Privacy protection and energy optimization for 5G-aided industrial internet of things

The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today's fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

An Approach to Embedding ITSs into Existing Systems

Intelligent Tutoring Systems (ITSs) have proven their effectiveness in many domains, but very few attempts have been made to embed them with existing systems. This area of research has a lot of potential in providing life-long learning and work place training. This PhD project makes several significant contributions. This is the first attempt to embed a Constraint-Based Tutor (CBT) with an existing system, in order to investigate the benefits of providing on-the-job training. We also propose a framework for embedded ITSs, and develop DM-Tutor (Decision-Making Tutor) embedded with the MIS for palm oil. DM-Tutor is the first ITS for the domain of oil palm plantation decision making, and was developed in the ASPIRE authoring system. Our hypothesis was that DM-Tutor embedded with the MIS for palm oil would provide effective instruction and training for oil palm plantation decision making. We also wanted to investigate the role of feedback messages in helping to provide effective training