Congestion Control in Wireless Sensor Networks based on Support Vector Machine, Grey Wolf Optimization and Differential Evolution

Author First name, Last name, Institution

Hafiza Syeda Zainab Kazmi, COMSATS University Islamabad
Nadeem Javaid, COMSATS University Islamabad
Muhammad Imran, King Saud University
Fatma Outay, Zayed University

Document Type

Conference Proceeding

Source of Publication

IFIP Wireless Days

Publication Date

4-1-2019

Abstract

© 2019 IEEE. Transmission rate is one of the contributing factors in the performance of Wireless Sensor Networks (WSNs). Congested network causes reduced network response time, queuing delay and more packet loss. To address this issue, we have proposed a transmission rate control method. The current node in a WSN adjusts its transmission rate based on the traffic loading information gained from the downstream node. Multi classification is used to control the congestion using Support Vector Machine (SVM). In order to get less miss classification error, Differential Evolution (DE) and Grey Wolf Optimization (GWO) algorithms are used to tune the SVM parameters. The comparative analysis has shown that the proposed approaches DE-SVM and GWO-SVM are more proficient than the other classification techniques in terms of classification error.

DOI Link

10.1109/wd.2019.8734265

ISBN

9781728101170

ISSN

2156-9711

Publisher

IEEE Computer Society

Volume

2019-April

Last Page

8

Disciplines

Computer Sciences

Keywords

Congestion Control, Differential Evolution, Grey Wolf Optimization, Support Vector Machine, Transmission Rate, Wireless Sensor Networks

Scopus ID

85068557239

Recommended Citation

Kazmi, Hafiza Syeda Zainab; Javaid, Nadeem; Imran, Muhammad; and Outay, Fatma, "Congestion Control in Wireless Sensor Networks based on Support Vector Machine, Grey Wolf Optimization and Differential Evolution" (2019). All Works. 1038.
https://zuscholars.zu.ac.ae/works/1038

Indexed in Scopus

yes

Open Access

no