Energy Efficient Cluster Based Secured Data Aggregation Using Genetic Algorithm for WSN
Aparna Shinde, R. S. Bichkar · 2023
Constricted energy of the sensor nodes is the major significant challenge in the designing of wireless sensor networks (WSNs). Data aggregation is one of the effective strategies to decrease consumption of energy and extend the network lifespan. Cluster-based data aggregation is proven to be the best approach because of its advantages likes efficient data computation, reliability, accuracy, flexibility, scalability, and less computational overhead. However, security, imbalanced energy consumption, locating routing channels, and maintaining clusters are the most critical problems in WSNs that substantially affect on the lifetime of network. In this research paper, we suggested an energy-efficient clustering-based secure data aggregation using a genetic algorithm. The suggested technique addressed the number of clustering-related problems, including load balancing, best cluster heads selection and their distribution above the network etc. The proposed algorithm not only maintains the balance in the network's energy consumption by selecting optimal cluster members in each cluster but also ensures proper selection and distribution of cluster heads above the field. The cost function of the proposed algorithm is designed in such a manner that it chooses energy-balanced compact clusters with optimal cluster heads that are dispersed evenly over the field and nearer to the sink to minimize inter-cluster communication distance. The proposed algorithm also detects and prevents black hole attack for secure data aggregation. The experimental results are examined with the existing approaches to verify the competence of the proposed algorithm. The simulation results show the superior performance of the suggested algorithm in terms of energy usage and lifespan of the network.