Introduction to IoT Using Arduino Training Course

The aim of this training is to explain what the 5G network is and the impact it has on smart technologies. We will explore both the advantages and disadvantages of these technological relationships (5G / IoT) and outline the network's development direction—since its inception, it has been designed specifically for the smart world.

6G is the next-generation wireless communication standard poised to transform IoT ecosystems through ultra-fast connectivity, advanced sensing, and integrated AI capabilities.

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

Upon completing this course, learners will be able to:

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

Course Format

• Concept-focused lectures combined with expert-led discussions.
• Practical exercises designed to reinforce key engineering principles.
• Case-based exploration and scenario analysis within a guided environment.

Course Customisation Options

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

In this instructor-led, live training in New Zealand, participants will learn how to programme the Arduino using advanced techniques as they step through the creation of a simple sensor alert system.

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

• Understand how Arduino works.
• Explore in depth the main components and functionalities of Arduino.
• Programme the Arduino without using the Arduino IDE.

In this instructor-led, live training in New Zealand, participants will learn how to program the Arduino for real-world applications, such as controlling lights, motors, and motion detection sensors. This course assumes the use of actual hardware components in a live lab environment (rather than software-simulated hardware).

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

• Program the Arduino to control lights, motors, and other devices.
• Understand the Arduino architecture, including inputs and connectors for add-on devices.
• Integrate third-party components such as LCDs, accelerometers, gyroscopes, and GPS trackers to extend the Arduino's functionality.
• Explore various programming language options, from C to drag-and-drop environments.
• Test, debug, and deploy Arduino solutions to tackle real-world challenges.

Technological advances and the exponential growth of information are reshaping how business is conducted across many sectors, including the public sector. Driven by the rapid proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals, government data generation and digital archiving rates are climbing. As digital information expands and grows more complex, so too do the challenges of managing, processing, storing, securing, and disposing of it. Emerging tools for capture, search, discovery, and analysis are enabling organisations to extract valuable insights from unstructured data. The government sector stands at a critical juncture, recognising that information is a strategic asset. To better serve the public and meet mission requirements, government bodies must protect, leverage, and analyse both structured and unstructured information. As leaders strive to evolve into data-driven organisations capable of successfully fulfilling their missions, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions will emerge from a convergence of the most disruptive technologies:

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

Big Data stands as one of the key intelligent industry solutions, empowering government to make better decisions by acting on patterns revealed through the analysis of vast data volumes—both related and unrelated, structured and unstructured.

However, achieving these outcomes requires far more than merely 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.

Taking a step towards helping agencies identify these technologies, the White House established the National Big Data Research and Development Initiative in 2012. This initiative included over $200 million in funding to maximise the benefits of the Big Data explosion and the tools required to analyse it.

The challenges posed by Big Data are nearly as daunting as the promise it offers is encouraging. Efficient data storage is one such challenge. As always, budgets remain tight, meaning agencies must minimise the per-megabyte cost of storage while ensuring data remains easily accessible so users can retrieve it when needed and in the format they require. Backing up massive quantities of data further heightens this challenge.

Effectively analysing the data presents another major hurdle. Many agencies employ commercial tools that enable them to sift through mountains of data, identifying trends that can improve operational efficiency. (A recent study by MeriTalk found that federal IT executives believe Big Data could help agencies save more than $500 billion while simultaneously fulfilling mission objectives.)

Custom-developed Big Data tools are also enabling agencies to meet their data analysis needs. For instance, the Oak Ridge National Laboratory's Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has assisted medical researchers in identifying links that can alert doctors to aortic aneurysms before they strike. It is also employed for more routine tasks, such as screening resumes to connect job candidates with hiring managers.

In this instructor-led, live training, participants will learn how to build a robot using Arduino hardware and the Arduino (C/C++) language.

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

• Build and operate a robotic system that includes both software and hardware components
• Understand the key concepts used in robotic technologies
• Assemble motors, sensors and microcontrollers into a working robot
• Design the mechanical structure of a robot

Audience

• Developers
• Engineers
• Hobbyists

Format of the course

• Part lecture, part discussion, with hands-on exercises and practical activities

Note

• Hardware kits will be specified by the instructor prior to the training, but will generally include the following components:
• Arduino board
• Motor controller
• Distance sensor
• Bluetooth slave module
• Prototyping board and cables
• USB cable
• Vehicle kit
• Participants will need to purchase their own hardware.
• If you wish to customise this training, please contact us to arrange.

Insurtech (also known as Digital Insurance) refers to the convergence of insurance and emerging technologies. In the Insurtech space, "digital insurers" apply technological innovations to their business and operating models to reduce costs, enhance customer experience, and improve operational agility.

In this instructor-led training, participants will gain a clear understanding of the technologies, methods, and mindset required to drive digital transformation within their organisations and across the wider industry. The training is designed for managers who need a high-level overview, wish to cut through the hype and jargon, and are ready to take the first steps toward developing an Insurtech strategy.

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

• Discuss Insurtech and all its components intelligently and systematically
• Identify and demystify the role of each key technology within Insurtech.
• Draft a general strategy for implementing Insurtech within their organisation

Audience

• Insurers
• Technologists within the insurance industry
• Insurance stakeholders
• Consultants and business analysts

Format of the course

• Part lecture, part discussion, with exercises and case study group activities

This instructor-led, live training in New Zealand (delivered online or on-site) is designed for intermediate-level IT professionals and business managers seeking to understand the potential of IoT and edge computing to drive efficiency, enable real-time processing, and foster innovation across various industries.

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

• Understand the principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing within the manufacturing, logistics, and energy sectors.
• Differentiate between edge and cloud computing architectures and deployment scenarios.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in New Zealand (available online or on-site) is designed for intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

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

• Understand 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 New Zealand (online or on-site) is designed for product managers and developers who wish to leverage Edge Computing to decentralise data management, achieving faster performance by utilising smart devices located at the source network.

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

• Understand the core concepts and advantages of Edge Computing.
• Identify practical use cases and examples where Edge Computing can be applied.
• Design and build Edge Computing solutions that enable faster data processing and reduce operational costs.

Embedded systems are purpose-built computing systems designed to perform dedicated functions within larger systems. IoT (Internet of Things) is a network of interconnected physical devices embedded with sensors and software that communicate and exchange data over the internet.

This instructor-led, live training (online or on-site) is aimed at beginner-level technical professionals who wish to understand and apply embedded systems and IoT concepts using C and microcontroller architectures.

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

• Understand the architecture and components of embedded systems.
• Write and compile C code for embedded hardware interaction.
• Work with microcontroller peripherals such as timers and ADCs.
• Understand how embedded systems contribute to IoT architectures.

Course Format

• Interactive lecture and discussion.
• Numerous exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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

This instructor-led, live training in New Zealand (available online or on-site) is designed for intermediate-level professionals seeking to apply Federated Learning to optimise IoT and edge computing solutions.

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

• Understand the core principles and benefits of Federated Learning in IoT and edge computing contexts.
• Implement Federated Learning models on IoT devices to enable decentralised AI processing.
• Reduce latency and enhance real-time decision-making in edge computing environments.
• Tackle challenges related to data privacy and network constraints in IoT systems.

This instructor-led, live training in New Zealand (on-site or remote) is designed for engineers seeking to set up, deploy, and manage secure IoT applications.

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

• Develop and deploy applications to securely manage IoT devices.
• Integrate IoT devices with the Cloud in a secure manner.
• Integrate an IoT application with existing infrastructure.

In this instructor-led, live training in New Zealand, participants will explore the various facets of NB-IoT (also known as LTE Cat NB1) as they develop and deploy a sample NB-IoT-based application.

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

• Identify the distinct components of NB-IoT and understand how they integrate to form a cohesive ecosystem.
• Understand and articulate the security features embedded within NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

ThingsBoard is an open-source IoT platform that provides device management, data collection, processing, and visualisation for your IoT solution.

In this instructor-led, live training, participants will learn how to integrate ThingsBoard into their IoT solutions.

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

• Install and configure ThingsBoard
• Understand the fundamentals of ThingsBoard features and architecture
• Build IoT applications with ThingsBoard
• Integrate ThingsBoard with Kafka for telemetry device data routing
• Integrate ThingsBoard with Apache Spark for data aggregation from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Course format

• A blend of lecture, discussion, exercises, and extensive hands-on practice

Note

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