[Paper Review] Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique for Nodes Starvation Problem in Wireless Mesh Network

Fuad A. Ghaleb, Bander Ali Saleh Al‐rimy+3 more|arXiv (Cornell University)|Jun 17, 2020

Cooperative Communication and Network Coding45 references4 citations

TL;DR

This paper proposes FA-SCGA-CAA, a fairness-oriented semi-chaotic genetic algorithm for channel assignment in multi-radio multi-channel wireless mesh networks, to mitigate node starvation caused by unfair bandwidth distribution. By integrating a semi-chaotic initialization for chromosomes and a nonlinear fitness function optimizing fairness and interference, the method reduces node starvation by 22% and improves network capacity utilization by 23% compared to existing approaches.

ABSTRACT

Multi-Radio Multi-Channel 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) and vehicular networks. 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, 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 either reduce the total network interference or maximize the total network throughput, which neither guarantees a fair distribution of the channels nor eliminates node starvation. To this end, the Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) was proposed in this paper for Nodes Starvation Problem in Wireless Mesh Networks. FA-SCGA-CAA optimizes fairness based on multiple-criterion using a modified version of the Genetic Algorithm (GA). The modification includes proposing a semi-chaotic technique for creating the primary chromosome with powerful genes. Such a chromosome was used to create a strong population that directs the search towards the global minima in an effective and efficient way. The outcome is a nonlinear fairness oriented fitness function that aims at maximizing the link fairness while minimizing the link interference. Comparison with related work shows that the proposed FA_SCGA_CAA reduced the potential nodes starvation by 22% and improved network capacity utilization by 23%.

Motivation & Objective

• To address the overlooked problem of node starvation due to unfair channel distribution in multi-radio multi-channel wireless mesh networks (WMNs).
• To develop a channel assignment technique that prioritizes fairness among mesh clients, rather than solely maximizing throughput or minimizing interference.
• To enhance network resource utilization by ensuring equitable bandwidth distribution across all mesh nodes.
• To propose a modified genetic algorithm with semi-chaotic initialization to improve convergence toward global optima in the channel assignment problem.

Proposed method

• A modified genetic algorithm (GA) is employed, with a semi-chaotic technique used to generate the initial population’s primary chromosome to enhance genetic diversity and convergence speed.
• The algorithm uses a nonlinear fitness function that simultaneously maximizes link fairness and minimizes interference across the network.
• The semi-chaotic initialization ensures strong genes are embedded in the initial population, promoting effective exploration of the solution space.
• The fitness function evaluates channel assignments based on fairness metrics and interference levels, guiding the search toward optimal, balanced configurations.
• The algorithm iteratively evolves the population through selection, crossover, and mutation, with the goal of minimizing unfairness and interference.
• The method is evaluated in simulation environments to compare fairness, starvation reduction, and capacity utilization against existing channel assignment techniques.

Experimental results

Research questions

• RQ1 To what extent can a modified genetic algorithm reduce node starvation in multi-radio multi-channel wireless mesh networks?
• RQ2 How does semi-chaotic initialization improve the performance of genetic algorithms in channel assignment compared to standard initialization?
• RQ3 Can a fairness-oriented fitness function effectively balance interference reduction and equitable bandwidth distribution in WMNs?
• RQ4 How does the proposed FA-SCGA-CAA method compare to existing algorithms in terms of starvation reduction and network capacity utilization?

Key findings

• The proposed FA-SCGA-CAA technique reduced potential node starvation by 22% compared to existing channel assignment methods.
• Network capacity utilization improved by 23% due to more balanced and efficient channel allocation.
• The semi-chaotic initialization enhanced population diversity and accelerated convergence toward optimal solutions.
• The nonlinear fairness-oriented fitness function successfully minimized interference while maximizing fairness across mesh links.
• The method outperformed traditional approaches that prioritize throughput or interference reduction at the cost of fairness.