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Course Outline
Module 1: Introduction, Basics and Case Studies from Power Utility Companies
- Fundamentals of all technology stacks in IIoT.
- The rate of IoT adaptation 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 vehicle, and smart grid: brief definitions, adoption rates, and challenges.
- Business rule generation 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 where they fall short.
Module 2: Sensors, Hardware and Sensor Networks
- Basic function and architecture of a sensor: sensor body, mechanism, calibration, maintenance, cost and pricing structure, and the distinction between legacy and modern sensor networks. All the 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 modern times. Legacy protocols such as Modbus, relay, and HART, compared to modern protocols like Zigbee, Zwave, X10, Bluetooth, ANT, 6LoPAN, WiFi-x, NB-IoT, SignalFx, and LORA.
- Powering options for sensors: Battery, solar, mobile, and Power over Ethernet (PoE).
- Energy harvesting solutions for wearables.
- SoC (Sensors on Chips) and MEMS-based sensors.
- Sampling rate matching with applications: why this matters in a business context.
- What is a sensor network? What is an ad-hoc network?
- Wireless versus wireline networks.
- Autopairing and reconnection.
- 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 risk: 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.).
- Vulnerability of API endpoints: a list of all possible APIs in IoT architecture.
- Vulnerability of gateway devices and services.
- Vulnerability of connected sensors: gateway communication.
- Vulnerability of gateway-to-server communication.
- Vulnerability of cloud database services in IoT.
- Vulnerability of application layers.
- Vulnerability of gateway management services: local and cloud-based.
- Risks associated with 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 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 Machine.
- 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 C12.18 Protocols.
- NIST standards for HAN (Home Area Network).
- HomePlug Powerline Alliance.
- Security standards 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: 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 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.
- EV as a mobile battery and charging wallet.
- Large-scale battery storage: hydro battery, lithium battery, and other initiatives.
- Charging and storage as a service.
- Grid as a service for P2P energy trading.
- The 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 optimisation.
- 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 the IoT mobile modem.
- 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 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 fibre optical networks, SCADA, and PLC systems for power plants, substations, and critical transformers.
- SHM (Structural Health Monitoring) of dam systems: ICOLD standards for dam monitoring.
- Upgrading from SCADA to a local cloud-based system (not public cloud).
- Moving from SCADA/PLC to an intelligent local cloud for more efficient management of critical assets.
- Strategy for new policies on adopting smart devices.
Requirements
- Candidates should have a basic knowledge of business operations, devices, electronic systems, and data systems.
- A basic understanding of software and systems is essential.
A basic understanding of statistics (at an Excel level) is also required.
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 over 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.
