Distributed Soft Clustering Algorithm for IoT Based on Finite Time Average Consensus

Abstract

Clustering is a common technique for statistical data analysis and it has been widely used in many fields. This article investigates data clustering over the Internet-of-Things (IoT) network. Facing the IoT network challenges, including data volume, communication latency, and information security, we here propose a distributed soft clustering algorithm for the IoT environments where each IoT node may have data from multiple clusters. Considering that the main task of soft clustering is to compute each cluster center in a weighted averaging fashion, our distributed clustering method resorts to an efficient finite-time average-consensus algorithm. Moreover, to make the distributed clustering algorithm more stable and be able to escape from some bad local optimum, we propose a distributed deterministic initialization method based on data variance partitioning. Experiments show that the proposed distributed soft clustering algorithm can offer the same performance as its centralized counterpart in terms of both convergence and clustering quality. Besides, unlike most clustering methods relying on probabilistic initialization, our algorithm could provide stable clustering quality which makes it more suitable for IoT networks. A real-world case study about the clustering analysis for distributed data sets collected by environmental monitoring stations is offered, which shows the potential of our algorithms in practical applications.