Distributed robust multitask clustering in wireless sensor networks using Multi-Factorial Evolutionary Algorithm

Abstract

When data collected at the local nodes of a wireless sensor network (WSN) are volumetric in nature, there is a need for local processing, then distributed clustering plays an important role. Traditional clustering algorithms based on K-means, K-medoid are not effective in these scenarios for accurate data segregation. Further, there is a requirement of techniques that can effectively handle outliers and noise present in the sensed data. Thus, there is a need to design robust distributed data clustering algorithms. Multi-Task Optimization (MTO) has taken the attention of researchers in the last couple of years after the introduction of Multi-Factorial Evolutionary Algorithm (MFEA). The MFEA can handle several single objective tasks usually related to one another and share implicit knowledge or abilities common to them. In this manuscript, the MFEA is employed to solve two tasks: 1) outlier detection and 2) perform distributed clustering at the nodes of WSN. The resultant algorithm, termed as Distributed MFEA (DMFEA), effectively removes noise and segregates data present at multiple nodes of WSN. Simulation study reveals the superior performance of DMFEA over benchmark algorithms like distributed versions of K-means, particle swarm optimization, and moth-flame optimization on two synthetic and six real-life datasets based on forest fire monitoring, air pollution indexing, Intel laboratory environment sensing, agriculture soil quality labeling, river water quality analysis, and land mine detection. The superior performance of DMFEA is demonstrated based on the Silhouette Index of obtained clusters and the percentage of outliers detected. Additionally, the DMFEA average rank in Kruskal Wallis test, is better over the three comparative algorithms.