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无线传感器网络中的动态障碍覆盖用于智能电网。

Dynamic Barrier Coverage in a Wireless Sensor Network for Smart Grids.

机构信息

School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.

School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan 430073, China.

出版信息

Sensors (Basel). 2018 Dec 22;19(1):41. doi: 10.3390/s19010041.

DOI:10.3390/s19010041
PMID:30583532
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6338988/
Abstract

The development of engineering technology such as inspection robots (IR) for transmission lines and wireless sensor networks (WSN) are widely used in the field of smart grid monitoring. However, how to integrate inspection robots into wireless sensor networks is still a great challenge to form an efficient dynamic monitoring network for transmission lines. To address this problem, a dynamic barrier coverage (DBC) method combining inspection robot and wireless sensor network (WSN) is proposed to realize a low-cost, energy-saving and dynamic smart grid-oriented sensing system based on mobile wireless sensor network. To establish an effective smart grid monitoring system, this research focuses on the design of an effective and safe dynamic network coverage and network nodes deployment method. Multiple simulation scenarios are implemented to explore the variation of network performance with different parameters. In addition, the dynamic barrier coverage method for the actual scene of smart grid monitoring considers the balance between network performance and financial costs.

摘要

检测机器人（IR）和无线传感器网络（WSN）等工程技术的发展在智能电网监测领域得到了广泛应用。然而，如何将检测机器人集成到无线传感器网络中，对于形成高效的输电线路动态监测网络仍然是一个巨大的挑战。为了解决这个问题，提出了一种将检测机器人和无线传感器网络（WSN）相结合的动态障碍覆盖（DBC）方法，以实现基于移动无线传感器网络的低成本、节能、面向智能电网的动态传感系统。为了建立有效的智能电网监测系统，本研究侧重于设计一种有效和安全的动态网络覆盖和网络节点部署方法。通过多个仿真场景来探索不同参数对网络性能的变化。此外，针对智能电网监测的实际场景，动态障碍覆盖方法考虑了网络性能和财务成本之间的平衡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/afbfa6ef8daf/sensors-19-00041-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/96ea5083fc44/sensors-19-00041-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/49375a501b74/sensors-19-00041-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/f103cf5045cb/sensors-19-00041-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/d31955aa8eb6/sensors-19-00041-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/2dc9c726683f/sensors-19-00041-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/2112b487cd23/sensors-19-00041-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/a98b75bcb838/sensors-19-00041-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/b2bae8914c52/sensors-19-00041-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/5bc286b0971e/sensors-19-00041-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/6d06acb71e68/sensors-19-00041-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/9fd868616422/sensors-19-00041-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/3bc21e016cc9/sensors-19-00041-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/708286ace6a5/sensors-19-00041-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/b09c258062b0/sensors-19-00041-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/afbfa6ef8daf/sensors-19-00041-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/96ea5083fc44/sensors-19-00041-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/49375a501b74/sensors-19-00041-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/f103cf5045cb/sensors-19-00041-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/d31955aa8eb6/sensors-19-00041-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/2dc9c726683f/sensors-19-00041-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/2112b487cd23/sensors-19-00041-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/a98b75bcb838/sensors-19-00041-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/b2bae8914c52/sensors-19-00041-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/5bc286b0971e/sensors-19-00041-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/6d06acb71e68/sensors-19-00041-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/9fd868616422/sensors-19-00041-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/3bc21e016cc9/sensors-19-00041-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/708286ace6a5/sensors-19-00041-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/b09c258062b0/sensors-19-00041-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c38/6338988/afbfa6ef8daf/sensors-19-00041-g015.jpg

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本文引用的文献

[1]
Multi-Robot Cyber Physical System for Sensing Environmental Variables of Transmission Line.

Sensors (Basel). 2018-9-18

[2]
Achieving Crossed Strong Barrier Coverage in Wireless Sensor Network.

Sensors (Basel). 2018-2-10

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