Secure and Sustainable Predictive Framework for IoT-Based Multimedia Services Using Machine Learning

Naveed Islam,Khalid Haseeb,Majid Altamimi,Mohammad Siraj

doi:10.3390/su132313128

Naveed Islam, Khalid Haseeb + Show 2 more

Open Access

https://doi.org/10.3390/su132313128

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Journal: Sustainability	Publication Date: Nov 26, 2021
Citations: 11	License type: CC BY 4.0

Affiliation: Islamia College University, King Saud University

Abstract

In modern years, the Internet of Things (IoT) has gained tremendous growth and development in various sectors because of its scalability, self-configuring, and heterogeneous factors. It performs a vital role in improving multimedia communication and reducing production costs. The multimedia data consist of various types and formats (text, audio, videos, etc.), which are forwarded in the form of blocks of bits in the network layer of TCP/IP. Due to limited resources available to IoT-built devices, most of the Multimedia Internet of Things (MIoT)-based applications are delay constraints, especially for big data content. Similarly, multimedia-based applications are more vulnerable to security burdens and lower the trust of data processing. In this paper, we present a secure and sustainable prediction framework for MIoT data transmission using machine learning, which aims to offer intelligent behavior of the system with information protection. Firstly, the network edges exploit a regression analysis for a real-time multimedia routing scheme and achieve precise delivery towards the media servers. Secondly, an efficient and low-processing asymmetric process is proposed to provide secure data transmission between the IoT devices, edges, and data servers. Extensive experiments are performed over the OMNET++ network simulator, and its significance is achieved by an average for energy consumption by 71%, throughput by 30.5%, latency by 22%, bandwidth by 34.5%, packets overheads by 38.5%, computation time by 12.5%, and packet drop ratio by 35% in the comparison of existing schemes.

Highlights

A multimedia network is defined as a distributed system, and application users can exchange traffic, such as audio, video, and images, to remote users by using communication tools
In the case of the wireless sensor network (WSN), the Internet of Things (IoT) devices can be deployed in dynamic environments and extracting local intended information
The intelligent and dynamic decisions of machine learning algorithms collaborate with IoT devices and generate train models that minimize the processing overheads on wireless systems [3,4,5]

Summary

Introduction

A multimedia network is defined as a distributed system, and application users can exchange traffic, such as audio, video, and images, to remote users by using communication tools. A large number of IoT devices and wireless nodes are exchanging real-time data that deplete high energy consumption and impose additional management costs. The intelligent and dynamic decisions of machine learning algorithms collaborate with IoT devices and generate train models that minimize the processing overheads on wireless systems [3,4,5]. With the beginning of IoT devices and multimedia services for real-time and robust applications, a lot of heterogeneous services are collaborating in a distributed manner. Such systems offer numerous functions for the sharing and exchanging of graphics data over the Internet.

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