Impact Analysis of Data Clustering Techniques for Data-Based Topological Formation in WSNs

Abstract

Leveraged by IoT and Industry 4.0 solutions, Wireless Sensor Networks (WSNs) have been proposed as an important alternative for large-scale monitoring applications. Such technology provides sensor nodes with the intelligent and autonomous ability to monitor large areas, create self-organizing structures, detect events and process massive data. In this context, data-driven schemes are increasingly needed. For this, some data clustering techniques (DCTs) are used to tackle common problems in WSNs; however, the vast majority of techniques do not consider the data monitored by the sensors to perform topological changes and provide better network structures. This work addresses an architecture for this type of application and evaluates the impact of different DCTs on network performance and the creation of priority node groups.