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Course Outline

Session 1: Business Overview: Why IoT is Critical

  • Case studies from Nest, CISCO, and leading industries
  • IoT adoption rates in North America and how companies are aligning their future business models and operations with IoT
  • Broad application areas
  • The Smart Factory of 2020
  • Industrial Internet concepts
  • Predictive and preventative machine maintenance
  • Tracking machine utilization and productivity
  • Energy and cost optimization for manufacturing plants
  • Generating business rules for IoT
  • Three-layer architecture of Big Data: Physical (Sensors), Communication, and Data Intelligence

Session 2: Introduction to IoT: Sensors

  • Basic sensor function and architecture: body, mechanism, calibration, maintenance, cost, pricing, and legacy vs. modern sensor networks
  • Sensor electronics development: IoT vs. legacy, and open-source vs. traditional PCB design
  • Sensor communication protocols: Evolution from legacy protocols (Modbus, relay, HART) to modern ones (Zigbee, Z-Wave, X10, Bluetooth, ANT, etc.)
  • Business drivers for sensor deployment: FDA/EPA regulations, fraud/interference detection, supervision, quality control, and process management
  • Calibration techniques: Manual, automation, in-field, primary, and secondary calibration and their IoT implications
  • Sensor power options: Battery, solar, Witricity, Mobile, and Power over Ethernet (PoE)
  • Hands-on training with silicon and other sensors (temperature, pressure, vibration, magnetic field, power factor, etc.)

Demo: Logging data from a temperature sensor

Session 3: M2M Communication Fundamentals: Sensor Networks and Wireless Protocols

  • Understanding sensor networks and ad-hoc networks
  • Wireless vs. wired network comparisons
  • WiFi (802.11 families: N to S): Standards application and common vendors
  • Zigbee and Z-Wave: Advantages of low-power mesh networking, long-range Zigbee, and chip introductions
  • Bluetooth/BLE: Low vs. high power, detection speed, BLE classes, and vendor reviews
  • Creating networks using wireless protocols like Piconet via BLE
  • Protocol stacks and packet structures for BLE and Zigbee
  • Other long-distance RF communication links
  • Line of Sight (LOS) vs. Non-Line of Sight (NLOS) links
  • Capacity and throughput calculations
  • Application challenges in wireless protocols: power consumption, reliability, Packet Error Rate (PER), QoS, LOS
  • WAN deployment sensor networks using LPWAN: Comparing emerging protocols like LoRaWAN and NB-IoT
  • Hands-on training with sensor networks

Demo: Device control using BLE

Session 4: Electronics Platform Review, Production, and Cost Projections

  • PCB vs. FPGA vs. ASIC design: Decision-making factors
  • Prototyping electronics vs. production electronics
  • QA certifications for IoT: CE, CSA, UL, IEC, RoHS, IP65—understanding requirements and timing
  • Introduction to multi-layer PCB design and workflows
  • Electronics reliability: Basic concepts of FIT and early mortality rates
  • Environmental and reliability testing: Basic concepts
  • Open-source platforms: Arduino, Raspberry Pi, Beaglebone—when to use them

Session 5: Hardware and Protocol Elements of IIoT for Manufacturing

  • Current state-of-the-art and review of existing market technologies
  • PLC architecture
  • Cloud integration of PLC data
  • Visualization of PLC data
  • Digital Twin concepts
  • PLC protocols (Modbus, Fieldbus, Profibus) and cloud integration
  • Concept of the Industrial Gateway

Session 6: Mobile App Platform Introduction for IoT

  • Mobile app protocol stacks for IoT
  • Mobile-to-server integration factors
  • Intelligent layers for mobile apps
  • iBeacon in iOS
  • Windows Azure
  • Amazon AWS IoT
  • Web interfaces for mobile apps (REST/WebSockets)
  • IoT Application layer protocols (MQTT/CoAP)
  • IoT middleware security: Keys, tokens, and random password generation for gateway device authentication

Demo: Mobile app for tracking IoT-enabled trash cans

Session 7: Machine Learning for Intelligent IIoT

  • Introduction to Machine Learning
  • Learning classification techniques
  • Bayesian Prediction: Preparing training files
  • Support Vector Machines (SVM)
  • Predicting machine failure via vibrational analysis
  • Current signature analysis
  • Time series data and prediction

Demo: Using KNN Algorithm for regression analysis

Demo: SVM-based classification for image and video analysis

Session 8: Analytic Engine for IIoT

  • Insight analytics
  • Visualization analytics
  • Structured predictive analytics
  • Unstructured predictive analytics
  • Recommendation engines
  • Pattern detection
  • Root cause discovery for factory electrical failures
  • Root cause of machine failure
  • Logistics supply chain analysis for manufacturing

Session 9: Security in IoT Implementation

  • Why security is essential for IoT
  • Security breach mechanisms in the IoT layer
  • Privacy-enhancing technologies
  • Network security fundamentals
  • Encryption and cryptography implementation for IoT data
  • Security standards for available platforms
  • European legislation for IoT platform security
  • Secure booting
  • Device authentication
  • Firewalling and Intrusion Prevention Systems (IPS)
  • Updates and patches

Session 10: Database Implementation for IoT Cloud

  • SQL vs. NoSQL: Choosing the right database for your IoT application
  • Open-source vs. licensed databases
  • Available M2M cloud platforms
  • Cassandra for Time Series Data
  • MongoDB
  • Siemens MindSphere
  • GE Predix
  • IBM BlueMix
  • AWS IoT

Session 11: Common IIoT Systems for Manufacturing

  • Energy optimization in manufacturing
  • Vibration analysis for predictive maintenance
  • Power quality analysis for preventative maintenance
  • Recommendation systems for logistics supply chains
  • IIoT systems for industrial safety
  • IIoT system for asset identification
  • IIoT systems for manufacturing plant utilities (Chillers, Air compressors, HVAC)

Demo: Retail, Transportation & Logistics use cases for IoT

Session 12: Big Data for IoT

  • The 4Vs of Big Data: Volume, Velocity, Variety, and Veracity
  • The importance of Big Data in IoT
  • Big Data vs. legacy data in IoT
  • Hadoop for IoT: When and why to use it
  • Storage techniques for images, geospatial, and video data
  • Distributed databases: Cassandra as an example
  • Parallel computing basics for IoT
  • Microservices Architecture

Demo: Apache Spark

Requirements

Fundamental knowledge of business operations, devices, electronics systems, and data systems.

Basic understanding of software and systems.

Foundational understanding of Statistics (Excel level).

 21 Hours

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