DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-6, Issue-2 ( Feb, 2018 ) | |||||||||
Paper Title |
Bacterial Foraging- Efficient Heterogeneous based K-Mean Genetic Algorithm for CH Selection and Routing in Wireless Mesh Networks | |||||||||
Author Name |
Vikash Shukla, Manish Varshney | |||||||||
Affilition |
M.Tech (Computer Sc. & Engg.) Shri Siddhi Vinayak Institute of Technology (SSVIT) ,Bareilly HOD (Computer Sc. & Engg.) Shri Siddhi Vinayak Institute of Technology (SSVIT) ,Bareilly | |||||||||
Pages |
18-24 | |||||||||
Abstract |
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; | |||||||||
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