Fog Big Data Analysis for IoT Sensor Application Using Fusion Deep Learning

Anand Singh Rajawat,Amer Aljaedi,S. B. Goyal,Sajjad Shaukat Jamal,Piyush Kumar Shukla,Adel R. Alharbi,Pradeep Bedi,Vijay Kumar

doi:10.1155/2021/6876688

Abstract

The IoT sensor applications have grown in extreme numbers, generating a large amount of data, and it requires very effective data analysis procedures. However, the different IoT infrastructures and IoT sensor device layers possess protocol limitations in transmitting and receiving messages which generate obstacles in developing the smart IoT sensor applications. This difficulty prohibited existing IoT sensor implementations from adapting to other IoT sensor applications. In this article, we study and analyze how IoT sensor produces data for big data analytics, and it also highlights the existing challenges of intelligent solutions. IoT sensor applications required big data classification and analysis in a Fog computing (FC) environment using computation intelligence (CI). Our proposed Fog big data analysis model (FBDAM) and BPNN analysis model for IoT sensor application using fusion deep learning (FDL) pose new obstacles for potential machine-to-machine communication practices. We have applied our proposed FBDAM on the most significant Fog applications developed on smart city datasets (parking, transportation, security, and sensor IoT dataset) and got improving results. We compared different deep and machine learning algorithms (SVM, SVMG-RBF, BPNN, S3VM, and proposed FDL) on different smart city dataset IoT application environments.

Highlights

IoT sensor systems are limited due to their processing capacity and network bandwidth
We proposed a new deep learning-based Fog big data analysis model (FBDAM) for classification of IoTapplication generated big dataset
We have examined several challenges at data, model, and device level (Fog computing environment (FCE)) [27] to explain the definition of large volume of big data

Summary

Introduction

IoT sensor systems are limited due to their processing capacity and network bandwidth. Smart apps need vast data and computational power for deep learning (DL) based research. Erefore, present smart applications respond to certain constraints by offering deep learning (DL) research at the gateway or cloud. Applications need input from the person or other smart machines involving duplex communication. Input is processed by computational intelligence (CI) algorithm that needs more computation time than restricted devices can accomplish. Erefore, IoT systems [1, 2] have a restricted capacity to understand, develop, and share information autonomously. Is enables intelligent application systems by allowing IoT solutions to find inference rules or information built into other machines or the cloud. Adopting one Fog computing to another sector has proven difficult, given the widespread use of Fog computing [3] to construct smart applications. When using deep learning from another domain, an IoT application’s ability to semi-identify the context of messages deteriorates

Objectives

Methods

Results

Conclusion