Edge Computing with Python: End-to-end Edge Applications, Python Tools and Techniques, Edge Architectures, and AI Benefits

Cover Page
Title Page
Copyright Page
Dedication Page
Declaration by Author
About the Author
Acknowledgement
Preface
Errata
Table of Contents
1. Understanding Edge Computing
    Introduction
    Structure
    Objectives
    Know the Edge Computing
        Evolution of Edge Computing
            The 60s to 80s: decentralized
            The 80s to late 90s: decentralized
            The late 90s to mid-2010s: centralized
            The mid-2010s to 2020s: decentralized
        Use cases of Edge Computing
            Driver’s drowsiness detection
            Patient’s health monitoring
            Autonomous or driver-less vehicles
            Content Delivery Network (CDN)
        Advantages of Edge Computing
        Disadvantages of Edge Computing
    Introduction to Cloud Computing
        Advantages of Cloud Computing
        Disadvantages of Cloud Computing
    Introduction to the Internet of Things
        Applications of IoT
        Industrial IoT or Industry 4.0
        IIoT application example
    Classification of Edge devices
        Small Edge devices or IoT devices
        Medium Edge devices or Edge devices
        Large Edge devices or Edge cluster/server
        Gateway devices
        Fog computing
    Conclusion
    Points to remember
2. Up and Running with Edge Architecture
    Introduction
    Structure
    Objectives
    Requirements for Edge Architecture
        Functional requirements
            Customer requirements
            Developer requirements
        Non-functional requirements
            Process management
            Device and environment monitoring
            AI and real-time data analytics
    Views for Edge Architecture
        Physical view
        Solution view
        Functional view
    Reference Edge Architecture
        The architecture of Edge Device
            Edge-Core layer
            Common services layer
        Deployment Architecture models
            Centralized control plane
            Distributed control plane
    Critical elements for Edge Architecture
        Runtime
        Connectivity
        Data handling
        Management and monitoring
    Conclusion
    Points to remember
3. Challenges for Developers
    Introduction
    Structure
    Objectives
    Challenges for Edge application development
        Data availability
    Data protection
        Diversified solutions
        Portability and reusability
        Application lifecycle management
        Similar tooling
        Other miscellaneous challenges
    Conclusion
    Points to remember
4. Setting Up Edge Computing Environment
    Introduction
    Structure
    Objectives
    Development tools
        Microsoft Visual Studio Code
            Installation
            Python setup
            Working with Python in VS Code
            Python virtual environment
            Program to control keyboard and mouse
        Sensors and actuators
            Program a Little Piano
            Program to plot mouse movement
            Python for Edge development
        Virtual COM port ‘com0com’
        PySerial library
            Program to print the list of COM ports.
            Program for data exchange
    Introduction to Arduino
        Extended Arduino
        Writing Arduino program
            Arduino Program structure
        Arduino and Python
            Python program to interact with Arduino
    Introduction to MicroPython
        PyBoard
        PyBoard simulation
        Example program in MicroPython
    Conclusion
    Points to remember
5. Data Acquisition and Processing
    Introduction
    Structure
    Objectives
    Data handling
        Data acquisition
        Data processing
        Output handling
    Python data handling
        Health monitoring with patient’s ECG
            Part 1 – ECG data generator
            Part 2 – ECG data processor
        Video streaming and image processing
            Part 1 – Camera publisher
            Part 2 – Camera processor
            Convert to grayscale
            Edge detection
            Capturing and saving images from video
        Weather monitoring using external API
            Enhancing weather monitoring with HTML UI
    MicroPython examples
        Acquire data from temperature sensor
        Acquire data from HTTP API
    Best practices
    Conclusion
    Points to remember
6. Data Storage and Cloud Connectivity
    Introduction
    Structure
    Objectives
    Local data storage
        Basics of file storage handling
            Open file
            Reading the content of the file
            Writing to the file
            Deleting, renaming, and other operations on the file
        Using file storage in the program
            Advantage of the file
            JSON files
        Database (DB) storage
        SQLite DB in Python
            Program to plot mouse movement with SQLite
    Remote data storage
        REST API
            REST and HTTP
        Python for REST API
            Build REST API for SQLite
            Verifying our SQLite REST APIs
            HTTP Client for SQLite REST API
        MongoDB
        Time-series database
            Insert via ILP
            Executing SQL query
            Using REST APIs
    Sending data to the Cloud
        Azure Cloud account
    Install Azure IoT Explorer
        Program to send data to Cloud
    Conclusion
    Points to remember
7. Executing AI/ML Models
    Introduction
    Structure
    Objectives
    Artificial intelligence and machine learning
        Artificial intelligence
        Machine learning
            Difference between AI and ML
            Common terminology
    AI/ML with Python
        Steps of developing AI/ML program
        First AI/ML program – traffic sign identification
        Further improvements of this program
        Know about CNN
    Time-series Analysis
        Time-series prediction with LSTM model
        Time-series prediction with LGBM model
        Edge specific considerations
            TensorFlow Lite
            Microsoft EdgeML
            TensorFlow for MicroPython
    Image processing
        Image to text conversion
    Conclusion
    Points to remember
8. Security and Protection
    Introduction
    Structure
    Objectives
    Common Security Risks
        Physical level security
        Network level security
        Platform level security
        Application-level security
        Data level security
    Securing Python programs
        Self-signed certificate
        Using certificate with Python client
        Using NGINX reverse proxy
        SQL and script injection
        Encryption and decryption
    Conclusion
    Points to remember
9. Applying Advanced Tools and Techniques
    Introduction
    Structure
    Objectives
    Docker and container
        Container vs VM
        Container orchestration
        Docker terminology
    Hands-on with Docker
        Docker installation
        Running a container
        Build and run container
        Using Docker compose
    Introduction to MQTT
        MQTT topic
        MQTT with Python
    Introduction to FIWARE
        The Orion Context Broker (OCB)
            Running OCB
            OCB operations
            OCB subscriptions
            Using OCB with Python
    Basics of Kubernetes
        Kubernetes architecture
        Kubernetes at Edge or KubeEdge
        KubeEdge architecture
    Conclusion
    Points to remember
10. Developing End-to-End Edge Applications
    Introduction
    Structure
    Objectives
    Drowsiness detection application
        Method to detect drowsiness
        High-level architecture
        Scope of our development
        The Edge app
        Alert simulation
        Sensor simulation
        The final execution
    Predictive maintenance application
        Scope of our development
        The prediction program
        Sensors test data
        The final execution
    Conclusion
    Points to remember
11. Edge Platforms at a Glance
    Introduction
    Structure
    Objectives
    Edge computing platforms
        EdgeX Foundry
            EdgeX Foundry Dataflow
        Eclipse ioFog
        AWS Greengrass
    Siemens industrial Edge
        Architecture
            Industrial Edge Device (IED)
        Setting up the industrial Edge
            Copy app from Hub to IEM
            Installing app from IEM to IED
        Use case implementation example
        App development workflow
        Developing industrial Edge apps
    Conclusion
    Points to remember
Index