A Parallel Military-Dog-Based Algorithm for Clustering Big Data in Cognitive Industrial Internet of Things

Abstract

With the advancement of wireless communication, Internet of Things (IoT), and big data, high performance data analytic tools and algorithms are required. Data clustering, a promising analytic technique is widely used to solve the IoT and big-data-based problems, since it does not require labeled datasets. Recently, metaheuristic algorithms have been efficiently used to solve various clustering problems. However, to handle big datasets produced from IoT devices, these algorithm fail to respond within the desired time due to high computation cost. This article presents a new metaheuristic-based clustering method to solve the big data problems by leveraging the strength of MapReduce. The proposed methods leverages the searching potential of military dog squad to find the optimal centroids and MapReduce architecture to handle the big datasets. The optimization efficacy the proposed method is validated against 17 benchmark functions, and the results are compared with five other recent algorithms, namely, bat, particle swarm optimization, artificial bee colony, multiverse optimization, and whale optimization algorithm. Furthermore, a parallel version of the proposed method is introduced using MapReduce [MapReduce-based MDBO (MR-MDBO)] for clustering the big datasets produced from industrial IoT. Moreover, the performance of MR-MDBO is studied on two benchmark UCI datasets and three real IoT-based datasets produced from industry. The F-measure and computation time of the MR-MDBO is compared with the six other state-of-the-art methods. The experimental results witness that the proposed MR-MDBO-based clustering outperforms the other considered algorithms in terms of clustering accuracy and computation times.