Kaa IoT 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.

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.

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.

The Internet of Things (IoT) is 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 highly recommended for IoT development due to its widespread availability and advantages in low-level programming.

In 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 for programming IoT systems
• Build, test, deploy, and troubleshoot an IoT system using C

Audience

• Developers
• Engineers

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.

The Internet of Things (IoT) constitutes a network infrastructure that wirelessly links physical objects with software applications, enabling them to communicate and exchange data through network communications, cloud computing, and data capture. Java, a versatile programming language renowned for its "write once, run anywhere" capability, is highly recommended for IoT development due to its portability and efficiency.

During this instructor-led live training, participants will acquire the skills necessary to programme IoT solutions using Java.

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

• Install and configure the requisite tools and frameworks (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

Audience

• Developers
• Engineers

Format of the course

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

Note

• Should you wish to arrange customised training for this course, please contact us to make the necessary arrangements.

The proliferation of connected devices is disrupting numerous industries, with the power utility sector being no exception. Power utility companies currently face four key challenges arising from the growth of the Internet of Things (IoT):

1. Machines, controllers, Human-Machine Interfaces (HMI), and SCADA systems are increasingly being connected to the cloud by vendors promising enhanced analytics and insights for predictive and preventative maintenance. However, strict quarantine policies surrounding critical assets mean that power companies cannot utilise these new IoT features offered by machine and controller vendors.
2. With the ever-decreasing cost of solar and wind power microgrids, utility companies will soon experience a decline in revenue from power generation. To offset this loss, companies must aggressively pursue new revenue streams, such as home energy management as a service, energy storage as a service, EV charging grid services, and peer-to-peer (P2P) energy trading between homes, microgrids, and batteries. All of this requires facilitation through smart metering, smart grids, and secure transactions, which are only possible via Distributed Ledger Technology (DLT) like IOTA. Additionally, utilities are exploring the provision of smart city services to city authorities.
3. For critical infrastructure such as dams, the International Committee on Large Dams (ICOLD) requires real-time Structural Health Monitoring (SHM). This ensures that any impending danger of dam, rock, or tunnel collapse can be detected in advance, allowing for the timely evacuation of affected populations.
4. A new emerging revenue area is EV charging in parking facilities. The question remains: how can IoT facilitate smart charging and smart parking?

Over the past three years, IoT engineering has undergone massive changes, primarily driven by Microsoft, Google, and Amazon. These industry giants have invested billions of dollars to develop IoT platforms that are easier to manage and more secure. Furthermore, IoT edge computing has gained significant momentum in both research and deployment as the only viable means for practical IoT implementation. With 5G promising to transform the IoT business landscape, an unprecedented amount of research funding has been directed towards new IoT areas. Consequently, it is now absolutely essential for any practicing engineer to understand the IoT platforms developed by major players such as AWS, Google, and especially Microsoft.

However, none of the platforms mentioned above offer a completely exhaustive or comprehensive solution for scalable IoT. Merely deploying smart metering to millions of homes requires additional technology to secure the smart meters, radio networks, IoT management systems, and various other secured services. The strategy, pricing, and security of any IoT deployment must be optimal and acceptable. Given the sheer amount of interdisciplinary knowledge required, it is nearly impossible for any single 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 methods for deploying IoT in next-generation power utility businesses.

In addition, with scalable deployment, managing IoT services for thousands of sensors and connections is emerging as a distinct engineering discipline. Formally known as managed IoT services, this area is experiencing rapid growth, as the challenges of scaling IoT are far greater than simply building the systems. This includes ensuring the security of over-the-top firmware and software updates, managing sensor and system calibration, performing auto-diagnosis of connection issues, identifying the root causes of API failures, and tracking the hardware and service health of distributed systems.

Course objectives

The main objective of this course is to introduce emerging technological options, platforms, and case studies of IoT implementation in power utility companies, covering smart metering, smart vehicles, SHM (Structural Health Monitoring), power quality diagnosis, and smart contracts. It provides a basic introduction to all elements of IoT, including mechanical systems, electronic and sensor platforms, wireless and wireline protocols, mobile-to-electronics integration, mobile-to-enterprise integration, 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 Centralised versus Decentralised IoT.
2. The IoT ecosystem for business, third-party device management, and risk management across the entire IoT ecosystem.
3. M2M wireless protocols for IoT: WiFi, SigFox, LORA, LPWAN, Zigbee/Zwave, Bluetooth, and ANT+; guidance on when and where to use each protocol.
4. Fundamentals of IoT Gateways: Risks, management, and ecosystem considerations.
5. Mobile, desktop, and web applications for registration, data acquisition, and control, including available M2M data acquisition platforms for IoT such as AWS IoT, Azure IoT, and Google IoT.
6. Security issues and solutions for IoT, including a review of security across all technology stacks.
7. Enterprise IoT platforms such as Microsoft Azure IoT Suite, AWS IoT, Google IoT, and Siemens MindSphere.
8. Smart metering, Open Smart Grid Protocols (OSGP), ANSI C12.18 Protocols, NIST standards for HAN (Home Area Network), HomePlug Powerline Alliance, and security standards 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 vehicle charging.
10. IoT for critical infrastructure, including dams, transformers, substations, and high-tension wires.

This instructor-led, live training in New Zealand (online or onsite) is designed for advanced-level IoT developers and smart home enthusiasts who want 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 such as MQTT and REST APIs within n8n workflows.
• Monitor, troubleshoot, and optimise IoT automation workflows.

In this instructor-led, live training in New Zealand, participants will learn how to maximise Nginx performance while setting up, configuring, monitoring, and troubleshooting the system to handle various forms of HTTP and TCP traffic. Key topics include configuring the most critical parameters in Nginx, the operating system, and virtual machines to extract maximum value from the platform.

The aim of this training is to clarify what Smart solutions are—and what they are not—including technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Blockchain, Virtual Reality (VR), and the Metaverse. We will explore the advantages and disadvantages of these emerging technological domains.

This instructor-led, live training in New Zealand (online or on-site) is designed for 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.
• Optimise TinyML models for efficient power and memory usage.