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Article

Wild Animal Information Collection Based on Depthwise Separable Convolution in Software Defined IoT Networks

1
School of Information Engineering, Nanchang University, Nanchang 330031, China
2
State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
3
School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, China
4
College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Academic Editor: Martin Reisslein
Electronics 2021, 10(17), 2091; https://doi.org/10.3390/electronics10172091
Received: 13 July 2021 / Revised: 24 August 2021 / Accepted: 26 August 2021 / Published: 28 August 2021
The wild animal information collection based on the wireless sensor network (WSN) has an enormous number of applications, as demonstrated in the literature. Yet, it has many problems, such as low information density and high energy consumption ratio. The traditional Internet of Things (IoT) system has characteristics of limited resources and task specificity. Therefore, we introduce an improved deep neural network (DNN) structure to solve task specificity. In addition, we determine a programmability idea of software-defined network (SDN) to solve the problems of high energy consumption ratio and low information density brought about by low autonomy of equipment. By introducing some advanced network structures, such as attention mechanism, residuals, depthwise (DW) convolution, pointwise (PW) convolution, spatial pyramid pooling (SPP), and feature pyramid networks (FPN), a lightweight object detection network with a fast response is designed. Meanwhile, the concept of control plane and data plane in SDN is introduced, and nodes are divided into different types to facilitate intelligent wake-up, thereby realizing high-precision detection and high information density of the detection system. The results show that the proposed scheme can improve the detection response speed and reduce the model parameters while ensuring detection accuracy in the software-defined IoT networks. View Full-Text
Keywords: information collection; internet of things; deep neural network; SDN; object detection information collection; internet of things; deep neural network; SDN; object detection
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MDPI and ACS Style

Cao, Q.; Yu, L.; Wang, Z.; Zhan, S.; Quan, H.; Yu, Y.; Khan, Z.; Koubaa, A. Wild Animal Information Collection Based on Depthwise Separable Convolution in Software Defined IoT Networks. Electronics 2021, 10, 2091. https://doi.org/10.3390/electronics10172091

AMA Style

Cao Q, Yu L, Wang Z, Zhan S, Quan H, Yu Y, Khan Z, Koubaa A. Wild Animal Information Collection Based on Depthwise Separable Convolution in Software Defined IoT Networks. Electronics. 2021; 10(17):2091. https://doi.org/10.3390/electronics10172091

Chicago/Turabian Style

Cao, Qinghua, Lisu Yu, Zhen Wang, Shanjun Zhan, Hao Quan, Yan Yu, Zahid Khan, and Anis Koubaa. 2021. "Wild Animal Information Collection Based on Depthwise Separable Convolution in Software Defined IoT Networks" Electronics 10, no. 17: 2091. https://doi.org/10.3390/electronics10172091

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