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BP neural network based continuous objects distribution detection in WSNs

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Abstract

WSNs (Wireless Sensor Networks) are widely applied in environment monitoring. Especially, in large scale environment monitoring, its flexibility in deployment and self-organization are strong points. However for distribution detection of continuous objects in large scale environment monitoring, there are two primary constraints: energy consumption and the accuracy of the detection which relies on the density of the WSNs. Currently, almost all of the continuous object monitoring are based on the boundary detection, and all the energy efficiency solutions only focus on the WSNs itself. Unfortunately, with the boundary detection method, the accuracy of the continuous objects detection highly relies on the density of the sensor nodes. What is worse, it is even impossible to make sure of the density of the sensor nodes in real situation. In order to deal with these issues, we proposed the Optimal Fusion Set based Clustering algorithm based on the continuous characteristics of the targets to enhance the energy efficiency and Global Distribution Status Monitoring (GDSM) algorithm to implement the monitoring with finite sensor nodes. Firstly, a dynamic diffusion model based on the Gaussian Puff model is proposed, and then the characteristics of continuous objects are analyzed. According to the theoretic analysis and simulation results, the GDSM algorithm can achieve stable accuracy with limited sensor nodes.

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Acknowledgments

This work is supported by the 2014 Pearl River S&T Nova Program of Guangzhou (No. 2014J2200023), 2014 Foshan Science and Technology Project (No. 2014HK100103), the Open Fund of Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis (No. GDUPTKLAB201304), the 2014 Shenzhen City “Knowledge Innovation Program” Project (No. JCYJ20140417113430604), the Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (\(No. CAAC-ITRB-201406\)), the Science and Technology Project of Guangzhou (No. 7415538980596), the Open Project of Shanghai City Information Security Comprehensive Management Technology Research Laboratory (No. AKG2009002), the 2013 Special Fund of Guangdong Higher School Talent Recruitment, the Educational Commission of Guangdong Province (No. 2013KJCX0131), the Special Funds of Guangdong High-tech Development Project (No. 2013B010401035), and the Guangdong Province University Student Innovation and Entrepreneurship Training Project 2014.

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Authors and Affiliations

  1. Guangdong University of Technology, Guangzhou, 510006, China

    Xiaoling Wu, Hainan Chen & Guangcong Liu

  2. Guangzhou Institute of Advanced Technology, CAS, Guangzhou, 511458, China

    Xiaoling Wu & Yanwen Wang

  3. Shenzhen Institutes of Advanced Technology, CAS, Shenzhen, 518055, China

    Xiaoling Wu

  4. Information Technology Research Base of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin, 300300, China

    Xiaoling Wu

  5. Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, 525000, China

    Lei Shu

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  1. Xiaoling Wu
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  2. Hainan Chen
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  3. Yanwen Wang
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  4. Lei Shu
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  5. Guangcong Liu
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Correspondence to Xiaoling Wu.

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Wu, X., Chen, H., Wang, Y. et al. BP neural network based continuous objects distribution detection in WSNs. Wireless Netw 22, 1917–1929 (2016). https://doi.org/10.1007/s11276-015-1074-1

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