Smart solutions for HR Training Course

The objective of this training is to elucidate the nature of 5G networks and their influence on smart technologies. We aim to demonstrate the pros and cons of the relationship between these technologies (5G and IoT) and outline the development trajectory of a network designed from its inception for the smart world.

6G represents the next generation of wireless communication standards, poised to revolutionize 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 seeking 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 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 organizational technology roadmap, please contact us to arrange.

Technological advancements and the exponential growth of information are reshaping business operations across various sectors, including the government sector. Government data generation and digital archiving are accelerating due to the surge in mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information expands and grows in complexity, managing, processing, storing, securing, and disposing of this data becomes increasingly challenging. New tools for capturing, searching, discovering, and analyzing data are enabling organizations to extract valuable insights from unstructured data. The government sector is at a critical juncture, recognizing that information is a strategic asset. Governments must protect, leverage, and analyze both structured and unstructured information to better serve the public and meet mission objectives. As government leaders strive to evolve into data-driven organizations to successfully accomplish their missions, they are establishing the foundation to correlate dependencies across events, people, processes, and information.

High-impact government solutions will emerge from integrating the most disruptive technologies:

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

Big Data represents a key intelligent industry solution, enabling governments to make better decisions by acting on patterns revealed through the analysis of large volumes of data—both related and unrelated, structured and unstructured.

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

The White House took steps to assist agencies in finding these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximize the potential of the Big Data explosion and the tools required to analyze it.

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

Effectively analyzing data is another major challenge. Many agencies use commercial tools to sift through vast amounts of data, identifying trends that help them operate more efficiently. A recent MeriTalk study found that federal IT executives believe Big Data could help agencies save over $500 billion while also fulfilling mission objectives.

Custom-developed Big Data tools are also enabling agencies to address 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. This system has helped medical researchers identify links that can alert doctors to aortic aneurysms before they occur. It is also used for routine tasks, such as screening resumes to connect job candidates with hiring managers.

This instructor-led, live training in Malaysia (online or onsite) targets intermediate-level IT professionals and business managers who wish to understand how IoT and edge computing can drive efficiency, real-time processing, and 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 in 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 Malaysia (online or onsite) is designed for intermediate-level developers, system architects, and industry professionals looking to utilise Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

By the conclusion 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 Malaysia (online or onsite) is tailored for product managers and developers who intend to utilise Edge Computing to decentralise data management, enhancing performance by leveraging smart devices at the network source.

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

• Understand the core 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 specialized computing solutions engineered to carry out specific tasks within broader operational environments. The Internet of Things (IoT) refers to a vast network of physical devices integrated with sensors and software, enabling them to connect and share data across the internet.

This instructor-led training session, available both online and on-site, is designed for technical professionals at the beginner level who aim to grasp and implement embedded systems and IoT principles using C programming and microcontroller architectures.

Upon completing this training, participants will be equipped to:

• Comprehend the architecture and key components of embedded systems.
• Develop and compile C code to facilitate interaction with embedded hardware.
• Utilize microcontroller peripherals, including timers and Analog-to-Digital Converters (ADCs).
• Grasp the role of embedded systems within IoT architectures.

Course Format

• Engaging lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For those interested in a tailored training experience, please reach out to us to make arrangements.

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level professionals who wish to apply Federated Learning to optimise IoT and edge computing solutions.

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

• Understand the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

Internet of Things (IoT) is a network infrastructure that connects physical objects and software applications wirelessly, allowing them to communicate with each other and exchange data via network communications, cloud computing, and data capture. C is a general purpose programming language recommended for IoT due to its ubiquity and low-level programming benefits.

In this instructor-led, live training, participants will learn how to program IoT solutions with 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

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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

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

In this instructor-led live training, participants will gain the skills needed to program IoT solutions using Java.

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

• Install and set up tools and frameworks (Eclipse Open IoT Stack) for developing IoT systems with Java
• Grasp the core 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

• Software Developers
• Engineers

Course Format

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

Note

• For those seeking customised training for this course, please reach out to us to make arrangements.

The rise of connected devices is transforming numerous industries, with the power utility sector facing significant changes. Utility companies are confronting four key challenges driven by the growth of IoT:

1. Vendors are increasingly connecting machines, controllers, HMIs, and SCADA systems to the cloud, promising enhanced analytics and insights for predictive and preventative maintenance. However, strict quarantine policies governing critical assets prevent power companies from fully utilizing these new IoT features provided by machine and controller vendors.
2. As the cost of solar and wind power microgrids continues to decline, utility companies will experience falling revenue from power generation. To offset this loss, companies must aggressively pursue new revenue streams, such as energy management for homes as a service, energy storage as a service, grid services for EV charging, and grid services for peer-to-peer (P2P) energy trading among homes, between homes and microgrids, between microgrids, between microgrids and batteries, and between homes and batteries. All these initiatives require facilitation through smart metering, smart grids, and secure transactions enabled by Distributed Ledger Technology (DLT) like IOTA. Additionally, utilities are exploring ways to offer smart city services to municipal authorities.
3. For critical infrastructure such as dams, ICOLD (International Committee of Large Dams) mandates real-time Structural Health Monitoring (SHM) to detect any potential collapse of dams, rock formations, or tunnels. This allows for early warnings to evacuate people who might be affected.
4. Another emerging revenue area is EV charging in parking facilities. A key question arises: 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. IoT edge computing has gained significant momentum in both research and deployment as the primary means for practical IoT implementation. Furthermore, 5G is poised to transform the IoT business landscape. This has led to unprecedented funding for IoT research. Consequently, it is essential for practicing engineers to understand the IoT platforms developed by major players like AWS, Google, and especially Microsoft.

However, none of these platforms offer a completely exhaustive or comprehensive solution for scalable IoT. Deploying smart metering to millions of homes requires additional technologies to secure the smart meters, radio networks, IoT management technology, and other secured services. The strategy, pricing, and security of any IoT deployment must be optimal and acceptable. Given the extensive 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 on the challenges, risks, and practical approaches to deploying IoT for next-generation power utility businesses.

Additionally, with scalable deployment, managing IoT services for thousands of sensors and connections has emerged as a distinct engineering research subject. This area, formerly known as managed IoT services, is experiencing rapid growth because the challenges of scalable IoT are far greater than merely building them. This includes security for over-the-top firmware/software updates, managing sensor and system calibration, auto-diagnosing connection issues, pinpointing the root cause 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, covering Smart Metering, Smart Cars, SHM (Structural Health Monitoring), Power Quality Diagnosis, and Smart Contracts. It provides a basic introduction to all elements of IoT, including Mechanical, Electronics/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 & Mobile Applications for IoT, Centralized vs Decentralized IoT
2. IoT ecosystem for Business, third-party device management, risk management of the entire IoT ecosystem
3. M2M Wireless protocols for IoT - WiFi, SigFox, LORA, LPWAN, Zigbee/Zwave, Bluetooth, ANT+: When and where to use each one
4. Fundamentals of IoT Gateways - Risks, Management and Ecosystem
5. Mobile/Desktop/Web apps for registration, data acquisition, and control – Available M2M data acquisition platforms for IoT—AWS IoT, Azure IoT, Google IoT
6. Security issues and solutions for IoT - Review of security across all technology stacks
7. Enterprise IoT platforms such as Microsoft Azure IoT suites, AWS IoT, Google IoT, Siemens MindSphere
8. Smart Metering, Open Smart Grid Protocols (OSGP), ANSI C 2.18 Protocols, NIST Standard for HAN (Home Area Network), Home Plug Powerline Alliance, Security Standard for Smart Meter - IEC 62056
9. Distributed Ledger Technology (DLT) such as Blockchain, HyperLedger, and DAG (Directed Acyclic Graph) for smart contracts, P2P transactions, smart car charging
10. IoT for critical infrastructure like DAMs, Transformers, Sub-stations, High Tension Wires

This instructor-led, live training in Malaysia (online or onsite) is aimed at 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 Malaysia (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 optimize IoT automation workflows.

In this instructor-led live training conducted in Malaysia, participants will learn to maximise Nginx's performance by setting up, configuring, monitoring, and troubleshooting it for handling various forms of HTTP and TCP traffic. The curriculum covers configuring critical Nginx parameters, alongside optimising the operating system and virtual machine environments to extract maximum value from Nginx.

This instructor-led, live training in Malaysia (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.