Evaluating an Optimum Weighted Connected Vertex Cover Using Hybrid Genetic Algorithm Over a Wireless Sensor Network

A wireless sensor network (WSN) is a network of sensor nodes dispersed across a large area that wirelessly gathers and transmits environmental data, such as motion and temperature. Wireless sensor networks are crucial for applications like smart city infrastructure, military surveillance, and environmental monitoring because these nodes work together to transfer data to a central hub. Their significance is increased by their capacity for independent operation in challenging or isolated settings. A wireless sensor network (WSN) can be more efficiently seen and evaluated by turning it into a graph, which shows the network's connectivity and data flow. The energy efficiency, communication dependability, and general performance of the network must all be maximized using this graph-based strategy. This paper examines the significance of communication range, Euclidean distance, and hybrid genetic algorithms (HGA) in improving network topology using various graphs. The goal of the research is to find optimum weighted connected vertex cover using HGA algorithm to study WSN and improve the dependability and administration of the network by extending the lifespan of sensor nodes and ensuring efficient energy use through the application of graph theory.