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Course Outline
Module 1: Introduction, Basics and Case Studies from Power Utility Companies
- Fundamentals of all technology stacks in IIoT.
- IoT adaptation rate in the Power Utility Market and how companies are aligning their future business models and operations around IoT.
- Broad-scale application areas.
- Smart Meter, Smart Car, Smart Grid: Brief definition, adoption, and challenges.
- Business rule generation for IoT.
- 3-layered architecture of Big Data: Physical (Sensors), Communication, and Data Intelligence.
- Evolving standards and platform players like Azure, AWS, and Google: Brief introductions, what they offer, and what they do not.
Module 2: Sensors, Hardware and Sensor Networks
- Basic function and architecture of a sensor: Sensor body, sensor mechanism, sensor calibration, sensor maintenance, cost and pricing structure, legacy and modern sensor networks—covering all basics about sensors.
- Development of sensor electronics: IoT vs. legacy, and open source vs. traditional PCB design styles.
- Development of sensor communication protocols: History to modern days. Legacy protocols like Modbus, relay, HART to modern-day Zigbee, Zwave, X10, Bluetooth, ANT, 6LoPAN, WiFi-x, NB-IoT, SignalFx, LORA.
- Powering options for sensors: Battery, solar, mobile, and PoE.
- Energy harvesting solutions for wearables.
- SoC (Sensors on Chips) and MEMS-based sensors.
- Matching sampling rate with application: Why it matters in business.
- What is a sensor network? What is an ad-hoc network?
- Wireless vs. Wireline networks.
- Autopairing and reconnection.
- Which applications to use and where.
- Mathematical exercises to determine which network to pick up and where.
Module 3: Key Security and Risk Concerns in IoT
- Firmware patching risk: The 'soft belly' of IoT.
- Detailed review of security of IoT communication protocols: Transport layers (NB-IoT, 4G, 5G, LORA, Zigbee, etc.) and Application Layers (MQTT, Web Socket, etc.).
- Vulnerability of API endpoints: List of all possible APIs in IoT architecture.
- Vulnerability of gateway devices and services.
- Vulnerability of connected sensors and gateway communication.
- Vulnerability of gateway-server communication.
- Vulnerability of cloud database services in IoT.
- Vulnerability of application layers.
- Vulnerability of gateway management services: Local and cloud-based.
- Risk of log management in edge and non-edge architectures.
Module 4: Machine learning, AI, Analytics for intelligent IoT
- What is the return on investment for Intelligent IoT?
- In Utility: Power Quality, Energy Management, Other Analytics as a Service (AAS).
- Introduction to analytic stacks in IoT: Feature extraction, signal processing, machine learning.
- Introduction to digital signal processing.
- Fundamentals of analytic stacks in IoT applications.
- Learning classification techniques.
- Bayesian prediction: Preparing training files.
- Support Vector Machine.
- Image and video analytics for IoT.
- Fraud and alert analytics through IoT.
- Real-time analytics / Stream analytics.
- Scalability issues of IoT and machine learning.
- FOG computing.
- Edge architecture.
Module 5: Smart Metering - Standards, Security and Future
- Smart Metering.
- Open Smart Grid Protocols (OSGP).
- ANSI C 2.18 Protocols.
- NIST Standard for HAN (Home Area Network).
- Home Plug Powerline Alliance.
- Security Standard for Smart Meter: IEC 62056.
- Security vulnerability of smart metering: Case studies.
Module 6: Cloud Platform for IoT/IaaS/PaaS/SaaS for IoT
- IaaS: Infrastructure as a service—evolving models.
- Mechanism of security breach in the IoT layer for IaaS.
- Middleware for IaaS business implementation in healthcare, home automation, and farming.
- IaaS case study for vehicular information for auto-insurance and agriculture.
- PaaS: Platform as a service in IoT. Case studies of some IoT middleware.
- SaaS: Software/System as a service for IoT business models.
- Updates and patches via web-OTA mechanism.
- Microsoft IoT Central as an example of a PaaS platform.
- Google IoT, AWS IoT PaaS platforms.
Module 7: Future of Smart Grid and Smart Metering
- EV charging as a service.
- EV as a mobile battery and charger wallet.
- Large battery storage: Hydro battery, Lithium battery, and other initiatives.
- Charging and storage as a service.
- Grid as a service for P2P energy trading.
- Use of distributed ledger technology in P2P energy trading: Blockchain, HyperLedger, and DAG.
- IOTA/Tangle in P2P charging.
- IOTA/Tangle in smart energy and smart contracts.
Module 8: A few common IoT systems for Utility monetization
- Home automation.
- Smart parking.
- Energy optimization.
- Automotive—OBD / IaaS / PaaS for insurance and car parking.
- Mobile parking ticketing system.
- Indoor location tracking.
- Smart lighting for smart cities.
- Smart waste disposal systems.
- Smart pollution control in cities.
Module 9: Mobile IoT Modem, 4G, 5G, NB-IOT
- 4G IoT standards for IoT: LTE-M applications, NB-IoT, UNB standard for 3GPP, 4G, LTE CAT-1 IoT.
- 5G IoT standard for IoT: LPWA, eMTC, IMT 2020 5G.
- Detailed architecture of IoT Mobile Modem.
- Security vulnerability of 4G/5G and radio networks.
- IoT gateways: Architecture, classification, and security issues.
Module 10: Managed IoT Service: IoT management layers
- Sensor onboarding.
- Sensor mapping.
- Digital Twin.
- Asset management.
- Managing third-party devices and gateways.
- Managing sensor connectivity and gateway connectivity.
- Managing device and gateway health.
- Managing sensor calibration and QC.
- Managing OTA/patching on a bulk scale.
- Managing firmware, middleware, and analytic builds in distributed systems.
- Security and risk management.
- API management.
- Log management.
Module 11: Managing Critical Assets
- Review of existing fiber optical networks, SCADA, and PLC for power plants, sub-stations, and critical transformers.
- SHM (Structural Health Monitoring) of dam systems: ICOLD standard for dam monitoring.
- Upgrading from SCADA to local cloud-based systems (not public cloud).
- Transitioning SCADA/PLC to intelligent local cloud for more efficient management of critical assets.
- Strategy for new policies on adopting smart devices.
Requirements
- Should have basic knowledge of business operations, devices, electronics systems, and data systems.
- Must have a basic understanding of software and systems.
Basic understanding of Statistics (at an Excel level).
Target Audience
- Decision makers, strategists, and policy makers.
- Engineering leaders, lead developers, and security experts.
Breakdown of the Module (Each module is 2 hours; customers can request any number of modules): Total 22 hours, 3 days
22 Hours
Testimonials (3)
The ability of the trainer to align the course with the requirements of the organization other than just providing the course for the sake of delivering it.
Masilonyane - Revenue Services Lesotho
Course - Big Data Business Intelligence for Govt. Agencies
The oral skills and human side of the trainer (Augustin).
Jeremy Chicon - TE Connectivity
Course - NB-IoT for Developers
I enjoyed the relaxed mood. Also there was a very good balance between theoretical presentation and practical side.
