Improved Performance Using Fuzzy Possibilistic C-Means Clustering Algorithm in Wireless Sensor Network
Shweta Kushwaha, Kuldeep Singh Jadon · 2020
Small wireless sensors are equipped with micromechanical systems, & wireless communication technologies, digital systems. A Sensor Node understands the limited area for given functionality. In order to gather information on a large area, data centers need to be collected in cooperation with a center sensor node. These co-operative functional sensors create a wireless sensor network.In the previous work, they used K-Medoids Algorithm to form the clusters and Cluster heads for databroadcast towards the base station from the source node. K-Medoid algorithms are primarily disadvantageous because they are not ideal for clustering arbitrary groups of objects. It is that they use compactness as clustering parameters, in short, rather than connectivity, to minimize differences among non-medoid objects or medoids (cluster center). The drawback, even though the first k medoids are selected randomly, can produce different results on different runs in the same dataset. These drawbacks are overcome in Fuzzy Probabilistic C-Means Algorithm & increase the performance of the network. Simulation is performed on MATLAB tool & results show the effectiveness of the proposed work.