Practical IoT Edge Computing with Python - Develop End-to-End IoT Solutions with Python, Edge Computing, and Containerization for Fast and Reliable Decision-Making (English Edition) #919832

Build Real-time IoT Solutions Using Python, Edge, and Docker Tools.

Key Features
● Explore real-world IoT use-cases using Python and edge computing.
● Hands-on projects on embedded devices like Raspberry Pi and Jetson Nano.
● Learn edge-cloud integration, Docker, and protocols such as MQTT and CoAP.
● Covers security, streaming data, and future edge computing trends.

Book Description
The Internet of Things (IoT) is reshaping the way the world collects, processes, and responds to data—from smart homes and wearables to autonomous vehicles and industrial automation. As the demand for fast, secure, and intelligent data handling grows, edge computing becomes a key enabler, pushing computation closer to the source and reducing latency, cloud dependency, and security risks.

Practical IoT Edge Computing with Python equips readers with a complete, hands-on roadmap to build robust, real-time IoT systems, using edge devices and Python. You will begin by understanding the core concepts of IoT, and the limitations of traditional cloud-based models. Thus, step by step, you will move through building an IoT data pipeline, integrating edge and cloud, and deploying on devices like Raspberry Pi and Jetson Nano, as well as working with protocols such as MQTT and CoAP.

You will also gain practical experience with data preprocessing, edge intelligence, containerization (Docker/Kubernetes), and security measures like blockchain. Each chapter builds your confidence to design scalable, secure, and responsive IoT systems.

Hence, whether you are a student, developer, or industry professional, this book offers the tools and knowledge to turn IoT concepts into fully functioning edge solutions.

What you will learn
● Understand edge vs. cloud computing in IoT system architectures.
● Build IoT data pipelines including pre-processing and analysis steps.
● Deploy Python-based data processing on embedded edge platforms.
● Use networking protocols (MQTT, CoAP and AMQP) in edge devices.
● Containerize models with Docker/Kubernetes for edge deployment.
● Secure IoT systems with blockchain, and understand privacy challenges.

Table of Contents
1. Introduction to IoT and Edge
2. Conventional Cloud versus Edge
3. Building IoT Data Pipelines
4. Integrating Edge with Cloud in IoT Architecture
5. Exploring Edge Platforms and Devices
6. IoT Data Networking at Edge
7. Pre-Processing Data on Edge Devices
8. Leveraging Edge Intelligence
9. Streaming Data Processing
10. Containerization Technology for Edge Intelligence
11. Data Security and Privacy
12. Future Trends
       Index

About the Authors
Dr. Rajasi Tushar Athawale is a voluntary research associate in Mobile Sensing and Intelligence Security (MoSIS Lab), University of Tennessee, Knoxville, USA, working under Dr. Jian Liu. She also conducts independent research on IoT systems integrating ML/AI and edge computing. Her current research focuses on wearable and contactless gait analysis using multi-task transformer architectures to predict running gait parameters from low-cost IMU data. These models are optimized for real-time deployment on edge devices such as Raspberry Pi 4 and Jetson Nano.