Genetic Algorithm Based Approach for Integration of Multiple Parameters for Cluster Heads Selection

Description

A novel genetic algorithm (GA) based cluster head (CH) selection optimization, referred as the NCOGA, is discussed here. NCOGA is utilized for heterogeneous wireless sensor networks (WSNs). Here, residual energy, initial energy, distance to the sink, number of neighbors surrounded by the node, load balancing factor and communicating mode decider (CMD) are considered to determine the fitness value of the population during an evaluation. Initially, NCOGA is implemented for single objective and extended further to deal with multi-objective optimization problem of CH selection. Simulations have been conducted and based on analysis NCOGA outperformed the state-of-the-art protocols based on GA on the benchmarks (e.g. stability period, network lifetime, residual energy, and throughput etc.)

Period	13 Nov 2019
Held at	Indian Institute of Technology Indore, India
Degree of Recognition	International