3.8 Proceedings Paper

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

出版社

IEEE
DOI: 10.1109/INDIN51773.2022.9976088

关键词

cluster-tree; data-based WSNs; clustering

资金

  1. PQ UFPI [05/2021/PROPESQI/PRPG/UFPI]
  2. FAPEPI/MCT/CNPq/CT-INFRA [007/2018]
  3. CNPq/Brazil [407274/2021-9]

向作者/读者索取更多资源

This paper proposes an architecture for large-scale monitoring applications and evaluates the impact of different data clustering techniques on network performance and the creation of priority node groups.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

3.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据