Open Access

ARTICLE

An Intelligent Cluster Optimization Algorithm for Smart Body Area Networks

Adil Mushtaq¹, Muhammad Nadeem Majeed¹, Farhan Aadil², Muhammad Fahad Khan², Sangsoon Lim^3,*

1 Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan
2 Department of Computer Science, COMSATS University Islamabad, Attock Campus, Pakistan
3 Department of Computer Engineering, Sungkyul University, Anyang, 430010, Korea

* Corresponding Author: Sangsoon Lim. Email:

(This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)

Computers, Materials & Continua 2021, 67(3), 3795-3814. https://doi.org/10.32604/cmc.2021.015369

Received 17 November 2020; Accepted 15 January 2021; Issue published 01 March 2021

Abstract

Body Area Networks (BODYNETs) or Wireless Body Area Networks (WBAN), being an important type of ad-hoc network, plays a vital role in multimedia, safety, and traffic management applications. In BODYNETs, rapid topology changes occur due to high node mobility, which affects the scalability of the network. Node clustering is one mechanism among many others, which is used to overcome this issue in BODYNETs. There are many clustering algorithms used in this domain to overcome this issue. However, these algorithms generate a large number of Cluster Heads (CHs), which results in scarce resource utilization and degraded performance. In this research, an efficient clustering technique is proposed to handle these problems. The transmission range of BODYNET nodes is dynamically tuned accordingly as per their operational requirements. By optimizing the transmission range, the packet loss ratio is minimized, and link quality is improved, which leads to reduced energy consumption. To select optimal CHs the Whale Optimization Algorithm (WOA) is used based on their fitness, which enhances the network performance by reducing routing overhead. Our proposed scheme outclasses the existing state-of-the-art techniques, e.g., Ant Colony Optimization (ACO), Gray Wolf Optimization (GWO), and Dragonfly Optimization Algorithm (DFA) in terms of energy consumption and cluster building time.

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Keywords

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