Smart Devices with IOT
This practical micro course empowers learners to build professional smart home devices using IoT platforms, ESP microcontrollers, and cloud connectivity. Students create connected devices with mobile apps, cloud dashboards, and basic AI features for real-world automation and monitoring applications.
Key Learning Outcomes:
- Build cloud-connected IoT devices with Arduino Cloud and ESP platforms
- Create mobile/web dashboards for remote device control and monitoring
- Implement over-the-air (OTA) updates and secure cloud communication
- Integrate basic AI features like anomaly detection and predictive maintenance
- Design power-efficient IoT nodes with deep sleep and battery optimization
- Develop complete IoT solutions from hardware to cloud deployment
Platforms & Tools
- Arduino Cloud dashboards and device sync
- Blynk mobile app control
- ThingSpeak data visualization
- AWS IoT Core secure device messaging
Topics Covered
- ESP32/ESP8266 IoT programming with Arduino Cloud IoT
- Cloud platforms: Arduino Cloud, Blynk, ThingSpeak, AWS IoT Core
- Secure communication: MQTT, HTTPS, device certificates, API keys
- Mobile app integration: Native apps, Progressive Web Apps (PWA)
- Basic AI/ML: Edge anomaly detection, cloud predictive analytics
- Power management: Deep sleep modes, energy harvesting, solar charging
- Data visualization: Real-time charts, gauges, historical trends
Projects (Total 8+):
- ESP32/ESP8266 IoT programming and cloud linking
- MQTT/HTTPS security basics, API keys, device identity
- Progressive Web Apps (PWA) and dashboards
- Intro to edge anomaly alerts and cloud analytics
- Deep sleep modes and battery optimization
- Real-time charts, gauges, and historical trends
Teaching Methodology:
- Interactive coding and circuit building sessions
- Detailed Arduino programming tutorials and examples
- Hands-on projects with real components to reinforce learning
- Group discussions and troubleshooting workshops
- Regular assessment through project demonstrations
Prerequisites:
Completion of Applied Robotics or equivalent ROS/STM32 experience required.
Additional Resources
- ROS2 Navigation and control references
- Embedded AI optimization tutorials
- Simulation-driven design case studies
- Open-source robotics repositories for navigation and control
- Safety and standards awareness for autonomous mobile robots
Outcome & Benefits
Graduates can design autonomous systems that combine ROS2 navigation, embedded AI perception, and real-time STM32 control validated through simulation. These capabilities prepare students for advanced roles in:
- Autonomous vehicle and mobile robot engineering
- Industrial automation and smart logistics
- Robotics research and AI perception systems
- Next-generation intelligent machine development
An elite pathway into AI-powered autonomous robotics engineering.
Key Highlights
Project Based Learning
Real World Project
Comprehensive Curriculum
Expert Instructors
6 Steps to your Course Path
Building Future Innovators with Robotics & AI
At Vctroid, we go beyond basic coding by immersing students in hands-on robotics, real-world problem solving, and innovation-driven learning. Through expert-led training, gamified challenges, and exposure to global-level competitions, we help students build strong technical foundations, creative confidence, and future-ready skills that truly set them apart.
