A Next-generation IoT Platform for Edge AI Applications Leveraging Sensors and WebAssembly

Overview

Discover how to build an advanced IoT platform combining Edge AI, sensors, and WebAssembly for seamless device-to-cloud integration and dynamic AI model deployment.

Syllabus

- Introduction to IoT and Edge AI -- Overview of IoT Architecture -- Introduction to Edge AI and its Importance -- Key Components of an IoT Platform - Understanding Sensors -- Types of Sensors Used in IoT -- Sensor Data Acquisition and Processing -- Integrating Sensors with Edge Devices - Introduction to WebAssembly -- Basics of WebAssembly and its Role in IoT -- Benefits of Using WebAssembly for Edge Devices -- WebAssembly vs Traditional IoT Runtime Environments - Building the IoT Platform -- Designing a Scalable IoT Architecture -- Implementing Device-to-Cloud Communication -- Managing IoT Data Lifecycle - Dynamic AI Model Deployment on Edge Devices -- Overview of AI Models for Edge Computing -- Techniques for Training and Compacting AI Models for Edge Deployment -- Deploying AI Models using WebAssembly - Device-to-Cloud Integration -- Protocols and Standards for IoT Communication -- Ensuring Secure and Reliable Data Transmission -- Cloud Platforms for IoT Applications - Case Studies and Applications -- Real-world Examples of IoT Platforms with Edge AI -- Industry-specific Use Cases -- Lessons Learned and Best Practices - Hands-on Labs and Projects -- Setting Up a Basic IoT Platform -- Connecting and Managing Sensors with WebAssembly -- Deploying and Testing AI Models on Edge Devices - Challenges and Future Trends -- Current Challenges in Edge AI and IoT -- Emerging Trends and Technologies -- The Future of IoT Platforms and AI - Conclusion and Next Steps -- Recap of Key Learnings -- Further Resources for Learning -- Opportunities for Innovation in IoT and Edge AI

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