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      Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks

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          Abstract

          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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          Most cited references45

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          Newton's Method for Multiobjective Optimization

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            Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks

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              Kalman Filtering Framework based Real Time Target Tracking in Wireless Sensor Networks using Generalized Regression Neural Networks

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                13 May 2020
                May 2020
                : 20
                : 10
                : 2779
                Affiliations
                [1 ]Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China; wuchengdong@ 123456ise.neu.edu.cn
                [2 ]Engineering Faculty, University of Sydney, Sydney, NSW 2006, Australia; hawu1598@ 123456uni.sydney.edu.au
                [3 ]School of Compute Science, University of Oklahoma at Norman, Norman, OK 73070, USA; Zuyuan.Zhang-1@ 123456ou.edu
                [4 ]College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; gaoyuan@ 123456stumail.neu.edu.cn
                [5 ]JangHo School of Architecture, Northeastern University, Shenyang 110819, China; lili1118@ 123456mail.neu.edu.cn
                Author notes
                [* ]Correspondence: zhuangyaoming@ 123456mail.neu.edu.cn ; Tel.: +86-159-4025-5805
                Author information
                https://orcid.org/0000-0001-8815-0801
                https://orcid.org/0000-0002-3376-8433
                Article
                sensors-20-02779
                10.3390/s20102779
                7385723
                32414214
                5d7240b0-0d52-4fc7-9abd-fdd6f16c8115
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 April 2020
                : 11 May 2020
                Categories
                Article

                Biomedical engineering
                event-driven deployment,collaborative neural network,maximum entropy function,multiple constraints,wireless sensor and robot networks

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