Open Access

ARTICLE

Maximizing Throughput in Wireless Multimedia Sensor Network using Soft Computing Techniques

by Krishnan Muthumayil^1,*, Thangaiyan Jayasankar², Nagappan Krishnaraj³, Mohamed Yacin Sikkandar⁴, Prakash Nattanmai Balasubramanian⁵, Chokkalingam Bharatiraja⁶

1 Department of IT, PSNA College of Engineering and Technology, Dindigul, 624622, Tamil Nadu, India
2 Department of ECE, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, 620024, Tamil Nadu, India
3 School of Computing, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India
4 Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
5 Department of EEE, National Engineering College, Kovilpatti, 628503, Tamil Nadu, India
6 Department of EEE, SRM Institute of Science and Technology, Chennai, 603203, India

* Corresponding Author: Krishnan Muthumayil. Email:

Intelligent Automation & Soft Computing 2021, 27(3), 771-784. https://doi.org/10.32604/iasc.2021.012462

Received 30 August 2020; Accepted 19 October 2020; Issue published 01 March 2021

Abstract

Wireless Multimedia Sensor Networks (WMSN) provides valuable information for scalar data, images, audio, and video processing in monitoring and surveillance applications. Multimedia streaming, however, is highly challenging for networks as energy restriction sensor nodes limit the potential data transmission bandwidth and lead to reduced throughput. WMSN’s two key design challenges, which can be achieved by the clustering process, are energy efficiency and throughput maximization. The use of the clustering technique helps to organise the sensor nodes into clusters, and between each cluster a cluster head (CH) will be chosen. This paper introduces a new Artificial Fish Swarm Optimization Algorithm (AFSA) with a Clustering Technique for Throughput Maximization in WMSN, called AFSA-HC, based on Hill Climbing (HC). The proposed AFSA-HC algorithm includes four key processes to optimise network throughput, namely node initialization, node clustering based on AFSA-HC, data aggregation based on the deflate algorithm, and transmission of hybrid data. To check the adequate performance of the presented AFSA-HC technique, a thorough experimental review will be carried out. The results of the simulation showed that the AFSA-HC approach achieved optimum results for various steps, namely energy consumption, throughput, network life, network stability and packet loss.

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