All ETDs from UAB

Advisory Committee Chair

Karthikeyan Lingasubramanian

Advisory Committee Members

Murat M Tanik

Sazia Eliza

Mohammad Haider

Earl Wells

Document Type

Dissertation

Date of Award

2018

Degree Name by School

Doctor of Philosophy (PhD) School of Engineering

Abstract

The Internet of Things (IoT) is a ubiquitous technology that can mark the evolution of modern world. Since the internet has become a common household resource like the land line was in the past, it is time to take technology to the next level. With the manipulation of signals, we now have the use of wireless devices that are connected to various devices (both wireless and wired). The idea is to not only engineer devices that connect to the web, but also make them smart using processors embedded in devices coupled with internet to provide an intelligent system. Our research group, headed by Dr. Tanik, over the years developed a communication based modeling approach to address engineering problems. In his “third original idea” presentation he compared this model with the two widely used engineering models – particle-based models such as Newton's and wave based models such as Maxwell’s. Based on this approach, we propose that IoT systems can be modeled by Shannon’s communication channel represented as Multiple Input and Multiple Output (MIMO) channel. The comprehensiveness of our model can enable optimization of various parameters in IoT systems. To accomplish IoT model through Shannon’s channel, we use the independent set (stability set) from graph theory as a guiding principle to model for least action. It will be shown that the maximum independency number will correspond to the least action and related events. The least action principle can be translated into optimization schemes for various IoT parameters. The foundation of this research was formed under the guidance of Dr. Tanik and his research team; his research team was comprised of PhD students and a visiting scientist. Finally, we demonstrate the use of our model and its application in the modeling and analysis of a connected car IoT system. We successfully validated our model through accurate measurements of line of sight and proximity sensing efficiency. Based on our important fundamental contribution towards IoT modeling, we envision to explore re-source optimization in edge computers. Since edges in IoT systems need to have real time processing capabilities that can handle complex analysis and prediction algorithms, it is important to provide significant importance to edge computers while modeling IoT applications. The future work of this dissertation will focus on optimization of real time processing in edge computers of IoT systems.

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