Description
Introduction of Edge Computing and IoT
Edge IoT is transforming how data is processed and managed, bringing computation closer to where data is generated, enhancing response times, and reducing bandwidth usage. This course provides a comprehensive introduction to Edge IoT, focusing on its principles, key applications, and how it differs from traditional cloud-based IoT systems. Attendees will explore how Edge IoT enables real-time analytics, improves system efficiency, and supports the growing number of IoT devices in industries such as healthcare, manufacturing, and smart cities.
Prerequisites
- Basic understanding of IoT and cloud computing concepts
- Familiarity with network architectures
- Programming knowledge in languages such as Python or C++ is beneficial but not mandatory
- Understanding of sensors, embedded systems, and communication protocols (optional)
Table of Contents
- Introduction to Edge Computing and IoT
1.1. Overview of IoT architecture
1.2. Traditional IoT and Edge IoT
1.3. Benefits of Edge IoT: latency, bandwidth, and security
1.4. Key applications of Edge IoT in different industries
1.5. Challenges of Edge IoT - Edge Computing Architecture
2.1. The anatomy of an Edge device
2.2. Distributed computing and its relevance to IoT
2.3. Data processing: From sensor to edge
2.4. Edge gateway and communication protocols (MQTT, CoAP)
2.5. Cloud-Edge hybrid systems: When to use which - Edge Devices and Hardware
3.1. Overview of popular edge devices: Raspberry Pi, NVIDIA Jetson, etc.
3.2. Sensors and actuators in Edge IoT
3.3. Real-time data processing and local storage
3.4. Power management in Edge IoT systems
3.5. Case studies of hardware implementations - Software in Edge IoT
4.1. Operating systems for edge devices: Linux, RTOS
4.2. Programming edge devices: Python, C++, and JavaScript(Ref: C++ with Linux)
4.3. Introduction to machine learning at the edge
4.4. Managing Edge IoT devices remotely - Networking and Communication in Edge IoT
5.1. Key networking concepts for Edge IoT (LAN, WAN, 5G, etc.)
5.2. Communication protocols used in Edge IoT
5.3. Latency and bandwidth considerations
5.4. Edge security: Securing the communication and device - Data Management in Edge IoT
6.1. Data filtering, processing, and storage at the edge
6.2. Handling big data at the edge vs. the cloud
6.3. Implementing AI and ML for real-time decision-making
6.4. Edge-to-cloud integration for analytics - Edge IoT Security
7.1. Overview of security risks in Edge IoT systems
7.2. Data encryption, access control, and device authentication
7.3. Edge device firmware and software security
7.4. Case study: Security breaches in Edge IoT and lessons learned - Case Studies and Real-World Applications
8.1. Smart cities: Traffic management and surveillance systems
8.2. Healthcare: Real-time patient monitoring
8.3. Manufacturing: Predictive maintenance and automation
8.4. Agriculture: Precision farming using edge analytics
8.5. Future trends in Edge IoT applications - Hands-on Project and Demo
9.1. Setting up an edge computing environment with Raspberry Pi
9.2. Connecting sensors and performing local data processing
9.3. Building a simple edge-to-cloud solution
9.4. Real-time monitoring and control using Edge IoT
9.5. Group discussion and Q&A session
To conclude; this structured course will help participants gain a solid foundation in Edge IoT concepts and how to implement real-world applications, and addressing both technical and practical challenges.
Reviews
There are no reviews yet.