Volume 12, Number 3

Swarm Optimization based Gravitational Search Approach for Channel Assignment in
MCMR Wireless Mesh Network

  Authors

Nandini Balusu¹, Suresh Pabboju² and Narsimha G³, ¹Telangana University, India, ²Chaitanya Bharathi Institute of Technology, India and ³JNTUH College of Engineering, India

  Abstract

Wireless Mesh Networks offers cost-efficient and higher network efficiency by utilizing multiple channels multiple radio(MCMR) nodes. Also addition, the amalgamation of multiple radio nodes and multiple hops mesh framework tends to overcome the limitation of single radio networks like the ability to achieve the rising accessible system bandwidth. In spite of these benefits, certain MCMR wireless mesh networks still suffer from performance issues like network connectivity, network throughput degradation whenever network size increases. Thus, an effective channel assignment (CA) approach could minimize the number of interference co-channels and enhance the throughput of the network. Thus, a hybridized form of gravitational search approach and particle swarm optimization is presented in this paper to resolve the issue of CA. The velocity and position updates of PSO are merged with the GSA operations to obtain the best channel with good connectivity. This approach maximizes the capability of exploration and exploitation for global and local searches using PSO and GSA operations. The goal of this methodology is the minimization of a number of interfering links and the maximization of network connectivity and throughput. The experimental results for this approach are carried out using NS2 and compared with previously suggested heuristic optimization algorithms such as Learning Automated and Genetic Algorithm Approach, Improved Gravitational Search Approach and Dynamic particle swarm optimization Approach. The simulation outcome showed a better performance of the suggested methodology compared to existing methodologies.

  Keywords

Wireless Mesh Network, Channel Assignment, Multi-Channel Multi-Radio, Particle Swarm Optimization, Gravitational Search Algorithm.