Distributed IoT Community Detection via Gromov-Wasserstein Metric

Publication Date

1-1-2023

Document Type

Article

Publication Title

IEEE Internet of Things Journal

DOI

10.1109/JIOT.2023.3332740

Abstract

The Internet of Things (IoT) network is a complex system interconnected by different types of devices, e.g., sensors, smartphones, computers, etc.. Community detection is a critical component to understand and manage complex IoT networks. Although several community detection algorithms were proposed, they in general suffer several issues, such as lack of optimal solutions and scalability, and difficulty to be applied to a dynamic IoT environment. In this work, we propose a framework that uses Distributed Community Detection (DCD) algorithms based on Gromov-Wasserstein (GW) metric, namely GW-DCD, to support scalable community detection and address the issues with the existing community detection algorithms. The proposed GW-DCD applies Gromov-Wasserstein metric to detect communities of IoT devices embedded in a Euclidean space or in a graph space. GW-DCD is able to handle community detection problems in a dynamic IoT environment, utilizing translation/rotation invariance properties of the GW metric. In addition, distributed community detection approach and parallel matrix computations can be integrated into GW-DCD to shorten the execution time of GW-DCD. Finally, a new metric, i.e., Gromov-Wasserstein driven mutual information (GWMI), is derived to measure the performance of community detection by considering internal structure within each community. Numerical experiments for the proposed GW-DCD were conducted with simulated and real-world datasets. Compared to the existing community detection algorithms, the proposed GW-DCD can achieve a much better performance in terms of GWMI and the runtime.

Keywords

Community detection, Detection algorithms, Distributed algorithms, Extraterrestrial measurements, Gromov-Wasserstein metric, Heuristic algorithms, Internet of Things, Internet of Things (IoT), Measurement, Mutual information, Point cloud compression, Scalability

Department

Applied Data Science

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