This book covers techniques that can be used to analyze data from IoT sensors and addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so one can learn how to apply these tools in practice with a good understanding of their inner workings. This is an introductory book for readers who have no familiarity with these techniques.

The techniques presented in An Introduction to IoT Analytics come from the areas of machine learning, statistics, and operations research. Machine learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data and dimensionality reduction of data sets. Operations research is concerned with the performance of an IoT system by constructing a model of the system under study and then carrying out a what-if analysis. The book also describes simulation techniques.

Key Features

  • IoT analytics is not just machine learning but also involves other tools, such as forecasting and simulation techniques.
  • Many diagrams and examples are given throughout the book to fully explain the material presented.
  • Each chapter concludes with a project designed to help readers better understand the techniques described.
  • The material in this book has been class tested over several semesters.
  • Practice exercises are included with solutions provided online at www.routledge.com/9780367686314

Harry G. Perros is a Professor of Computer Science at North Carolina State University, an Alumni Distinguished Graduate Professor, and an IEEE Fellow. He has published extensively in the area of performance modeling of computer and communication systems.

chapter Chapter 1|13 pages


chapter Chapter 2|23 pages

Review of Probability Theory

chapter Chapter 3|36 pages

Simulation Techniques

chapter Chapter 4|13 pages

Hypothesis Testing

chapter Chapter 5|27 pages

Multivariable Linear Regression

chapter Chapter 6|43 pages

Time Series Forecasting

chapter Chapter 7|18 pages

Dimensionality Reduction

chapter Chapter 8|23 pages

Clustering Techniques

chapter Chapter 9|40 pages

Classification Techniques

chapter Chapter 10|35 pages

Artificial Neural Networks

chapter Chapter 11|24 pages

Support Vector Machines

chapter Chapter 12|33 pages

Hidden Markov Models