Water Wave Optimization Algorithm based Congestion Control and Quality of Service Improvement in Wireless Sensor Networks

Mukhdeep Singh Manshahia

doi:10.14738/tnc.54.3567

Abstract

Many researchers have implemented various machine learning algorithms and verify their results with the existing algorithms to control congestion in Wireless Sensor Networks. The major challenge lies in developing an algorithm which optimizes the value of the objective function on the basis of parameters like network throughput, residual energy and packet loss rate of the nodes in the network. An objective function based on these parameters is proposed in the present work. Water wave optimization algorithm is applied on the objective function and an optimum solution is obtained. The proposed approach is compared with the Congestion Detection and Avoidance algorithm (CODA) and Particle Swarm Optimization Algorithm (PSO). The proposed solution outperforms both algorithms on the basis of various performance parameters.

Highlights

Wireless sensor networks (WSNs) are the sensors deployed in physical environment which are controlled by a central receiving unit called Base Station
The results have proved that after implementation of water wave optimization algorithm, Queue length of each node decreases by increasing the number of hops
The network lifetime of nodes in the network is higher as compared to the Congestion Detection and Avoidance algorithm (CODA) and Particle Swarm Optimization Algorithm (PSO)

Summary

Introduction

Wireless sensor networks (WSNs) are the sensors deployed in physical environment which are controlled by a central receiving unit called Base Station. Base Station is responsible for the collection and processing of data collected from various different types of sensors [1, 2]. The sensors deployed in the network are either of the same type or of different types depending on the application [3]. High number sensors are deployed in the industrial environment to enhance coverage, fetch live data and take precise decisions [6]. Lots of potential applications of wireless sensor networks make wireless sensor networks a fast growing market [7, 8]

Methods

Results

Conclusion