Open Access

ARTICLE

Design of Intelligent Mosquito Nets Based on Deep Learning Algorithms

Yuzhen Liu^1,3, Xiaoliang Wang^1,*, Xinghui She¹, Ming Yi¹, Yuelong Li¹, Frank Jiang²

1 Hunan University of Science and Technology, Xiangtan, 411201, China
2 School of Info Technology, Deakin University, Geelong, 3215, Australia
3 Key Laboratory of Knowledge Processing and Networked Manufacturing, College of Hunan Province, 411201, China

* Corresponding Author: Xiaoliang Wang. Email:

Computers, Materials & Continua 2021, 69(2), 2261-2276. https://doi.org/10.32604/cmc.2021.015501

Received 24 November 2020; Accepted 28 April 2021; Issue published 21 July 2021

Abstract

An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes, and help people live well in mosquito-infested areas. In this study, we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things. In our method, decision-making is controlled by a deep learning model, and the proposed method uses infrared sensors and an array of pressure sensors to collect data. Moreover the ZigBee protocol is used to transmit the pressure map which is formed by pressure sensors with the deep learning perception model, determining automatically the intention of the user to open or close the mosquito net. We used optical flow to extract pressure map features, and they were fed to a 3-dimensional convolutional neural network (3D-CNN) classification model subsequently. We achieved the expected results using a nested cross-validation method to evaluate our model. Deep learning has better adaptability than the traditional methods and also has better anti-interference by the different bodies of users. This research has the potential to be used in intelligent medical protection and large-scale sensor array perception of the environment.

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Keywords

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