Distributed Data Clustering in the Context of the Internet of Things: A Data Traffic Reduction Approach

Abstract

The Internet of Things (IoT) emerged with the objective to integrate physical objects into classical computer networks. These objects usually generate larges amount of data, transferring the bottleneck of data processing from sensors to communication systems. For example, analyzing IoT data often demands data centralization before running a mining algorithm. Thus, in order to reduce the data transference commonly required by the data clustering task, this paper proposes a grid-based data summarization approach. The proposed approach uses a single uniform grid to partition the space into cells and to summarize data before centralization. Summarization ensures the reduction of the amounts of data transferred. This approach also includes a data clustering algorithm that deals with the summarized and centralized data. Our preliminary experiments revealed good results in terms of data compression and quality of clustering with a two-dimensional benchmark dataset.