IoT-Enabled Big Data Analytics Architecture for Multimedia Data Communications

The present spreading out of the Internet of Things (IoT) originated the realization of millions of IoT devices connected to the Internet. With the increase of allied devices, the gigantic multimedia big data (MMBD) vision is also gaining eminence and has been broadly acknowledged. MMBD management offers computation, exploration, storage, and control to resolve the QoS issues for multimedia data communications. However, it becomes challenging for multimedia systems to tackle the diverse multimedia-enabled IoT settings including healthcare, traffic videos, automation, society parking images, and surveillance that produce a massive amount of big multimedia data to be processed and analyzed efficiently. There are several challenges in the existing structural design of the IoT-enabled data management systems to handle MMBD including high-volume storage and processing of data, data heterogeneity due to various multimedia sources, and intelligent decision-making. In this article, an architecture is proposed to process and store MMBD efficiently in an IoT-enabled environment. The proposed architecture is a layered architecture integrated with a parallel and distributed module to accomplish big data analytics for multimedia data. A preprocessing module is also integrated with the proposed architecture to prepare the MMBD and speed up the processing mechanism. The proposed system is realized and experimentally tested using real-time multimedia big data sets from athentic sources that discloses the effectiveness of the proposed architecture.

Symmetric Key and Polynomial Based Key Generation Mechanism for Secured Data Communications in 5G Networks

Fifth Generation (5G) networks provide data communications through various latest technologies including Software Defined Network (SDN), Artificial Intelligence, Machine Learning and Cloud Computing. In 5G, secure data communication is a challenging issue due to the presence of enormous volume of users including malicious users communicating with latest technologies and also based their own requirements. In such a scenario, fuzzy rules and cryptographic techniques can play a major role in providing security to the data which are either communicated through the network or stored in network based databases including distributed databases and cloud databases with cloud networks. Therefore, new and efficient mechanisms for generation and exchange of keys are necessary since they are the most important component of cryptographic methods. Since most of the existing key generation techniques are focusing on 3G and 4G networks, new key generation methods that can be generalized to n-th order polynomials are necessary to suit the security requirements of 5G networks which is smart by using rules from Artificial Intelligence. This paper proposes a new key generation and encryption/decryption mechanism which is based on both symmetric key cryptography and polynomial operations for providing effective security on data communication in 5G networks. In this work, we introduce the usage of fuzzy rules and Binomial Theorem (Pascal triangle) technique for performing the data encryption process more efficiently since it is not used in any of the existing cryptographic algorithms. Moreover, two different polynomial equations, one of degree three and another of degree two are used in the proposed work for effective key generation. Here, we have applied differential calculus for finding the second-degree polynomial. In the decryption part of the proposed mechanism, nth root operation is applied which is able to reduce the number of steps used in a single mode operation. The experimental results of the proposed work proved that the proposed security model with fuzzy rule-based approach is better than other related systems that are available in the literature in terms of reduction in computational complexity and increase in security.

Power Consumption prediction of IoT application Protocols Based on Linear Regression

The advent of the Internet of Things (IoT) augurs new cutting-edge applications in modern life such as smart cities and smart grids. These applications require protocols more efficient for ensuring the reliability of data communications in the IoT networks. Many works state that IoT cannot meet their demands without application protocols improvement with Artificial Intelligence (AI) as IoT are expected to generate unprecedented traffic giving IoT researchers access to data that can help in studying and analyzing the demands and develop application protocols conceptions to meet the requirement of IoT applications. In literature, several works introduced AI in some layers of the TCP/IP model including wireless communication and routing. In this article, an evaluation of application protocols HTTP, MQTT, DDS, XMPP, AMQP, and CoAP has been presented; and subsequently, the power consumption prediction of MQTT and COAP based on the linear regression model is analyzed, in order to enhance data communications in IoT applications.

On assessment and evaluation of teaching computer networks to electrical engineering students by the aid of a lab course

In the era of Internet-of-everything, learning the principles of data communications and networking is inevitable for many electrical engineering disciplines. The paper addresses the effectiveness of teaching the fundamentals of data communications and networking using a dedicated lab course as a co-requisite to a classic lecture-based course. In the introduced lab course, the students are asked to do a variety of tasks using real hardware and a network simulator. The paper introduces quantitative measures of an outcome-based learning approach applied to both courses. Based on students’ achievements, the role of the lab course in the attainment of both the course learning outcomes and the electrical engineering program learning outcomes is measured in comparison with the case where the lab course is not taken. Our findings reveal a general enhancement trend in the attainment of the course and program learning outcomes with a significant increase in the program outcome related to solving engineering problems. Also, a slight increase is noticed in meeting the lab course outcomes for the students who attended the lab with the course in the same semester, which indicates an improvement in gaining practical knowledge.