Edge Computing and IoT Integration

Course Introduction

As billions of IoT devices generate massive volumes of data, centralized cloud computing faces challenges in latency, bandwidth, and real-time decision-making. Edge computing addresses these challenges by processing data closer to the source—at the “edge” of the network. This course provides practical knowledge and hands-on experience in integrating IoT solutions with edge computing infrastructure for fast, reliable, and scalable deployments across various industries.

Participants will gain a strong foundation in IoT architecture, edge devices, edge AI, communication protocols, security, and real-time analytics.

After Study Job Opportunities

• IoT Solutions Architect
• Edge Computing Engineer
• Embedded Systems Developer
• Industrial IoT (IIoT) Engineer
• Edge AI Developer
• Smart Infrastructure Technician
• IoT Systems Integrator

List of Required Documents

• Resume
• Passport or ID Photo

Admission Tests

• Study of the school file
• Tests: general knowledge, English, logic and practical case study
• Individual motivational interview

Professional Training

This course emphasizes:

• End-to-end IoT system design with edge processing capabilities
• Deployment and configuration of edge gateways and edge analytics
• Real-world application in smart homes, factories, transportation, and healthcare
• Hands-on training with microcontrollers, Raspberry Pi, and cloud-edge integration
• Security practices for protecting edge and IoT devices

Objectives and Context of Certification

Upon completion, participants will understand the architecture of IoT ecosystems and edge computing, configure and deploy edge devices and gateways, implement real-time analytics and processing at the edge, integrate cloud services with edge for hybrid solutions, identify use cases across smart cities, manufacturing, energy, and logistics, and earn a certification that validates skills in building edge-enabled IoT systems. This certification acts as a stepping stone for further specialization in IIoT, smart systems, and distributed computing infrastructure.

Graduate Responsibilities

After completing the training, participants will be able to:

• Design, develop, and maintain IoT systems with edge processing
• Select suitable hardware and communication protocols for specific use cases
• Troubleshoot connectivity and performance issues in edge networks
• Apply security best practices in distributed environments
• Collaborate with cross-functional teams on industrial and consumer IoT projects

Post-Study Job Opportunities

Graduates can work in industries such as Smart Manufacturing, Utilities, Healthcare, Transportation, and Telecom. Roles include Edge IoT Developer, Edge AI Specialist, and IoT Support Engineer in domains like Smart Cities, Predictive Maintenance, Connected Vehicles, and Smart Grid.

Evaluation Methodology

• Weekly Quizzes (20%) – Understanding of key concepts
• Hands-on Assignments (30%) – Application to practical scenarios
• Final Project / Mini Capstone (30%) – Cumulative understanding project
• Participation & Interaction (10%)
• Final Assessment Test (10%) – Summary of key takeaways

Course Summary

• Duration: 2 weeks (60 hours) or part-time: 80 hours over 8 weeks
• Format: Online / In-person / Hybrid (ILT)
• Modules Include: IoT Architecture, Edge Hardware Platforms, Communication Protocols, Real-Time Analytics, Edge AI, Security, Cloud-Edge Integration
• Assessment: Capstone project building an edge-enabled IoT solution
• Outcome: Professional certification in Edge Computing and IoT Integration

This course provides practical knowledge and hands-on experience in integrating IoT solutions with edge computing infrastructure for fast, reliable, and scalable deployments.

Starting intake: According to customer request

Tuition Fees:

• €3,000 per participant (60 hours)
• Inhouse corporate training: €30,000 per cohort (10-15 participants)