AI & IoT Integration: Building Smart Systems & Connected Devices Training Course

Introduction

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is creating a new paradigm of smart systems and truly connected devices, transforming industries from healthcare to smart cities and manufacturing. This AI & IoT Integration: Building Smart Systems & Connected Devices Training Course is specifically designed for engineers, developers, solution architects, and data scientists who aim to harness this powerful synergy to build intelligent, autonomous, and responsive applications. Participants will delve into the practical methodologies for integrating AI algorithms and machine learning models directly into IoT ecosystems, moving beyond simple data collection to intelligent decision-making at the edge.

This comprehensive program explores how IoT devices can serve as intelligent data sources, feeding real-time information to AI models for advanced analytics, predictive insights, and automated actions. The curriculum covers essential topics such as edge AI, sensor data processing, anomaly detection for IoT, predictive maintenance, and building robust, secure IoT-AI architectures. By mastering the integration of AI with IoT, you will be equipped to design, develop, and deploy innovative smart solutions that drive efficiency, enhance user experiences, and unlock unprecedented value from interconnected data.

Target Audience

• IoT Developers and Engineers.
• AI/ML Engineers looking to apply AI to real-world IoT data.
• Solution Architects designing smart systems.
• Data Scientists working with time-series and sensor data.
• Embedded Systems Engineers interested in AI at the edge.
• Product Managers overseeing smart device development.

Duration

10 days

Course Objectives

1. Understand the fundamental concepts of IoT and the synergy between AI and IoT (AIoT).
2. Learn how to collect, process, and manage data from diverse IoT sensors and devices.
3. Apply machine learning techniques for data analysis and intelligent decision-making within IoT environments.
4. Explore the principles and applications of Edge AI and TinyML for on-device intelligence.
5. Develop skills in building predictive maintenance and anomaly detection systems for IoT.
6. Understand robust architectures for integrating AI models with IoT platforms and cloud services.
7. Address security, privacy, and ethical considerations in AI-powered IoT solutions.
8. Implement practical AIoT applications for smart homes, industrial IoT, and smart cities.

Course Content

Module 1. Introduction to AIoT: The Synergy of AI and IoT

• Defining IoT: Devices, sensors, actuators, connectivity, platforms
• The convergence of AI and IoT (AIoT): Why they are better together
• Key drivers and benefits of AIoT: Automation, predictive insights, real-time decision-making
• Overview of AIoT use cases across various industries (smart cities, healthcare, manufacturing)
• Challenges and opportunities in AIoT integration

Module 2. IoT Data Acquisition and Management

• Types of IoT sensors and their data characteristics (time-series, categorical, geospatial)
• IoT data ingestion strategies: Message queues (MQTT, Kafka), streaming platforms
• Data cleaning, preprocessing, and standardization for IoT data
• Scalable data storage solutions for IoT (time-series databases, cloud storage)
• Managing data volume, velocity, and variety in IoT ecosystems

Module 3. Edge AI and On-Device Intelligence

• Understanding Edge Computing: Processing data closer to the source
• The role of Edge AI: Running ML models directly on IoT devices
• Benefits of Edge AI: Reduced latency, privacy, bandwidth savings, offline capability
• Introduction to TinyML: Machine Learning on microcontrollers and embedded devices
• Optimizing ML models for resource-constrained edge devices

Module 4. Machine Learning for IoT Data Analysis

• Supervised learning applications in IoT: Predictive analytics, classification for device state
• Unsupervised learning for anomaly detection in sensor data
• Time-series analysis and forecasting for IoT data (e.g., sensor readings, energy consumption)
• Clustering for device grouping and behavioral segmentation
• Reinforcement Learning basics for autonomous IoT systems

Module 5. Predictive Maintenance and Anomaly Detection in IoT

• The concept of predictive maintenance: Moving from reactive to proactive
• Collecting and preparing sensor data for anomaly detection
• Implementing machine learning models for anomaly detection (e.g., Isolation Forest, Autoencoders)
• Building predictive models for equipment failure and remaining useful life (RUL)
• Alerting and visualization for detected anomalies and predicted failures

Module 6. AIoT Architecture and Cloud Integration

• Common AIoT architectures: Edge-to-Cloud continuum, hybrid approaches
• Integrating IoT devices with cloud AI platforms (AWS IoT, Azure IoT Hub, Google Cloud IoT Core)
• Data flow and communication protocols in AIoT systems
• Leveraging serverless computing for IoT data processing and AI inference
• Designing scalable and resilient AIoT solutions

Module 7. IoT Connectivity and Communication Protocols

• Overview of IoT communication protocols: MQTT, CoAP, HTTP/S
• Low-power wide-area networks (LPWAN): LoRaWAN, NB-IoT
• Short-range wireless technologies: Bluetooth Low Energy (BLE), Zigbee
• Network topology for AIoT deployments: Mesh, Star, Hub-and-Spoke
• Securing IoT communication channels

Module 8. Security and Privacy in AIoT

• Unique security challenges in IoT environments
• Securing IoT devices: Authentication, authorization, secure boot
• Data privacy considerations for sensitive IoT data
• Edge security: Protecting AI models and data at the edge
• Compliance and regulatory aspects for AIoT (e.g., GDPR implications for sensor data)

Module 9. Deploying and Managing AI Models on IoT Devices

• Workflow for deploying ML models to edge devices
• Model compression and optimization techniques (quantization, pruning)
• Over-the-Air (OTA) updates for AI models on IoT devices
• Edge AI frameworks and toolkits (e.g., TensorFlow Lite, OpenVINO)
• Monitoring and managing model performance on distributed IoT devices

Module 10. AIoT for Smart Cities and Infrastructure

• Smart street lighting, traffic management, and waste management with AIoT
• Environmental monitoring and predictive analytics for air quality
• Smart public safety and surveillance applications
• Intelligent building management systems for energy efficiency
• Data-driven urban planning and resource optimization

Module 11. AIoT for Industrial IoT (IIoT) and Manufacturing

• AIoT in Industry 4.0: Smart factories and connected production lines
• Asset tracking and supply chain optimization
• Quality control and defect detection using computer vision on the edge
• Worker safety and predictive analytics for hazardous conditions
• Digital Twins: Creating virtual replicas for AIoT insights

Module 12. Future Trends and Ethical Considerations in AIoT

• Emerging AIoT technologies: 5G for IoT, AI-powered robotics, haptic feedback
• The role of explainable AI (XAI) in AIoT decision-making
• Ethical implications of pervasive sensing and autonomous AIoT systems
• Addressing bias and fairness in AIoT applications
• Regulatory outlook and standards for responsible AIoT deployment

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank