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Home > IoT & Hardware > Single Board Computers > Smart Internet of Things Projects
Smart Internet of Things Projects
Smart Internet of Things Projects

Smart Internet of Things Projects: Discover how to build your own smart Internet of Things projects and bring a new degree of interconnectivity to your world

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Smart Internet of Things Projects

Chapter 2. Decision System for IoT Projects

If we feel a cold, then we use a jacket. When we are hungry, we decide to eat. These decisions can be made by us, but how does a machine make a decision? In this chapter, we learn how to build a decision system which can be implemented on IoT devices.

We explore the following topics:

  • Introduction to decision system and machine learning
  • Exploring Python libraries to build a decision system
  • Building a simple decision system-based Bayesian theory
  • Integrating a decision system and IoT project
  • Building your own decision system-based IoT

Introduction to decision system and machine learning

A decision system is a system that makes a decision based on several input parameters. A decision system is built on decision theories. Being human involves making decisions for almost life cases.

The following are examples of decisions that humans take:

  • Shall I buy the car today? The decision depends on my preferences. This car looks fine, but it is too expensive for me.
  • Shall I bring an umbrella today? This decision depends on the current condition in the area where we are staying. If it is cloudy, it's better to bring an umbrella even though it may not rain.

Generally speaking, we teach a machine such as a computer in order to understand and achieve a specific goal. This case is called machine learning. Varieties of programs are implemented in machines so they can make decisions.

Machine learning consists of various algorithms to build a decision system. In this book, I use fuzzy logic and Bayesian algorithms to make a decision system...

Decision system-based Bayesian

Bayesian uses the manipulation of conditional probabilities approach to interpret data. In this section, we build a decision system using the Bayesian method.

Consider D, called the decision space, which denotes the space of all possible decisions d that could be chosen by the decision maker (DM). Θ is the space of all possible outcomes or state of nature ω, ω∈Θ.

Decision system-based Bayesian is built by Bayesian theory. For illustration, I show a simple spam filter using Bayesian. Imagine the sample space X is the set of all possible datasets of words, from which a single dataset word x will result. For each ω∈Θ and x∈X, the sampling model P(ω) describes a belief that x would be the outcome of spam probability. P(x|ω), prior distribution, is the true population characteristics and supposes a spam probability for x.P(ω|x)., posterior distribution, describes a belief that ω is...

Decision system-based fuzzy logic

Consider you want to make a decision based on the current temperature, for instance, if the room's temperature is 30°C, then you turn on a cooler machine. Otherwise, if the room's temperature is 18°C, you turn on a heater machine.

This decision happens because we already defined exact values for turning on the machines. What's happening is that we say that we want to turn on the cooler machine if the room's temperature is hot. Furthermore, we also want to turn on the heater machine if the room's temperature is cold.

Cold and hot are two terms related to human linguistics. We should determine how cold and hot criteria are. A human differentiates the criteria for cold and hot, but how can a computer and machine know?

This problem can be solved using fuzzy logic. The idea of fuzzy logic was first introduced by Dr. Lotfi Zadeh from the University of California at Berkeley in the 1960s. The theory of fuzzy logic is developed with...

Python libraries for building a decision system

In this section, we explore some Python libraries to build our decision system. I focus on Bayesian and fuzzy logic models for implementing the decision system.

Bayesian

We can implement Bayesian probability using Python. For our demo, we generate output values from two independent variables, x1 and x2. The output model is defined as follows:

Bayesian

c is a random value. We define α, β1, β2, and σ as 0.5, 1, 2.5, and 0.5.

These independent variables are generated using a random object from the NumPy library. After that, we compute the model with these variables.

We can implement this case with the following scripts:

import matplotlib
matplotlib.use('Agg')

import numpy as np
import matplotlib.pyplot as plt

# initialization
np.random.seed(100)
alpha, sigma = 0.5, 0.5
beta = [1, 2.5]
size = 100

# Predictor variable
X1 = np.random.randn(size)
X2 = np.random.randn(size) * 0.37

# Simulate outcome variable
Y = alpha + beta...

Introduction to decision system and machine learning

A decision system is a system that makes a decision based on several input parameters. A decision system is built on decision theories. Being human involves making decisions for almost life cases.

The following are examples of decisions that humans take:

  • Shall I buy the car today? The decision depends on my preferences. This car looks fine, but it is too expensive for me.

  • Shall I bring an umbrella today? This decision depends on the current condition in the area where we are staying. If it is cloudy, it's better to bring an umbrella even though it may not rain.

Generally speaking, we teach a machine such as a computer in order to understand and achieve a specific goal. This case is called machine learning. Varieties of programs are implemented in machines so they can make decisions.

Machine learning consists of various algorithms to build a decision system. In this book, I use fuzzy logic and Bayesian algorithms to make a decision system. I...

Decision system-based Bayesian

Bayesian uses the manipulation of conditional probabilities approach to interpret data. In this section, we build a decision system using the Bayesian method.

Consider D, called the decision space, which denotes the space of all possible decisions d that could be chosen by the decision maker (DM). Θ is the space of all possible outcomes or state of nature ω, ω∈Θ.

Decision system-based Bayesian is built by Bayesian theory. For illustration, I show a simple spam filter using Bayesian. Imagine the sample space X is the set of all possible datasets of words, from which a single dataset word x will result. For each ω∈Θ and x∈X, the sampling model P(ω) describes a belief that x would be the outcome of spam probability. P(x|ω), prior distribution, is the true population characteristics and supposes a spam probability for x.P(ω|x)., posterior distribution, describes a belief that ω is the true value of spam, having observed dataset x.

The posterior distribution is obtained...

Decision system-based fuzzy logic

Consider you want to make a decision based on the current temperature, for instance, if the room's temperature is 30°C, then you turn on a cooler machine. Otherwise, if the room's temperature is 18°C, you turn on a heater machine.

This decision happens because we already defined exact values for turning on the machines. What's happening is that we say that we want to turn on the cooler machine if the room's temperature is hot. Furthermore, we also want to turn on the heater machine if the room's temperature is cold.

Cold and hot are two terms related to human linguistics. We should determine how cold and hot criteria are. A human differentiates the criteria for cold and hot, but how can a computer and machine know?

This problem can be solved using fuzzy logic. The idea of fuzzy logic was first introduced by Dr. Lotfi Zadeh from the University of California at Berkeley in the 1960s. The theory of fuzzy logic is developed with fuzzy sets and memberships.

In general...

Python libraries for building a decision system

In this section, we explore some Python libraries to build our decision system. I focus on Bayesian and fuzzy logic models for implementing the decision system.

Bayesian

We can implement Bayesian probability using Python. For our demo, we generate output values from two independent variables, x1 and x2. The output model is defined as follows:

c is a random value. We define α, β1, β2, and σ as 0.5, 1, 2.5, and 0.5.

These independent variables are generated using a random object from the NumPy library. After that, we compute the model with these variables.

We can implement this case with the following scripts:

import matplotlib
matplotlib.use('Agg')

import numpy as np
import matplotlib.pyplot as plt

# initialization
np.random.seed(100)
alpha, sigma = 0.5, 0.5
beta = [1, 2.5]
size = 100

# Predictor variable
X1 = np.random.randn(size)
X2 = np.random.randn(size) * 0.37

# Simulate outcome variable
Y = alpha + beta[0]*X1 + beta[1]*X2 + np.random.randn...

Building a simple decision system-based Bayesian theory

In this section, we build a simple decision system using Bayesian theory. A smart water system is a smart system that controls water. In general, you can see the system architecture in the following figure:

After using a sensing process on water to obtain the water quality, you can make a decision. If the water quality is good, we can transfer the water to customers. Otherwise, we purify the water.

To implement a decision system-based Bayesian theory, firstly we define the state of nature. In this case, we define two states of nature:

  • ω1: water is ready for drinking

  • ω2: water should be cleaned (kotor)

For inputs, we can declare x1 and x1 as negative and positive as the observation results.

We define prior values and class conditional probabilities as follows:

To build a decision, we should make a loss function The following is a loss function for our program:

Now you can write the complete scripts for the program.

# decision action
# d1 = distribute...
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Key benefits

  • Learn how to extract and analyse data from physical devices and build smart IoT projects
  • Master the skills of building enticing projects such as a neural network autonomous car, computer vision through a camera, and cloud-based IoT applications
  • This project-based guide leverages revolutionary computing chips such as Raspberry Pi, Arduino, and so on

Description

Internet of Things (IoT) is a groundbreaking technology that involves connecting numerous physical devices to the Internet and controlling them. Creating basic IoT projects is common, but imagine building smart IoT projects that can extract data from physical devices, thereby making decisions by themselves. Our book overcomes the challenge of analyzing data from physical devices and accomplishes all that your imagination can dream up by teaching you how to build smart IoT projects. Basic statistics and various applied algorithms in data science and machine learning are introduced to accelerate your knowledge of how to integrate a decision system into a physical device. This book contains IoT projects such as building a smart temperature controller, creating your own vision machine project, building an autonomous mobile robot car, controlling IoT projects through voice commands, building IoT applications utilizing cloud technology and data science, and many more. We will also leverage a small yet powerful IoT chip, Raspberry Pi with Arduino, in order to integrate a smart decision-making system in the IoT projects.

Who is this book for?

If you are hobbyist who is keen on making smart IoT projects, then this book is for you. You should have a basic knowledge of Python.

What you will learn

  • Implement data science in your IoT projects and build a smart temperature controller
  • Create a simple machine learning application and implement decision system concepts
  • Develop a vision machine using OpenCV
  • Build a robot car with manual and automatic control
  • Implement speech modules with your own voice commands for IoT projects
  • Connect IoT to a cloud-based server

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 30, 2016
Length: 258 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466518
Category :
IoT & Hardware
Languages :
Python
Concepts :
Single Board Computers
Tools :
Raspberry Pi

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Product Details

Publication date : Sep 30, 2016
Length: 258 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466518
Category :
IoT & Hardware
Languages :
Python
Concepts :
Single Board Computers
Tools :
Raspberry Pi

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Table of Contents

7 Chapters
1. Making Your IoT Project Smart Chevron down icon Chevron up icon
1. Making Your IoT Project Smart
Introducing basic statistics and data science
Python for computational statistics and data science
Building a simple program for statistics
IoT devices and platforms
Sensing and actuating on IoT devices
Building a smart temperature controller for your room
Summary
References
2. Decision System for IoT Projects Chevron down icon Chevron up icon
2. Decision System for IoT Projects
Introduction to decision system and machine learning
Decision system-based Bayesian
Decision system-based fuzzy logic
Python libraries for building a decision system
Building a simple decision system-based Bayesian theory
Integrating a decision system and IoT project
Building your own decision system-based IoT
Summary
References
3. Building Your Own Machine Vision Chevron down icon Chevron up icon
3. Building Your Own Machine Vision
Introducing machine vision
Introducing the OpenCV library
Deploying OpenCV on Raspberry Pi
Building a simple program with OpenCV
Working with camera modules
Accessing camera modules from the OpenCV library
Introducing pattern recognition for machine vision
Building a tracking vision system for moving objects
Building your own IoT machine vision
Summary
References
4. Making Your Own Autonomous Car Robot Chevron down icon Chevron up icon
4. Making Your Own Autonomous Car Robot
Introducing autonomous systems
Introducing mobile robots
Building your own car robot
Working with the Pololu Zumo robot for Arduino
Controlling a car robot from a computer
Working with a GPS module for navigation
Introducing map engine platforms
Building a car-based GPS
Making your own autonomous car
Summary
References
5. Building Voice Technology on IoT Projects Chevron down icon Chevron up icon
5. Building Voice Technology on IoT Projects
Introduce a speech technology
Introduce sound sensors and actuators
Introduce pattern recognition for speech technology
Reviewing speech and sound modules for IoT devices
Build your own voice commands for IoT projects
Make your IoT board speak
Make Raspberry Pi speak
Summary
References
6. Building Data Science-based Cloud for IoT Projects Chevron down icon Chevron up icon
6. Building Data Science-based Cloud for IoT Projects
Introduce cloud technology
Introducing cloud-based data science
Connecting IoT boards to cloud-based server
Working with Microsoft Azure IoT Hub
Building data science-based cloud
Make IoT application with data science-based cloud
Summary
Index Chevron down icon Chevron up icon
Index

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Anthony Oct 31, 2019
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Mostly, a cookbook - rather than really teaching concepts and explaining options, the focus here is on directions to wire hardware together, install and run software - fine as that, but as it becomes dated online sources are a better bet.
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Buy this book only if you are comfortable with technical terms related to IoT boards. No indepth explanation of concepts, lots of links provided for additional information, why would I buy your book when I can access the links? Poorly edited, one of the low quality books from packt.
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About the author

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Kurniawan
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Agus Kurniawan is an independent technology consultant, author, and lecturer. He has over 18 years' experience working on various software development projects, including delivering training courses and workshops, and delivering technical writing. He has done a few research activities related to wireless networking, software, and security in multiple universities. Currently, he is pursuing a Ph.D. program in Computer Science in Germany. He has previously written five books for Packt.
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