MEGA: Maximum-Entropy Genetic Algorithm for Router Nodes Placement in Wireless Mesh Networks

TL;DR

This paper introduces MEGA, a novel maximum-entropy genetic algorithm designed to optimize router node placement in wireless mesh networks, improving coverage and connectivity.

Contribution

The paper presents a new metaheuristic algorithm, MEGA, specifically tailored for the NP-hard problem of router placement in WMNs, demonstrating its effectiveness over existing methods.

Findings

MEGA outperforms COA, FA, GA, and PSO in key metrics.

Simulation results show improved network connectivity.

MEGA achieves better user coverage.

Abstract

Over the past decade, Wireless Mesh Networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivates us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results show a comparison of MEGA with other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA's effectiveness and usability in determining optimal locations for mesh routers.