Student Psychology based optimized routing algorithm for big data clustering in IoT with MapReduce framework

Abstract

Clustering plays a fundamental task in the process of data mining, which remains more demanding due to the ever-increasing dimension of accessible datasets. Big data is considered more populous as it has the ability to handle various sources and formats of data under numerous highly developed technologies. This paper devises a robust and effective optimization-based Internet of Things (IoT) routing technique, named Student Psychology Based Optimization (SPBO) -based routing for the big data clustering. When the routing phase is done, big data clustering is carried out using the Deep Fractional Calculus-Improved Invasive Weed Optimization fuzzy clustering (Deep FC-IIWO fuzzy clustering) approach. Here, the Mapreduce framework is used to minimizing the over fitting issues during big data clustering. The process of feature selection is performed in the mapper phase in order to select the major features using Minkowski distance, whereas the clustering procedure is carried out in the reducer phase by Deep FC-IIWO fuzzy clustering, where the FC-IIWO technique is designed by the hybridization of Improved Invasive Weed Optimizer (IIWO) and Fractional Calculus (FC). The developed SPBO-based routing approach achieved effective performance in terms of energy, clustering accuracy, jaccard coefficient, rand coefficient, computational time and space complexity of 0.605 J, 0.935, 0.947, 0.954, 2100.6 s and 72KB respectively.