Smart solutions for HR Training Course

This training aims to clarify what 5G networks are and how they influence smart technologies. We will explore both the benefits and drawbacks of the synergy between 5G and the Internet of Things (IoT), while highlighting the developmental trajectory of networks designed from the outset for the smart ecosystem.

6G represents the next generation of wireless communication standards, poised to revolutionise IoT ecosystems through ultra-fast connectivity, advanced sensing, and integrated AI capabilities.

This instructor-led, live training (available online or onsite) is designed for advanced-level participants who wish to understand and leverage the emerging intersection of 6G technologies and IoT applications.

By completing this course, learners will gain the ability to:

• Explain the core technical concepts behind 6G.
• Assess how 6G will reshape IoT device communication and architecture.
• Evaluate 6G-enabled IoT use cases across industries.
• Prepare strategies for integrating 6G capabilities into existing IoT solutions.

Format of the Course

• Concept-focused lectures combined with expert discussion.
• Applied exercises designed to reinforce key engineering principles.
• Case-based exploration and scenario analysis in a guided environment.

Course Customization Options

• For tailored versions of this training aligned with your organisational technology roadmap, please contact us to arrange.

Advancements in technology and the exponential growth of data are reshaping business operations across various sectors, including the government sector. Government agencies are experiencing a surge in data generation and digital archiving, driven by the rapid proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information becomes more expansive and complex, the management, processing, storage, security, and disposition of this data become increasingly intricate. New tools for capture, search, discovery, and analysis are enabling organisations to extract valuable insights from unstructured data. The government sector is at a critical juncture, recognising that information is a strategic asset. Governments must protect, leverage, and analyse both structured and unstructured information to better serve citizens and meet mission objectives. As government leaders strive to evolve into data-driven organisations to successfully achieve their missions, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions are being developed through the integration of the most disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

Big Data represents an intelligent industry solution that enables government entities to make better decisions by taking action based on patterns revealed through the analysis of large volumes of data—both related and unrelated, structured and unstructured.

However, achieving these outcomes requires far more than simply accumulating massive quantities of data. "Making sense of these volumes of Big Data requires cutting-edge tools and technologies that can analyse and extract useful knowledge from vast and diverse streams of information," wrote Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy in a post on the OSTP Blog.

The White House took a significant step toward helping agencies identify these technologies when it established the National Big Data Research and Development Initiative in 2012. This initiative included more than $200 million to maximise the potential of the Big Data explosion and the tools needed to analyse it.

The challenges posed by Big Data are nearly as daunting as its promise is encouraging. Efficiently storing data is one such challenge. With budgets always tight, agencies must minimise the per-megabyte cost of storage and keep data readily accessible so users can retrieve it when and how they need it. Backing up massive quantities of data further heightens this challenge.

Effectively analysing data is another major challenge. Many agencies employ commercial tools that enable them to sift through mountains of data, spotting trends that help them operate more efficiently. A recent study by MeriTalk found that federal IT executives believe Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.

Custom-developed Big Data tools are also allowing agencies to address their need for data analysis. For example, Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers identify a link that can alert doctors to aortic aneurysms before they occur. It is also used for more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

This instructor-led live training in Australia (online or onsite) is tailored for intermediate-level IT professionals and business managers who wish to understand the potential of IoT and edge computing to enable efficiency, real-time processing, and innovation across various industries.

By the end of this training, participants will be able to:

• Comprehend the principles of IoT and edge computing and their pivotal role in digital transformation.
• Recognise practical use cases for IoT and edge computing within the manufacturing, logistics, and energy sectors.
• Distinguish between edge and cloud computing architectures and their respective deployment scenarios.
• Deploy edge computing solutions to facilitate predictive maintenance and real-time decision-making.

This instructor-led, live training in Australia (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing IoT applications with intelligent data processing and analytics capabilities.

By the end of this training, participants will be able to:

• Grasp the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in IoT systems.
• Integrate Edge AI with various IoT protocols and platforms.
• Address ethical considerations and best practices in Edge AI for IoT.

This instructor-led, live training in Australia (online or onsite) is designed for product managers and developers who wish to use Edge Computing to decentralize data management for improved performance, leveraging smart devices located on the source network.

By the end of this training, participants will be able to:

• Grasp the fundamental concepts and advantages of Edge Computing.
• Identify use cases and examples where Edge Computing can be applied.
• Design and build Edge Computing solutions for faster data processing and reduced operational costs.

Embedded systems are specialised computing solutions designed to execute specific tasks within broader technological environments. The Internet of Things (IoT) refers to a network of physical devices equipped with sensors and software, enabling them to connect, communicate, and share data via the internet.

This instructor-led, live training (available online or onsite) is tailored for beginner-level technical professionals looking to grasp and apply concepts related to embedded systems and IoT using C programming and microcontroller architectures.

Upon completion of this training, participants will be able to:

• Comprehend the architecture and key components of embedded systems.
• Write and compile C code to facilitate interaction with embedded hardware.
• Utilise microcontroller peripherals, including timers and ADCs.
• Understand the role embedded systems play within IoT architectures.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

• To request a customised training session for this course, please get in touch with us to make arrangements.

This instructor-led, live training in Australia (online or onsite) targets intermediate-level professionals eager to apply Federated Learning to optimise IoT and edge computing solutions.

By the conclusion of this training, participants will be equipped to:

• Grasp the principles and advantages of Federated Learning in IoT and edge computing.
• Deploy Federated Learning models on IoT devices for decentralized AI processing.
• Minimise latency and enhance real-time decision-making within edge computing environments.
• Navigate challenges pertaining to data privacy and network constraints in IoT systems.

The Internet of Things (IoT) refers to a network infrastructure that wirelessly connects physical objects and software applications, enabling them to communicate and exchange data through network communications, cloud computing, and data capture. C is a general-purpose programming language recommended for IoT development due to its widespread availability and low-level programming advantages.

During this instructor-led live training, participants will learn how to develop IoT solutions using C.

By the end of this training, participants will be able to:

• Install and configure NetBeans for programming IoT systems with C
• Understand the fundamentals of IoT architecture
• Learn the benefits of using C in programming IoT systems
• Build, test, deploy, and troubleshoot an IoT system using C

Audience

• Developers
• Engineers

Format of the course

• A mix of lectures, discussions, exercises, and extensive hands-on practice

Note

• To request a customised training for this course, please contact us to arrange.

The Internet of Things (IoT) constitutes a network infrastructure that seamlessly connects physical devices and software applications via wireless links, enabling them to communicate and exchange data through network protocols, cloud computing, and data capture mechanisms. Java, a versatile programming language celebrated for its "write once, run anywhere" capability, is highly recommended for IoT development due to its portability and efficiency.

Through this instructor-led live training, participants will gain the skills to programme IoT solutions using Java.

Upon completion of this training, participants will be able to:

• Install and configure necessary tools and frameworks, such as the Eclipse Open IoT Stack, for programming IoT systems with Java
• Grasp the fundamental principles of IoT architecture
• Utilise the Eclipse Open IoT Stack for Java to connect and manage devices within an IoT solution
• Construct, test, and deploy an IoT system using Java

Target Audience

• Developers
• Engineers

Course Format

• A blend of lectures and discussions, complemented by exercises and extensive hands-on practice

Note

• For inquiries regarding customized training for this course, please contact us to make arrangements.

Connected devices are disrupting numerous industries, with power utilities being no exception. Utility companies currently face four primary challenges driven by the growth of IoT.

1. Machines, controllers, HMIs, and SCADA systems are increasingly becoming cloud-connected, with vendors promising enhanced analytics and insights via their data for predictive and preventative maintenance. However, the strict quarantine policies regarding critical assets prevent power companies from utilising these new IoT features offered by machine and controller vendors.
2. With the continuously decreasing costs of solar and wind power microgrids, utility companies will soon experience declining revenue from power generation. To compensate for this loss, companies must aggressively pursue new revenue streams such as home energy management as a service, energy storage as a service, and providing grid services for EV charging, peer-to-peer (P2P) energy trading between homes and microgrids, microgrid-to-microgrid transfers, microgrid-to-battery connections, and home-to-battery interactions. These activities require smart metering, smart grids, and secure transactions facilitated by Distributed Ledger Technology (DLT) such as IOTA. Additionally, utilities are exploring opportunities to offer smart city services to local authorities.
3. For critical infrastructure like dams, the International Committee on Large Dams (ICOLD) requires real-time Structural Health Monitoring (SHM) to provide early warnings of impending dangers such as dam, rock, or tunnel collapses, allowing for the evacuation of affected individuals.
4. Another emerging revenue area is EV charging in car parks. This module explores how IoT can facilitate smart charging and smart parking solutions.

Over the past three years, engineering in IoT has undergone massive changes, primarily driven by Microsoft, Google, and Amazon. These tech giants have invested billions of dollars into developing IoT platforms that are easier to manage and secure. IoT edge computing has gained significant momentum in both research and deployment as the practical means for implementing IoT. Furthermore, 5G promises to transform the IoT business landscape, leading to unprecedented levels of research funding in this field. Consequently, for any practicing engineer, it is essential to understand the IoT platforms developed by major players like AWS, Google, and especially Microsoft.

However, none of the aforementioned platforms offer an exhaustive or fully comprehensive solution for scalable IoT. For instance, deploying smart meters to millions of homes requires additional technologies to secure the meters, radio networks, IoT management technology, and many other secured services. Strategy, pricing, and security for any IoT deployment must be optimal and acceptable. Given the extensive interdisciplinary knowledge required, it is difficult for any company to assemble a team capable of meeting all these requirements.

This course is a modest attempt to educate key decision-makers, developers, and security experts about the challenges, risks, and practical approaches to deploying IoT for next-generation power utility businesses.

Additionally, with scalable deployments, managing IoT services for thousands of sensors and connections is emerging as a separate engineering subject of research. This area, formally known as managed IoT services, is experiencing rapid growth because the challenges of scalable IoT are much greater than simply building them. This includes securing over-the-top firmware/software updates, managing sensor and system calibration, auto-diagnosing connection issues, narrowing down root causes of API failures, and tracking the hardware and service health of distributed systems.

Course objectives

The main objective of the course is to introduce emerging technological options, platforms, and case studies of IoT implementation in power utility companies, including smart metering, smart cars, structural health monitoring (SHM), power quality diagnosis, and smart contracts. It provides a basic introduction to all elements of IoT, such as mechanical aspects, electronics/sensor platforms, wireless and wireline protocols, mobile-to-electronics integration, mobile-to-enterprise integration, and data-analytics and control plane applications.

1. IoT technology stacks: Devices, gateways, edge, edge cloud, public cloud, IoT databases, web and mobile applications for IoT, and centralized versus decentralized IoT.
2. IoT ecosystem for business, third-party device management, and risk management of the entire IoT ecosystem.
3. M2M wireless protocols for IoT: WiFi, SigFox, LoRa, LPWAN, Zigbee/Z-Wave, Bluetooth, ANT+: Understanding when and where to use each.
4. Fundamentals of IoT gateways: Risks, management, and ecosystem.
5. Mobile/Desktop/Web apps for registration, data acquisition, and control. Overview of available M2M data acquisition platforms for IoT: AWS IoT, Azure IoT, Google IoT.
6. Security issues and solutions for IoT: A review of security across all technology stacks.
7. Enterprise IoT platforms such as Microsoft Azure IoT Suites, AWS IoT, Google IoT, and Siemens MindSphere.
8. Smart metering, Open Smart Grid Protocols (OSGP), ANSI C 2.18 protocols, NIST Standard for HAN (Home Area Network), HomePlug Powerline Alliance, and the security standard for smart meters: IEC 62056.
9. Distributed Ledger Technology (DLT) such as Blockchain, HyperLedger, and DAG (Directed Acyclic Graph) for smart contracts, P2P transactions, and smart car charging.
10. IoT applications for critical infrastructure like dams, transformers, substations, and high-tension wires.

This instructor-led live training in Australia (online or onsite) is tailored for developers and programmers who wish to install, configure, and manage the Kaa platform to build IoT applications.

By the end of this training, participants will be able to build, develop, manage, and implement IoT applications for smart devices and machines using Kaa.

This instructor-led, live training in Australia (online or onsite) is aimed at advanced-level IoT developers and smart home enthusiasts who wish to automate IoT processes and create innovative solutions using n8n.

By the end of this training, participants will be able to:

• Set up and configure n8n for IoT workflow automation.
• Integrate IoT devices and platforms using n8n nodes and connectors.
• Implement custom workflows to automate IoT tasks and processes.
• Use IoT protocols like MQTT and REST APIs within n8n workflows.
• Monitor, troubleshoot, and optimise IoT automation workflows.

In this instructor-led, live training in Australia, participants will learn how to maximise the performance of Nginx while setting up, configuring, monitoring, and troubleshooting it to handle various forms of HTTP and TCP traffic. Topics covered include configuring the most critical parameters in Nginx, the operating system, and a virtual machine to derive maximum value from Nginx.

This instructor-led, live training in Australia (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Develop and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection using TinyML.
• Optimize TinyML models for efficient power and memory usage.