Open Access

ARTICLE

Smart Object Detection and Home Appliances Control System in Smart Cities

by Sulaiman Khan¹, Shah Nazir¹, Habib Ullah Khan^2,*

1 Department of Computer Science, University of Swabi, KP, Pakistan
2 Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar

* Corresponding Author: Habib Ullah Khan. Email:

(This article belongs to the Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)

Computers, Materials & Continua 2021, 67(1), 895-915. https://doi.org/10.32604/cmc.2021.013878

Received 25 August 2020; Accepted 04 November 2020; Issue published 12 January 2021

Abstract

During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities. This hybrid model consists of; convolution neural network and binary long short term model for the object detection to ensure safety of the homes while IoT based hardware components like; Raspberry Pi, Amazon Web services cloud, and GSM modems for remotely accessing and controlling of the home appliances. An android application is developed and deployed on Amazon Web Services (AWS) cloud for the remote monitoring of home appliances. A GSM device and Message queuing telemetry transport (MQTT) are integrated for communicating with the connected IoT devices to ensure the online and offline communication. For object detection purposes a camera is connected to Raspberry Pi using the proposed hybrid neural network model. The applicability of the proposed model is tested by calculating results for the object at varying distance from the camera and for different intensity levels of the light. Besides many applications the proposed model promises for providing optimum results for the small amount of data and results in high recognition rates of 95.34% compared to the conventional recognition model (k nearest neighbours) recognition rate of 76%.

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Citations