Journal
SENSORS
Volume 20, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/s20102779
Keywords
event-driven deployment; collaborative neural network; maximum entropy function; multiple constraints; wireless sensor and robot networks
Funding
- Fundamental Research Funds for the Central Universities [N2026006, N2011001, N2026005, N181602014, N2026004, N2026001, N172604004]
- National Natural Science Foundation of China [61701101, 61973093, U1713216, 61901098, 61971118, 61973063]
- General Research Project of Zhejiang Provincial Department of Education [Y201839944]
- Public Welfare Technology Research Project of Zhejiang Province [LGG19F020007]
- PublicWelfare Technology Application Research Project of Shaoxing City [2018C10013]
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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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