Open AccessA Cooperative Block-variant Monitoring Mechanism Based on Spectral Clustering for Internet of Things
Author(s) -
Jiaxi Chen,
Xingchuan Liu,
Jinyun Wu,
Zhiqiang Ding,
Yue Cui,
Simon X. Yang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1659/1/012045
Subject(s) - cluster analysis , block (permutation group theory) , computer science , mechanism (biology) , task (project management) , real time computing , the internet , continuous monitoring , power consumption , internet of things , power (physics) , distributed computing , embedded system , engineering , artificial intelligence , mathematics , philosophy , operations management , geometry , physics , systems engineering , epistemology , quantum mechanics , world wide web
There exist two main defects in traditional monitoring systems: rigid monitoring intensities and fixed task roles, which induce low monitoring efficiencies and large power consumptions. To alleviate these two problems, this paper proposes a cooperative block-variant monitoring mechanism for Internet of Things. This mechanism divides the whole monitoring area into several blocks with different monitoring intensities according to the spatial distribution of monitoring terminals based on spectral clustering. The monitoring intensities, including monitoring densities and frequencies, are decided by the status of monitoring objects in different blocks and the values of pre-setting thresholds. By adjusting the monitoring densities and frequencies in real time, this method makes the monitoring focus on the most critical blocks, which improves the monitoring efficiencies and reduces the overall consumption of system. In addition, adequate switching of the task roles of nodes balances their workloads, and therefore extends the overall life of the monitoring systems. A large number of experiments have been carried out, and the results show that this collaborative monitoring mechanism achieves good performance.