Harry G. Perros - An Introduction to IoT Analytics

Reflecting the interdisciplinary nature of the field, this book series brings together researchers, practitioners, and instructors from statistics, computer science, Machine Learning, and analytics. The series will publish cutting-edge research, industry applications, and textbooks in data science.

The inclusion of concrete examples, applications, and methods is highly encouraged. The scope of the series includes titles in the areas of Machine Learning, pattern recognition, predictive analytics, business analytics, Big Data, visualization, programming, software, learning analytics, data wrangling, interactive graphics, and reproducible research.

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First edition published 2021
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2021 Harilaos G Perros
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Library of Congress CataloginginPublication Data
Names: Perros, Harry G., author.
Title: An introduction to IoT analytics / Harry G. Perros.
Description: First edition. | Boca Raton : CRC Press, 2021. | Includes bibliographical references and index.
Identifiers: LCCN 2020038957 (print) | LCCN 2020038958 (ebook) | ISBN 9780367687823 (hardback) | ISBN 9780367686314 (paperback) | ISBN 9781003139041 (ebook)
Subjects: LCSH: Internet of things. | System analysis. | System analysis--Statistical methods. | Operations research.
Classification: LCC TK5105.8857 .P47 2021 (print) | LCC TK5105.8857 (ebook) | DDC 004.67/8--dc23
LC record available at https://lccn.loc.gov/2020038957
LC ebook record available at https://lccn.loc.gov/2020038958

ISBN: 978-0-367-68782-3 (hbk)
ISBN: 978-0-367-68631-4 (pbk)
ISBN: 978-1-003-13904-1 (ebk)

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To those who aspire to become better individuals

IoT is a closed loop system consisting of sensors, servers , a network connecting sensors to servers, and a data base that stores the information. Decision-making tools are used to make decisions based on the information received by the sensors which are then fed back into the system. There are numerous IoT applications in all aspects of our lives, such as, applications for smart cities, structural health, traffic congestion, smart environment, smart water, smart metering, assisted living, healthcare, security and emergency, smart retail, smart agriculture, and smart animal farming.

Analytics is a term that became popular with the advent of data mining, and it refers to the analysis of data in order to make decisions. The tools used in analytics come from the areas of Machine Learning, Statistics, and Operations Research. There is a plethora of tools from Machine Learning, including well-known tools such as, artificial neural networks, support vector machines, and hidden Markov models. Some of the commonly used tools from Statistics are multivariable linear regression, dimensionality reduction, and forecasting.

The contribution of Operation Research to analytics is less prominent because it has a different perspective. Operation Research techniques are used to study the performance of a system by developing mathematical and computer-based models of a system, which are then exercised in order to study the systems performance under different assumptions. Typically, it is applied to systems that have not been built yet, such as, new designs or new enhancements of an existing system. The applications of Operations Research to the study of the performance of computers, computer networks and IoT is known as Performance Evaluation and Modeling. Typical tools are simulation, queueing theory, and optimization methods.

In this book, I use the term analytics to include both data exploration techniques and techniques for evaluating the performance of a system.

There are a lot of applications of analytics to IoT-related problems, such as, security and breach detection, data assurance, smart metering, predictive maintenance, network capacity and planning, sensor management, decision-making using sensor data, fault management, and resource optimization.

This book arose out of teaching a one-semester graduate-level course on IoT Analytics in Computer Science Department, North Carolina State University. It covers the following set of analytic tools which can be seen as a minimum set of required knowledge.

• Simulation techniques

• Multivariable linear regression

• Time series forecasting techniques

• Dimensionality reduction

• Clustering

• Classification

• Artificial neural networks

• Support vector machines

• Hidden Markov models

In addition, there are two introductory chapters, one on probability theory and another on statistical hypothesis testing. These two chapters can be skipped by the knowledgeable reader. There are also two Appendices, one on some basic concepts of queueing theory and the other on the maximum likelihood estimation (MLE) technique, that supplement some of the material in this book.