Bacterial Foraging- Efficient Heterogeneous based K-Mean Genetic Algorithm for CH Selection and Routing in Wireless Mesh Networks
We consider or merges the enhance coverage ratio and Overlap-Sense Ratio using mobility in heterogonous with
Wireless Mesh Networks (WMN). We study the dead node condition replacement in grid for Efficient Heterogeneous KMean
Genetic Bacterial Foraging Algorithm (EKMG-BFA) is a widely popular, nature-inspired optimization algorithm.
Routing and CH selection are immensely popular techniques for improving the life of the Wireless Mesh Networks (WMN).
In the bi-tier architecture CH selection dies earlier. Therefore, extra care must be taken while selection of CH's. The present
study focuses on solving both of the problems using bacteria foraging algorithm. The CH selection algorithm is improvised
with a new fitness function based on residual energy and distance. And the routing proposed is also of novel fitness which
considers energy and distance. The proposed algorithms are rigorously tested in different scenarios to exhibit their
performance and are compared with traditional methods such as, EADC, DHCR and Hybrid Routing. Experimental results
show that proposed algorithms perform. In wireless Mesh network the transmission calculation is based on received
transmission power Strength and CH selection algorithm is defining with new fitness function for any soft computing based
technique which can be find residual energy, optimality and distance. The routing also proposed with novel fitness which
considers energy and distance and counter max fitness value. The proposed algorithms have to enhance no of alive nodes
with different simulation area of Mesh network which different scenarios to show its performance an enhanced K-means
Genetic Algorithm for optimal clustering in network of data. The aim is to maximize the compactness the cluster head with
large separation between at farthest distance using K-Means approach in between two clusters. The superiority of EKMGBFA
is compared with grouping BFA approach would be simulated using MATLAB 2014Ra.
Keyword - HKMGA, Heterogeneous Based K-Mean Genetic Algorithm; Wireless Module Mesh Networks TRMN; Cluster
Head selection; Routing;