An introduction to IoT Analytics 1st Edition by Harry Perros ISBN 0367686317 978-0367686314

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ISBN 10: 0367686317
ISBN 13:  978-0367686314
Author: Harry Perros

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.

An introduction to IoT Analytics 1st Table of contents:

Chapter 1: Introduction

1.1 The Internet of Things (IoT)

1.2 IoT Application Domains

1.3 IoT Reference Model

1.4 Performance Evaluation and Modeling of IoT Systems

1.5 Machine Learning and Statistical Techniques for IoT

1.6 Overview of the Book

Exercises

References

Chapter 2: Review of Probability Theory

2.1 Random Variables

2.2 Discrete Random Variables

2.2.1 The Binomial Random Variable

2.2.2 The Geometric Random Variable

2.2.3 The Poisson Random Variable

2.2.4 The Cumulative Distribution

2.3 Continuous Random Variables

2.3.1 The Uniform Random Variable

2.3.2 The Exponential Random Variable

2.3.3 Mixtures of Exponential Random Variables

2.3.4 The Normal Random Variable

2.4 The Joint Probability Distribution

2.4.1 The Marginal Probability Distribution

2.4.2 The Conditional Probability

2.5 Expectation and Variance

2.5.1 The Expectation and Variance of Some Random Variables

Exercises

References

Chapter 3: Simulation Techniques

3.1 Introduction

3.2 The Discrete-event Simulation Technique

3.2.1 Recertification of IoT Devices: A Simple Model

3.2.2 Recertification of IoT Devices: A More Complex Model

3.3 Generating Random Numbers

3.3.1 Generating Pseudo-Random Numbers

3.3.2 Generating Random Variates

3.4 Simulation Designs

3.4.1 The Event List

3.4.2 Selecting the Unit Time

3.5 Estimation Techniques

3.5.1 Collecting Endogenously Created Data

3.5.2 Transient-State versus Steady-State Simulation

3.5.3 Estimation of the Confidence Interval of the Mean

3.5.4 Estimation of the Confidence Interval of a Percentile

3.5.5 Estimation of the Confidence Interval of a Probability

3.5.6 Achieving a Required Accuracy

3.6 Validation of a Simulation Model

3.7 Simulation Languages

Exercises

Simulation Project

Task 1 (to be completed after you read Section 3.2)

Task 2 (to be completed after you read Section 3.3)

Task 3 (to be completed after you read Section 3.5)

References

Chapter 4: Hypothesis Testing

4.1 Statistical Hypothesis Testing for a Mean

4.1.1 The p-Value

4.1.2 Hypothesis Testing for the Difference between Two Population Means

4.1.3 Hypothesis Testing for a Proportion

4.1.4 Type I and Type II Errors

4.2 Analysis of Variance (ANOVA)

4.2.1 Degrees of Freedom

Exercises

References

Chapter 5: Multivariable Linear Regression

5.1 Simple Linear Regression

5.2 Multivariable Linear Regression

5.2.1 Significance of the Regression Coefficients

5.2.2 Residual Analysis

5.2.3 R-Squared

5.2.4 Multicollinearity

5.2.5 Data Transformations

5.3 An Example

5.4 Polynomial Regression

5.5 Confidence and Prediction Intervals

5.6 Ridge, Lasso, and Elastic Net Regression

5.6.1 Ridge Regression

5.6.2 Lasso Regression

5.6.3 Elastic Net Regression

Exercises

Regression Project

Data Set Generation

Task 1 Basic Statistics Analysis

Task 2 Simple Linear Regression

Task 3 Linear Multivariable Regression

Task 4 Lasso Regression

References

Chapter 6: Time Series Forecasting

6.1 A Stationary Time Series

6.1.1 How to Recognize Seasonality

6.1.2 Techniques for Removing Non-Stationary Features

6.2 Moving Average or Smoothing Models

6.2.1 The Simple Average Model

6.2.2 The Exponential Moving Average Model

6.2.3 The Average Age of a Model

6.2.4 Selecting the Best Value for k and a

6.3 The Moving Average MA(q) Model

6.3.1 Derivation of the Mean and Variance of Xt

6.3.2 Derivation of the Autocorrelation Function of the MA(1)

6.3.3 Invertibility of MA(q)

6.4 The Autoregressive Model

6.4.1 The AR(1) Model

6.4.2 Stationarity Condition of AR(p)

6.4.3 Derivation of the Coefficients ai, i = 1, 2, …, p

6.4.4 Determination of the Order of AR(p)

6.5 The Non-Seasonal ARIMA (p,d,q) Model

6.5.1 Determination of the ARIMA Parameters

6.6 Decomposition Models

6.6.1 Basic Steps for the Decomposition Model

6.7 Forecast Accuracy

6.8 Prediction Intervals

6.9 Vector Autoregression

6.9.1 Fitting a VAR(p)

Exercises

Forecasting Project

Data Set

Task 1 Check for Stationarity

Task 2 Fit a Simple Moving Average Model

Task 3 Fit an Exponential Smoothing Model

Task 4 Fit an Arma(p, q) Model

Task 5 Comparison of All the Models

References

Chapter 7: Dimensionality Reduction

7.1 A Review of Eigenvalues and Eigenvectors

7.2 Principal Component Analysis (PCA)

7.2.1 The PCA Algorithm

7.3 Linear and Multiple Discriminant Analysis

7.3.1 Linear Discriminant Analysis (LDA)

7.3.2 Multiple Discriminant Analysis (MDA)

Exercises

References

Chapter 8: Clustering Techniques

8.1 Distance Metrics

8.2 Hierarchical Clustering

8.2.1 The Hierarchical Clustering Algorithm

8.2.2 Linkage Criteria

8.3 The k-Means Algorithm

8.3.1 The Algorithm

8.3.2 Determining the Number k of Clusters

a. Silhouette Scores

b. Akaike’s Information Criterion (AIC)

8.4 The Fuzzy c-Means Algorithm

8.5 The Gaussian Mixture Decomposition

8.6 The DBSCAN Algorithm

8.6.1 Determining MinPts and ε

8.6.2 Advantages and Disadvantages of DBSCAN

Exercises

Clustering Project

Data Set Generation

Task 1 Hierarchical Clustering

Task 2 k-Means Algorithm

Task 3 DBSCAN Clustering

Task 4 Conclusions

References

Chapter 9: Classification Techniques

9.1 The k-Nearest Neighbor (k-NN) Method

9.1.1 Selection of k

9.1.2 Using Kernels with the k-NN Method

9.1.3 Curse of Dimensionality

9.1.4 Voronoi Diagrams

9.1.5 Advantages and Disadvantages of the k-NN Method

9.2 The Naive Bayes Classifier

9.2.1 The Simple Bayes Classifier

9.2.2 The Naive Bayes Classifier

9.2.3 The Gaussian Naive Bayes Classifier

9.2.4 Advantages and Disadvantages

9.2.5 The k-NN Method Using Bayes’ Theorem

9.3 Decision Trees

9.3.1 Regression Trees

9.3.2 Classification Trees

9.3.3 Pre-Pruning and Post-Pruning

9.3.4 Advantages and Disadvantages of Decision Trees

9.3.5 Decision Trees Ensemble Methods

9.4 Logistic Regression

9.4.1 The Binary Logistic Regression

9.4.2 Multinomial Logistics Regression

9.4.3 Ordinal Logistic Regression

Exercises

Classification Project

References

Chapter 10: Artificial Neural Networks

10.1 The Feedforward Artificial Neural Network

10.2 Other Artificial Neural Networks

10.3 Activation Functions

10.4 Calculation of the Output Value

10.5 Selecting the Number of Layers and Nodes

10.6 The Backpropagation Algorithm

10.6.1 The Gradient Descent Algorithm

10.6.2 Calculation of the Gradients

10.7 Stochastic, Batch, Mini-Batch Gradient Descent Methods

10.8 Feature Normalization

10.9 Overfitting

10.9.1 The Early Stopping Method

10.9.2 Regularization

10.9.3 The Dropout Method

10.10 Selecting the Hyper-Parameters

10.10.1 Selecting the Learning Rate γ

10.10.2 Selecting the Regularization Parameter λ

Exercises

Neural Network Project

Data Set Generation

Task 1 Train a Feedforward Neural Network

Task 2 Automatic Grid Search

Task 3 Compare the Best Trained Neural Network Model with Multivariable Regression

References

Chapter 11: Support Vector Machines

11.1 Some Basic Concepts

11.2 The SVM Algorithm: Linearly Separable Data

11.3 Soft-Margin SVM (C-SVM)

11.4 The SVM Algorithm: Non-Linearly Separable Data

11.5 Other SVM methods

11.6 Multiple Classes

11.7 Selecting the Best Values for C and γ

11.8 ε-Support Vector Regression (ε-SVR)

Exercises

SVM Project

Data Set Generation

Tasks

References

Chapter 12: Hidden Markov Models

12.1 Markov Chains

12.2 Hidden Markov Models – An Example

12.3 The Three Basic HMM Problems

12.3.1 Problem 1 – The Evaluation Problem

12.3.2 Problem 2 – The Decoding Problem

12.3.3 Problem 3 – The Learning Problem

12.4 Mathematical Notation

12.5 Solution to Problem 1

12.5.1 A Brute Force Solution

12.5.2 The Forward–Backward Algorithm

12.6 Solution to Problem 2

12.6.1 The Heuristic Solution

12.6.2 The Viterbi Algorithm

12.7 Solution to Problem 3

12.8 Selection of the Number of States N

12.9 Forecasting OT+t

12.10 Continuous Observation Probability Distributions

12.11 Autoregressive HMMs

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