Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks Article Swipe
Yaoming Zhuang
,
Chengdong Wu
,
Hao Wu
,
Zuyuan Zhang
,
Yuan Gao
,
Li Li
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20102779
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20102779
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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s20102779
- https://www.mdpi.com/1424-8220/20/10/2779/pdf?version=1589622966
- OA Status
- gold
- Cited By
- 5
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3025561182
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- OpenAlex ID
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https://openalex.org/W3025561182Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s20102779Digital Object Identifier
- Title
-
Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-13Full publication date if available
- Authors
-
Yaoming Zhuang, Chengdong Wu, Hao Wu, Zuyuan Zhang, Yuan Gao, Li LiList of authors in order
- Landing page
-
https://doi.org/10.3390/s20102779Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/20/10/2779/pdf?version=1589622966Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/20/10/2779/pdf?version=1589622966Direct OA link when available
- Concepts
-
Software deployment, Wireless sensor network, Computer science, Event (particle physics), Artificial neural network, Real-time computing, Distributed computing, Algorithm, Artificial intelligence, Computer network, Operating system, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 2, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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51Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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