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

Qinghua Cao,Hao Quan,Yan Yu,Lisu Yu,Anis Koubaa,Zhen Wang,Zahid Khan,Shanjun Zhan

doi:10.3390/electronics10172091

Abstract

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.

Highlights

Traditional wild animal information collection system is mainly deployed in the form of wireless sensor network (WSN), and the most widely used method is large-scale deployment of infrared cameras [1,2,3]
We use transfer learning for the part of the network structure that can use the pre-trained model [46,47], and load the pre-trained model of VOC2007 to speed up the convergence of the model
As for the reduction of energy consumption, we mainly introduced deep neural network (DNN) network to achieve the collection of specified animal information and reduce the energy consumption caused by false trigger

Summary

Introduction

Traditional wild animal information collection system is mainly deployed in the form of wireless sensor network (WSN), and the most widely used method is large-scale deployment of infrared cameras [1,2,3]. The method of infrared camera monitoring for information collection has the advantages of simple use and convenient operation [4], and brings about the problem of insufficient intelligence. We only need to collect the specified species information [5]. Due to the complexity of the wild environment and species diversity, false triggering for a variety of other reasons often occurs, causing additional energy consumption by cameras with limited resources, and adding additional workload for subsequent screening of collected information

Methods

Results

Conclusion