Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks
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Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China
2
Engineering Faculty, University of Sydney, Sydney, NSW 2006, Australia
3
School of Compute Science, University of Oklahoma at Norman, Norman, OK 73070, USA
4
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
5
JangHo School of Architecture, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2779; https://doi.org/10.3390/s20102779
Received: 10 April 2020
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Revised: 8 May 2020
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Accepted: 11 May 2020
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Published: 13 May 2020
(This article belongs to the Section Sensor Networks)
Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.
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Keywords:
event-driven deployment; collaborative neural network; maximum entropy function; multiple constraints; wireless sensor and robot networks
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MDPI and ACS Style
Zhuang, Y.; Wu, C.; Wu, H.; Zhang, Z.; Gao, Y.; Li, L. Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks. Sensors 2020, 20, 2779. https://doi.org/10.3390/s20102779
AMA Style
Zhuang Y, Wu C, Wu H, Zhang Z, Gao Y, Li L. Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks. Sensors. 2020; 20(10):2779. https://doi.org/10.3390/s20102779
Chicago/Turabian StyleZhuang, Yaoming, Chengdong Wu, Hao Wu, Zuyuan Zhang, Yuan Gao, and Li Li. 2020. "Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks" Sensors 20, no. 10: 2779. https://doi.org/10.3390/s20102779
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