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基于皮亚诺分形的节能工业物联网软件定义网络

Energy-Efficient Industrial Internet of Things Software-Defined Network by Means of the Peano Fractal.

作者信息

Moreno Escobar Jesus Jaime, Morales Matamoros Oswaldo, Lina Reyes Ixchel, Tejeida-Padilla Ricardo, Chanona Hernández Liliana, Posadas Durán Juan Pablo Francisco

机构信息

Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Mexico City 07340, Mexico.

Escuela Superior de Turismo, Instituto Politécnico Nacional, Mexico City 07630, Mexico.

出版信息

Sensors (Basel). 2020 May 18;20(10):2855. doi: 10.3390/s20102855.

DOI:10.3390/s20102855
PMID:32443435
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7288025/
Abstract

The Industrial Internet of Things (IIoT) network generates great economic benefits in processes, system installation, maintenance, reliability, scalability, and interoperability. Wireless sensor networks (WSNs) allow the IIoT network to collect, process, and share data of different parameters among Industrial IoT sense Node (IISN). ESP8266 are IISNs connected to the Internet by means of a hub to share their information. In this article, a light-diffusion algorithm in WSN to connect all the IISNs is designed, based on the Peano fractal and swarm intelligence, i.e., without using a hub, simply sharing parameters with two adjacent IINSs, assuming that any IISN knows the parameters of the rest of these devices, even if they are not adjacent. We simulated the performance of our algorithm and compared it with other state-of-the-art protocols, finding that our proposal generates a longer lifetime of the IIoT network when few IISNs were connected. Thus, there is a saving-energy of approximately 5% but with 64 nodes there is a saving of more than 20%, because the IIoT network can grow in a 3 n way and the proposed topology does not impact in a linear way but log 3 , which balances energy consumption throughout the IIoT network.

摘要

工业物联网(IIoT)网络在流程、系统安装、维护、可靠性、可扩展性和互操作性方面产生了巨大的经济效益。无线传感器网络(WSN)使IIoT网络能够在工业物联网传感节点(IISN)之间收集、处理和共享不同参数的数据。ESP8266是通过集线器连接到互联网以共享其信息的IISN。在本文中,基于皮亚诺分形和群体智能设计了一种WSN中的光扩散算法,用于连接所有IISN,即无需使用集线器,只需与两个相邻的IINS共享参数,假设任何IISN都知道其他设备的参数,即使它们不相邻。我们模拟了算法的性能,并将其与其他先进协议进行比较,发现当连接的IISN较少时,我们的方案能使IIoT网络的寿命更长。因此,大约能节省5%的能源,但当有64个节点时,能节省超过20%的能源,因为IIoT网络可以以3的n次方方式增长,且所提出的拓扑结构不是以线性方式而是以log 3的方式影响,这平衡了整个IIoT网络的能源消耗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/1da36394648d/sensors-20-02855-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/521c2384e474/sensors-20-02855-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/121d2aa40dac/sensors-20-02855-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/8aa683551858/sensors-20-02855-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/c8c267f31fbc/sensors-20-02855-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/a4c5166e1713/sensors-20-02855-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/1efb1c95d128/sensors-20-02855-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/6e4c979cc552/sensors-20-02855-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/4e853e17a096/sensors-20-02855-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/1da36394648d/sensors-20-02855-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/521c2384e474/sensors-20-02855-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/121d2aa40dac/sensors-20-02855-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/8aa683551858/sensors-20-02855-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/c8c267f31fbc/sensors-20-02855-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/a4c5166e1713/sensors-20-02855-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/1efb1c95d128/sensors-20-02855-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/6e4c979cc552/sensors-20-02855-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/4e853e17a096/sensors-20-02855-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3170/7288025/1da36394648d/sensors-20-02855-g009.jpg

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本文引用的文献

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