Cloud and Edge Computing in IoT Ecosystems Training Course

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Duration

3 Days

Course Overview

This training course explores how IoT ecosystems leverage cloud computing, edge devices, and hybrid architectures to process and analyze data efficiently. Participants will gain a deep understanding of cloud-based IoT platforms, edge computing technologies, and hybrid architectures that balance data processing between the edge and the cloud. Through hands-on exercises, attendees will configure edge devices, cloud services, and hybrid IoT solutions for real-time data processing and decision-making.

Format of Training

Instructor-led sessions
Hands-on lab exercises
Practical demonstrations
Interactive discussions

Course Objectives

Understand the role of cloud computing in IoT and its advantages.
Explore edge computing and its impact on reducing latency and bandwidth usage.
Implement hybrid architectures that balance cloud and edge processing.
Deploy IoT data pipelines on AWS IoT, Azure IoT, and Google Cloud IoT.
Optimize real-time data processing on edge devices.
Secure IoT data across cloud and edge environments.
Build and deploy an end-to-end hybrid IoT system.

Prerequisites

Course Outline

Day 1

Session 1: Introduction to Cloud Computing in IoT

Role of cloud computing in IoT ecosystems
Benefits of scalability, storage, and remote access in IoT
Overview of AWS IoT, Azure IoT, and Google Cloud IoT

Session 2: Cloud Architecture for IoT Applications

Cloud-based IoT data processing models (batch vs. real-time)
IoT device-to-cloud and cloud-to-cloud communication
Hands-on: Setting up an IoT device with a cloud platform

Session 3: Data Storage and Management in IoT Cloud Platforms

Time-series databases for IoT data storage
IoT data lifecycle: ingestion, processing, and retrieval
Hands-on: Configuring cloud-based IoT data storage

Day 2

Session 1: Edge Computing for IoT Systems

Edge vs. cloud computing in IoT ecosystems
Advantages of local data processing and reduced latency
Hands-on: Deploying an IoT edge computing system

Session 2: Implementing Hybrid IoT Architectures

Edge-to-cloud integration strategies
Load balancing and optimizing bandwidth usage
Hands-on: Configuring a hybrid IoT solution with cloud and edge computing

Session 3: Edge AI and Real-Time Decision Making

AI/ML models running on edge devices
Using TensorFlow Lite and OpenVINO for edge AI
Hands-on: Running an AI-powered IoT application at the edge

Day 3

Session 1: Security and Privacy in Cloud and Edge Computing

Cybersecurity threats in cloud and edge IoT
Implementing encryption, authentication, and secure communication
Hands-on: Securing cloud-edge IoT data transfers

Session 2: Deploying an End-to-End IoT Application

Integrating sensors, edge devices, and cloud storage
Optimizing real-time analytics and automation
Hands-on: Building and deploying a full IoT system with cloud and edge processing

Session 3: Future Trends in Cloud and Edge Computing for IoT

The role of 5G, blockchain, and serverless computing in IoT
Emerging edge AI and federated learning techniques
Hands-on: Finalizing and presenting an IoT cloud-edge prototype

Bespoke Option

We are open to customizing this program to align with your specific learning objectives. If your team has particular goals or areas they wish to focus on, we would be happy to tailor the course outline to meet those needs and ensure the program supports the achievement of your desired outcomes.

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