Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks

Ghaleb, Fuad and Al-rimy, Bander and Boulila, Wadii and Saeed, Faisal and Kamat, Maznah and Rohan, Mohd. Foad and Abd Razak, Shukor (2021) Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks. Computational Intelligence and Neuroscience, 2021. ISSN 1687-5265

[img]
Preview
Text
2977954.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB)

Abstract

Wireless Mesh Networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and Internet of Vehicles (IoV). Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth distribution, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms equally distribute the interference reduction on the links to achieve fairness which neither guarantees a fair distribution of the network bandwidth nor eliminates node starvation. In addition, the metaheuristic-based solutions such as genetic algorithm, which is commonly used for WMN, use randomness in creating initial population and selecting the new generation which usually leads the search to local minima. To this end, this study proposes a Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) to solve Nodes Starvation Problem in Wireless Mesh Networks. FA-SCGA-CAA maximizes link fairness while minimizing link interference using a Genetic Algorithm (GA) with a novel nonlinear fairness-oriented fitness function. The primary chromosome with powerful genes is created based on multi-criterion links ranking channel assignment algorithm. Such a chromosome was used with a proposed semi-chaotic technique to create a strong population that directs the search towards the global minima effectively and efficiently. The proposed semi-chaotic was also used during the mutation and parent selection of the new genes. Extensive experiments were conducted to evaluate the proposed algorithm. Comparison with related work shows that the proposed FA_SCGA_CAA reduced the potential node starvation by 22% and improved network capacity utilization by 23%. It can be concluded that the proposed FA_SCGA_CAA is reliable to maintain high node-level fairness while maximizing the utilization of the network resources, which is the ultimate goal of many wireless networks.

Item Type: Article
Identification Number: https://doi.org/10.1155/2021/2977954
Dates:
DateEvent
31 July 2021Accepted
10 August 2021Published Online
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 27 Oct 2021 13:51
Last Modified: 27 Oct 2021 13:51
URI: http://www.open-access.bcu.ac.uk/id/eprint/12337

Actions (login required)

View Item View Item

Research

In this section...