Importance of machine learning based clustering algorithmsin wireless sensor networks

Multiple sensors dispersed across a large region constitute Wireless Sensor Networks, or WSNs. One base station or sink node receives the data that is collected from various sensors by the cluster head of each cluster, which is made up of the sensors grouped in distinct clusters. Sensor nodes are frequently installed in locations that are difficult for people to access. Normally sensor nodes are powered by batteries. The main challenge in the WSN application is saving energy and increase battery life. It increases the life time of the entire network. One of the best ways to save energy is to choose the right routing scheme. The Low Energy Adaptive Clustering Hierarchy algorithm, or LEACH, is among the greatest routing algorithms in terms of energy efficiency. In LEACH algorithm sensor nodes are arranged as different clusters and then transfer the information. Clustering means divide sensor nodes into smaller number of groups. To form different clusters, we can use different machine learning approaches. Here in this paper explains some machine learning based clustering algorithms which helps in routing of sensor nodes. A network’s lifetime is extended and energy loss is minimized using an effective clustering method. Here explains K-Means clustering algorithm, Density-Based Spatial Clustering (DBSCAN), Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) and FUZZY Clustering. K-Means clustering algorithm means form K different clusters. Initially form K different centroids, then calculate distance from each node to K-different centroids and assign these nodes to different clusters based on the minimum distance. Then centroids are updated and repeat this procedure until the same clusters are formed. In DBSCAN clustering denser nodes are grouped together and forms arbitrary shaped clusters. In this method classify nodes into core point, border point, and outlier point. Based on these classification neighboring points are grouped into one cluster. That is core points with its neighbors along with its border points forms dense clusters. In BIRCH clustering we can cluster large datasets effectively. First generate a small summary of the large datasets. Then this small summary again divided into different clusters. In fuzzy clustering as the name suggest it create fuzzy clusters, and assign a membership degree to each node for each cluster. A node that is closer to one cluster has a higher membership degree. Fuzzy clustering may be useful in situa