Analysis of Density-Based Methods for Selecting Cluster Heads

It is one of the most significant things that can be done to make a Wireless Sensor Networks(WSNs) last for a longer period of time, and one of those things is to reduce the amount of power that each sensor node in the network consumes. Not only does the total number of clusters have a significant bearing on the effectiveness of the network as a whole, but so does the physical location of the Cluster Heads (CHs). Using a method referred to as sensor node clustering, The objective of the study is to do this by utilising density-based clustering algorithms in order to select the cluster head. The application of a sophisticated method known as density-based clustering is one of the techniques that can be employed to move closer to achieving this objective. Density-based algorithms are those that make use of the concept of density to locate groupings of data that share similar properties despite the fact that they differ in size, shape, and density from one another. While selecting a Cluster head, one should give careful consideration to how well it can distribute loads and accommodate growth. In this research, researchers examine a number of different cluster head selection strategies for WSNs that are based on density and compare and contrast them using a variety of parameters.