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Course Outline
Module 1: Introduction, Basics, and Case Studies from Power Utility Companies
- Fundamentals of all technology stacks in Industrial IoT (IIoT).
- The rate of IoT adoption in the power utility market and how companies are aligning their future business models and operations around IoT.
- Broad-scale application areas.
- Smart Meters, Smart Cars, and Smart Grids – brief definitions, adoption rates, and associated challenges.
- Generation of business rules for IoT.
- The three-layered architecture of Big Data: Physical (Sensors), Communication, and Data Intelligence.
- Evolving standards and platform players like Azure, AWS, and Google – brief introductions covering what they offer and what they lack.
Module 2: Sensors, Hardware, and Sensor Networks
- Basic functions and architecture of a sensor – sensor body, mechanism, calibration, maintenance, cost and pricing structures, legacy versus modern sensor networks – covering all basics about sensors.
- Development of sensor electronics: IoT versus legacy approaches, and open-source versus traditional PCB design styles.
- Development of sensor communication protocols – from history to the modern day. Legacy protocols like Modbus, Relay, and HART versus modern protocols such as Zigbee, Zwave, X10, Bluetooth, ANT, 6LoPAN, WiFi-x, NB-IoT, SignalFx, and LORA.
- Powering options for sensors: Battery, solar, mobile, and PoE.
- Energy harvesting solutions for wearables.
- SoC (Sensors on Chips) and MEMS-based sensors.
- Sampling rate matching with applications – why this matters in business contexts.
- Defining sensor networks and Ad-hoc networks.
- Wireless versus wireline networks.
- Autopairing and reconnection mechanisms.
- Guidance on which applications to use and where.
- Mathematical exercises to determine which network to select and where to deploy it.
Module 3: Key Security and Risk Concerns in IoT
- Firmware patching risks – the soft underbelly of IoT.
- Detailed review of security for IoT communication protocols: Transport layers (NB-IoT, 4G, 5G, LORA, Zigbee, etc.) and Application layers (MQTT, Web Socket, etc.).
- Vulnerabilities of API endpoints – listing all possible APIs in IoT architecture.
- Vulnerabilities of gateway devices and services.
- Vulnerabilities of connected sensors – gateway communication.
- Vulnerabilities of gateway-to-server communication.
- Vulnerabilities of cloud database services in IoT.
- Vulnerabilities of application layers.
- Vulnerabilities of gateway management services – both local and cloud-based.
- Risks associated with log management in edge and non-edge architectures.
Module 4: Machine Learning, AI, and Analytics for Intelligent IoT
- What is the return on investment (ROI) for Intelligent IoT?
- In the utility sector: Power quality, energy management, and other analytics as a service (AAS).
- Introduction to analytics stacks in IoT: Feature extraction, signal processing, and machine learning.
- Introduction to digital signal processing.
- Fundamentals of analytics stacks in IoT applications.
- Learning classification techniques.
- Bayesian prediction – preparing training files.
- Support Vector Machines.
- Image and video analytics for IoT.
- Fraud and alert analytics through IoT.
- Real-time analytics and 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).
- HomePlug Powerline Alliance.
- Security Standard for Smart Meters – IEC 62056.
- Security vulnerabilities of smart metering – case studies.
Module 6: Cloud Platform for IoT / IaaS / PaaS / SaaS for IoT
- IaaS: Infrastructure as a Service – evolving models.
- Mechanisms of security breaches in the IoT layer for IaaS.
- Middleware for IaaS business implementation in healthcare, home automation, and farming.
- IaaS case study for vehicular information in 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 (Over-The-Air) mechanisms.
- Microsoft IoT Central as an example of a PaaS platform.
- Google IoT and AWS IoT PaaS platforms.
Module 7: Future of Smart Grid and Smart Metering
- EV charging as a service.
- EVs as mobile batteries and charging wallets.
- Large battery storage – Hydro batteries, Lithium batteries, 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 systems.
- 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, and LTE CAT-1 IoT.
- 5G IoT standards for IoT: LPWA, eMTC, IMT 2020 5G.
- Detailed architecture of IoT mobile modems.
- Security vulnerabilities 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 and gateway connectivity.
- Managing device and gateway health.
- Managing sensor calibration and Quality Control (QC).
- Managing OTA/patching on a bulk scale.
- Managing firmware, middleware, and analytics builds in distributed systems.
- Security and risk management.
- API management.
- Log management.
Module 11: Managing Critical Assets
- Review of existing fiber optic networks, SCADA, and PLC systems for power plants, substations, and critical transformers.
- Structural Health Monitoring (SHM) of dam systems – ICOLD standards for dam monitoring.
- Upgrading from SCADA to local cloud-based systems (excluding public clouds).
- Transitioning SCADA/PLC to intelligent local clouds for more efficient management of critical assets.
- Strategy for new policies on adopting smart devices.
Requirements
- A basic understanding of business operations, devices, electronic systems, and data systems is required.
- A fundamental grasp of software and systems is essential.
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 (2)
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).
