6G and IoT 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 and the Intelligent Edge is a forward-looking course that explores the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT architects who wish to understand and design next-generation distributed architectures leveraging the synergy of 6G connectivity and intelligent edge systems.

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

• Grasp how 6G will transform edge computing and IoT architectures.
• Design distributed systems for ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge for intelligent decision-making.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures.
• Evaluate business and operational models enabled by 6G-edge convergence.

Format of the Course

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation with optional edge or container tools.

Course Customization Options

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

The '6G-Ready Infrastructure and Network Design' program is a specialised training course designed to equip existing telecommunications and enterprise networks for the upcoming era of wireless connectivity. It achieves this through advanced architectural and engineering methodologies. Key topics encompass the evolution of transport and fronthaul networks, cloud-native and open RAN approaches, edge and distributed computing, timing and synchronisation, spectrum and RF readiness, automation and AI-native operations, alongside practical migration strategies for operators and enterprises.

This instructor-led, live training session (available online or onsite) targets intermediate-level telecom engineers and network architects who aim to design, optimise, and evolve their current 4G/5G infrastructure to satisfy the performance, scalability, and reliability demands of 6G.

Upon completing this course, participants will be able to:

• Evaluate current network infrastructure gaps and its readiness for 6G evolution.
• Design transport and fronthaul/backhaul architectures suited for ultra-low latency and high throughput.
• Implement cloud-native principles, vRAN/O-RAN integration, and edge compute placement for 6G use cases.
• Plan the timing, synchronisation, and RF upgrades necessary for mmWave/THz and dense deployments.
• Establish testing, validation, and operational monitoring strategies to guarantee performance and reliability.
• Develop a phased migration and investment roadmap aligned with business priorities and risk management.

Course Format

• Technical lectures and in-depth architecture studies.
• Case studies and design workshops.
• Hands-on labs utilising simulation and verification tools.

Customisation Options

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

This instructor-led, live training session (available online or onsite) is designed for executives seeking to comprehend the global 6G landscape, evaluate its commercial potential, and formulate strategic plans for early investment.

Upon completion of this course, participants will be equipped with the insights required to:

• Recognise emerging market trends and global initiatives influencing the 6G ecosystem.
• Grasp the regulatory frameworks and spectrum allocation schedules associated with IMT-2030.
• Assess the dynamic vendor landscape and technology readiness levels.
• Construct a roadmap for early investment, research collaborations, and pilot projects.

AI and Digital Twins in 6G Networks is an advanced, specialised course that examines how digital twin technology and AI-native optimisation converge to model, simulate, and operate next-generation 6G infrastructures.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to apply digital twin methodologies and AI techniques to design, validate, and optimise 6G network behaviour in realistic, reproducible environments.

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

• Explain the role and architecture of digital twins in the lifecycle of 6G networks.
• Build and configure digital twin models for RAN, transport, and edge compute components.
• Apply AI/ML methods for closed-loop optimisation, anomaly detection, and predictive maintenance of network elements.
• Integrate real-time telemetry and simulation data to enable model-driven orchestration and intent-based control.
• Design validation and verification workflows using co-simulation, emulation, and digital twin testbeds.

Format of the Course

• Technical lectures and architecture deep-dives.
• Hands-on labs with simulators, twin models, and ML toolchains.
• Case study walkthroughs and a practical mini-project integration exercise.

Course Customization Options

• To request a customised training for this course, 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.

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.

6G represents the next-generation wireless networking paradigm, building upon the advancements of 5G to deliver ultra-low latency, extremely high throughput, pervasive intelligence, and integrated sensing capabilities for a new class of applications and services.

This instructor-led live training, available online or onsite, is designed for beginner to intermediate-level professionals seeking to understand the technical foundations, regulatory landscape, and strategic business implications of 6G to inform planning and decision-making.

Upon completing this training, participants will be able to:

• Explain core 6G concepts and how they differ from 5G.
• Identify key enabling technologies and their practical implications.
• Assess high-value use cases and industry verticals that 6G will enable.
• Understand spectrum, regulatory, and policy considerations for 6G adoption.
• Draft a high-level 6G readiness roadmap for their organization.

Format of the Course

• Interactive lectures with conceptual walkthroughs.
• Case studies and sector-specific examples.
• Group workshop to develop an organizational 6G readiness outline.

Course Customization Options

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

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.