sensor networks (WSN) to estimate packet flow, which relies on a Gaussian filter to filter the transmitted signal. The practical aim of this method is to predict the next step of packet flow earlier, which helps reduce congestion if it occurs. The proposed model (SDN-WSN with Gaussian filter) is applied to enhance signal transmission, reduce data error, reduce congestion network and reduce data overflow data. The methodology of the proposed work can be explained as follows: first: distributing nodes randomly; second: Applying the K-mean cluster to choose to select the optimum position of the head cluster node; third: connecting the network using LEACH protocol. Moreover. In this work, SDN with a Gaussian filter is proposed to control the network and minimize data error. It is possible to achieve that by adding buffer memory for each node to store data. The data transmission process is controlled by SDN, and a Gaussian filter is applied before transmitting data to minimize error data. The proposed method's simulation results proved its effectiveness in prolonging the network lifetime by nearly more than 30% rounds than out-of-date WSN, reducing the average density of memory to 20% than out-of-date WSN, and increasing the average capacitance of memory to 20% than out-of-date WSN