Smart solutions for HR Training Course

The primary goal of this training is to clarify what the 5G network entails and its influence on smart technologies. The focus is on illustrating both the benefits and drawbacks of this technological synergy (5G / IoT), while also highlighting the developmental trajectory of a network inherently designed for the smart ecosystem from its inception.

6G represents the upcoming generation of wireless communication standards, set to revolutionize IoT ecosystems by offering ultra-fast connectivity, sophisticated sensing, and integrated artificial intelligence capabilities.

This instructor-led, live training (available online or onsite) is designed for advanced-level participants seeking to comprehend and exploit the emerging synergy between 6G technologies and IoT applications.

Upon completing this course, learners will be able to:

• Articulate the core technical concepts underpinning 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 fundamentally transforming business operations across numerous industries, including the public sector. The rate of government data generation and digital archiving is accelerating, driven by the rapid proliferation of mobile devices and applications, smart sensors and IoT devices, cloud computing solutions, and citizen-facing digital portals. As digital information expands in volume and complexity, the challenges associated with information management, processing, storage, security, and disposition become increasingly sophisticated. New tools for capture, search, discovery, and analysis are enabling organizations to derive valuable insights from unstructured data. The government sector is reaching a tipping point, recognizing information as a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured data to better serve citizens and meet mission requirements. As government leaders strive to evolve into data-driven organizations, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions are emerging 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 is one such intelligent industry solution that empowers government entities to make better decisions by taking action based on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these capabilities requires 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 noted in a post on the OSTP Blog.

The White House took a significant step toward helping agencies identify 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 necessary to analyze it.

The challenges posed by Big Data are nearly as daunting as its promise is encouraging. One significant challenge is storing data efficiently. With budgets often 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. Additionally, backing up massive amounts of data compounds this challenge.

Effectively analyzing data is another major hurdle. Many agencies utilize commercial tools to sift through vast amounts of data, identifying trends that enhance operational efficiency. (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 allowing 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 more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

This instructor-led, live training in Botswana (online or on-site) 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 Botswana (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:

• 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 Botswana (online or onsite) is aimed at product managers and developers who wish to use Edge Computing to decentralize 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 engineered to execute specific tasks within broader systems. The Internet of Things (IoT) refers to a network of physical devices embedded with sensors and software, enabling them to communicate and share data over the internet.

This instructor-led live training, available either online or on-site, is designed for beginner-level technical professionals aiming 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 components of embedded systems.
• Write and compile C code to interact with embedded hardware.
• Utilize microcontroller peripherals such as timers and Analog-to-Digital Converters (ADCs).
• Understand the role of embedded systems within IoT architectures.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation in a live laboratory environment.

Course Customization Options

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

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

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

• Grasp 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.

The 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) serves as a network infrastructure that wirelessly links physical objects and software applications, enabling them to communicate and exchange data through network communications, cloud computing, and data capture. Java, a general-purpose language celebrated 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 learn how to develop IoT solutions using Java.

Upon completing this training, participants will be able to:

• Install and configure tools and frameworks (Eclipse Open IoT Stack) for programming IoT systems with Java
• Grasp the fundamentals 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 blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• For a customised training request, please contact us to make arrangements.

Connected devices are disrupting numerous businesses, and the power utility sector is no exception. Power utility companies currently face four primary challenges arising from the growth of the Internet of Things (IoT).

1. Vendors are increasingly connecting machines, controllers, Human-Machine Interfaces (HMI), and SCADA systems to the cloud, promising enhanced analytics and insights through their data for predictive and preventative maintenance. However, due to the strict quarantine policies surrounding critical assets, power companies are often unable to leverage these new IoT features provided by machine and controller vendors.
2. As the costs of solar and wind power microgrids continue to decrease, utility companies anticipate declining revenue from traditional 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, grid services for electric vehicle (EV) charging, and grid services facilitating Peer-to-Peer (P2P) energy trading between homes, between homes and microgrids, between microgrids, between microgrids and batteries, and between homes and batteries. All these initiatives require smart metering, smart grids, and secure transactions, which are only feasible through Distributed Ledger Technology (DLT) such as IOTA. Furthermore, utilities are exploring opportunities to provide certain smart city services to municipal authorities.
3. For critical infrastructure such as dams, the International Committee of Large Dams (ICOLD) mandates real-time Structural Health Monitoring (SHM). This allows for the early detection of impending dangers like dam, rock, or tunnel collapse, enabling authorities to evacuate potentially affected populations in advance.
4. Additionally, EV charging in parking facilities represents an emerging revenue area. The key question is how IoT can facilitate both smart charging and smart parking solutions.

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

However, none of these major platforms offer an exhaustive or entirely comprehensive solution for scalable IoT. For instance, deploying smart metering to millions of homes requires additional technologies to secure the smart meters, manage radio networks, handle IoT management technology, and provide numerous other secured services. The strategy, pricing, and security of any IoT deployment must be optimal and acceptable. Given the vast 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 on the challenges, risks, and practical methods for deploying IoT to support their next-generation power utility businesses.

Furthermore, with scalable deployments, managing IoT services for thousands of sensors and connections is emerging as a distinct engineering discipline. 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 securing over-the-top firmware and 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 this 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 systems, electronics and 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, and 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/Z-Wave, Bluetooth, ANT+ : Guidance on 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: A review of the security of all technology stacks.
7. Enterprise IoT platforms such as Microsoft Azure IoT suites, AWS IoT, Google IoT, and 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, and the Security Standard 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 car charging.
10. IoT applications for critical infrastructure like dams, transformers, sub-stations, and high-tension wires.

This instructor-led, live training in Botswana (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 Botswana (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 Botswana, participants will learn how to maximise Nginx performance by setting up, configuring, monitoring, and troubleshooting it to manage various forms of HTTP and TCP traffic. Topics include configuring the most important Nginx parameters, the operating system, and virtual machines to gain maximum value from Nginx.

This instructor-led, live training in Botswana (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.