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Erratum: Li, S.; Miao, L.; Xu, H.; Zhou, X. Searchable Encryption Scheme for Personalized Privacy in IoT-Based Big Data. Sensors 2019, 19, 1059
Open AccessArticle

A Node Density Control Learning Method for the Internet of Things

by Shumei Lou 1, Gautam Srivastava 2,3,* and Shuai Liu 4,5
1
College of Mathematics and Computer Science, Xinyang Vocational and Technical College, Xinyang 464000, China
2
Department of Mathematics & Computer Science, Brandon University, Brandon, MB R7A6A9, Canada
3
Research Center for Interneural Computing, China Medical University, Taichung 40402, Taiwan
4
College of Computer Science, Inner Mongolia University, Hohhot 010012, China
5
Hunan Provincial Key Laboratory of Intelligent Computer and Laungrage Information Processing, Hunan Normal University, Changsha 410081, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(15), 3428; https://doi.org/10.3390/s19153428
Received: 17 July 2019 / Revised: 31 July 2019 / Accepted: 2 August 2019 / Published: 5 August 2019
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%. View Full-Text
Keywords: Internet of Things; wireless sensors; mobile nodes; density control; probability Internet of Things; wireless sensors; mobile nodes; density control; probability
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Lou, S.; Srivastava, G.; Liu, S. A Node Density Control Learning Method for the Internet of Things. Sensors 2019, 19, 3428.

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