Smart solutions for HR Training Course

This training aims to clarify what the 5G network is and how it influences smart technologies. It will demonstrate both the benefits and drawbacks of the relationship between 5G and IoT, while also outlining the development trajectory of a network designed from its inception for the smart world.

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

This instructor-led live training, available online or onsite, targets advanced-level participants aiming 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.

Course Format

• 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.

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 practices across numerous sectors, including the government sector. The rates of government data generation and digital archiving are climbing rapidly, driven by the proliferation of mobile devices and applications, smart sensors and gadgets, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes increasingly complex, the management, processing, storage, security, and disposition of data also grow more intricate. New tools for capture, search, discovery, and analysis are enabling organizations to extract valuable insights from unstructured data. The government sector is at a critical juncture, recognizing information as a strategic asset. Governments must now 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 that successfully achieve their missions, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

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

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

Big Data serves as an intelligent industry solution that empowers the government to make superior decisions by acting on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these results 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 analyze and extract useful knowledge from vast and diverse streams of information," Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog.

The White House took a significant step toward helping agencies locate these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to capitalize on the explosion of Big Data and the tools necessary to analyze 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 minimize the cost per megabyte of storage while keeping data 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 hurdle. Many agencies utilize commercial tools that allow them to sift through mountains of data, identifying trends that can enhance operational efficiency. (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 enabling agencies to address their need to analyze data. For instance, 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 more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

This instructor-led, live training in Nigeria (online or onsite) is designed for intermediate-level IT professionals and business managers who wish to understand the potential of IoT and edge computing for enabling efficiency, real-time processing, and innovation in 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 Nigeria (available online or onsite) targets intermediate-level developers, system architects, and industry professionals who wish to enhance IoT applications with intelligent data processing and analytics capabilities using Edge AI.

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 Nigeria (online or onsite) is aimed at product managers and developers who wish to use Edge Computing to decentralise data management for faster performance, leveraging smart devices located on the source network.

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

• Understand the basic concepts and advantages of Edge Computing.
• Identify the 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 platforms created to carry out specific tasks within larger mechanical or electrical systems. The Internet of Things (IoT) refers to a vast network of physical devices equipped with sensors and software, enabling them to connect and exchange data over the internet.

This live, instructor-led training, available online or at your workplace, is tailored for technical beginners who want to grasp and apply the principles of embedded systems and IoT using C programming and microcontroller architectures.

Upon completing this training, participants will be equipped to:

• Comprehend the structure and components of embedded systems.
• Write and compile C code to facilitate interaction with embedded hardware.
• Utilize microcontroller peripherals like 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 in a live laboratory environment.

Course Customization Options

• For those interested in a customized version of this course, please reach out to us to make arrangements.

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

Upon completing this training, participants will be capable of:

• Grasping the principles and advantages of Federated Learning in IoT and edge computing.
• Deploying Federated Learning models on IoT devices to enable decentralized AI processing.
• Minimizing latency and enhancing real-time decision-making in edge computing settings.
• Tackling challenges associated with data privacy and network limitations in IoT systems.

The Internet of Things (IoT) constitutes a network infrastructure that wirelessly connects physical devices with 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 low-level programming advantages.

In this instructor-led live training, participants will acquire the skills necessary to program IoT solutions using C.

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

• Install and configure NetBeans for programming IoT systems with C
• Grasp the fundamentals of IoT architecture
• Understand the benefits of using C for 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) constitutes a network infrastructure that wirelessly interconnects physical objects with software applications, enabling seamless communication and data exchange through network protocols, cloud computing, and data capture mechanisms. Java, a versatile programming language renowned for its "write once, run anywhere" capability, is highly recommended for IoT development due to its efficiency and portability.

In this instructor-led live training, participants will gain practical skills in programming IoT solutions using Java.

Upon completing this training, participants will be capable of:

• Installing and configuring the necessary tools and frameworks, specifically the Eclipse Open IoT Stack, to develop IoT systems with Java
• Gaining a solid understanding of core IoT architecture principles
• Utilizing the Eclipse Open IoT Stack for Java to connect and manage devices within an IoT ecosystem
• Constructing, testing, and deploying complete IoT systems using Java

Target Audience

• Software Developers
• Engineers

Course Format

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

Note

• For those interested in customized training for this course, please reach out to us to make arrangements.

Connected devices are disrupting numerous business sectors, and the power utility industry is no exception. Power utility companies currently face four primary challenges stemming from the growth of IoT:

1. Vendors are increasingly connecting machines, controllers, HMIs, and SCADA systems to the cloud, promising enhanced analytics and insights via their data for predictive and preventative maintenance. However, the strict quarantine policies protecting critical assets prevent power companies from utilizing these new IoT features offered by machine and controller vendors.
2. With the rapidly decreasing cost 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, and microgrid-to-battery or home-to-battery exchanges. All these services require smart metering, smart grids, and secure transactions facilitated by Distributed Ledger Technology (DLT) like IOTA. Additionally, utilities are exploring opportunities to offer smart city services to local authorities.
3. For critical infrastructure like dams, the International Committee of Large Dams (ICOLD) demands real-time Structural Health Monitoring (SHM). This enables early warning of potential collapses in dams, rocks, or tunnels, allowing for the timely evacuation of affected populations.
4. Another emerging revenue area is EV charging in parking facilities, specifically exploring how IoT can facilitate smart charging and smart parking solutions.

Over the past three years, IoT engineering has undergone massive changes, largely driven by Microsoft, Google, and Amazon. These tech giants have invested billions 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 primary means for practical IoT implementation. Furthermore, 5G promises to transform the IoT business landscape, leading to unprecedented volumes of research funding. Consequently, for any practicing engineer, understanding the IoT platforms developed by major players like AWS, Google, and particularly Microsoft is essential.

However, none of these platforms offer a fully exhaustive or comprehensive solution for scalable IoT. Deploying smart metering to millions of homes, for instance, requires additional technologies to secure smart meters, radio networks, IoT management tools, and various other secured services. The strategy, pricing, and security of any IoT deployment must be optimized and acceptable. Given the interdisciplinary nature of this knowledge, 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, formally known as managed IoT services, is experiencing rapid growth because the challenges of scalable IoT extend far beyond its initial construction. These challenges include securing over-the-top firmware/software updates, managing sensor and system calibration, auto-diagnosing connection issues, identifying 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, SHM (Structural Health Monitoring), Power Quality Diagnosis, and Smart Contracts. It provides a basic introduction to all IoT elements: mechanical components, 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 & mobile applications for IoT, centralized vs decentralized IoT.
2. The 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/Zwave, Bluetooth, ANT+ — 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 — 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 C2.18 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 (Direct 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, conducted Nigeria (online or onsite), is tailored for developers and programmers who wish to install, configure, and manage the Kaa platform to develop 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 Nigeria (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 held in Nigeria, participants will learn how to maximize Nginx performance by setting up, configuring, monitoring, and troubleshooting it for handling various HTTP and TCP traffic. Topics include configuring key Nginx parameters and optimizing the OS and virtual machine settings to gain maximum value from Nginx.

This instructor-led, live training in Nigeria (online or onsite) targets intermediate-level IoT developers, embedded engineers, and AI practitioners who want 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.