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

Naveed Islam, Majid Altamimi, Khalid Haseeb and Mohammad Siraj
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Naveed Islam: Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan
Majid Altamimi: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Khalid Haseeb: Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan
Mohammad Siraj: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia

Sustainability, 2021, vol. 13, issue 23, 1-15

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.

Keywords: sustainable network; big data; edge nodes; machine learning; Internet of Things (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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