Machine-to-Machine (M2M) Training Course

This advanced course delves into the innovative realm of edge computing, providing a comprehensive exploration of sophisticated architectures and addressing integration challenges. Participants will gain the necessary skills to leverage the full potential of edge computing in various government environments. The curriculum focuses on developing expertise in cutting-edge tools and methodologies to deploy, manage, and optimize edge computing solutions that meet specific public sector needs for government.

Advances in technology and the proliferation of information are transforming how business is conducted across various industries, including operations for government. The rate of data generation and digital archiving within government agencies is increasing due to the rapid adoption of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As the volume and complexity of digital information grow, the challenges related to information management, processing, storage, security, and disposition also become more intricate. New capture, search, discovery, and analysis tools are enabling organizations to derive valuable insights from their unstructured data. The government market is at a critical juncture, recognizing that information is a strategic asset. Government entities need to protect, leverage, and analyze both structured and unstructured information to better serve the public and meet mission requirements. As leaders in government strive to evolve into data-driven organizations, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value solutions for government will be created through 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 of the intelligent industry solutions that enable government to make better decisions by acting on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

Achieving these outcomes requires more than just accumulating vast amounts of data. "Making sense of these volumes of Big Data necessitates cutting-edge tools and technologies that can analyze and extract useful knowledge from diverse streams of information," according to Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy.

The White House took a significant step in 2012 by establishing the National Big Data Research and Development Initiative. This initiative allocated more than $200 million to capitalize on the explosion of Big Data and the tools needed for its analysis.

The challenges posed by Big Data are as formidable as its promise is encouraging. Efficient data storage is one such challenge. Budget constraints require agencies to minimize per-megabyte storage costs while ensuring easy access to data for users. Backing up massive quantities of data further complicates this issue.

Effective data analysis presents another major challenge. Many agencies use commercial tools to sift through large datasets, 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 analyze their data effectively. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other government entities. This system has aided medical researchers in identifying a link that can alert doctors to aortic aneurysms before they occur. It is also used for more routine tasks, such as filtering through resumes to connect job candidates with hiring managers.

Insurtech, also known as Digital Insurance, represents the integration of insurance with emerging technologies. In the realm of Insurtech, "digital insurers" leverage technological advancements to optimize their business and operational models, thereby reducing costs, enhancing customer experiences, and increasing operational agility.

This instructor-led training is designed to provide participants with a comprehensive understanding of the technologies, methodologies, and strategic mindset necessary for driving digital transformation within their organizations and across the industry. The training is specifically aimed at managers who need to grasp the broader implications, dispel common misconceptions, and initiate the development of an Insurtech strategy for government.

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

• Articulate the concept of Insurtech and its various components with clarity and precision
• Clearly define the role of each key technology within the Insurtech framework
• Develop a strategic plan for implementing Insurtech within their organization

Audience

• Insurance providers
• Technologists in the insurance sector
• Stakeholders in the insurance industry
• Consultants and business analysts

Format of the Course

• A combination of lectures, discussions, exercises, and group activities focused on case studies

This instructor-led, live training in US (online or onsite) is designed for intermediate-level IT professionals and business managers who seek to understand the potential of IoT and edge computing for enhancing efficiency, enabling real-time processing, and fostering innovation across various industries.

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

• Comprehend the principles of IoT and edge computing and their significance in digital transformation for government and private sectors.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors, including applications for government operations.
• Differentiate between edge and cloud computing architectures and deployment scenarios suitable for government and enterprise environments.
• Implement edge computing solutions to support predictive maintenance and real-time decision-making in both public and private sector contexts.

This comprehensive course integrates the transformative power of artificial intelligence (AI) with the agility of edge computing. Participants will learn to deploy AI models directly on edge devices, covering topics from understanding convolutional neural network (CNN) architectures to mastering knowledge distillation and federated learning. This hands-on training is designed to equip professionals with the skills necessary to optimize AI performance for real-time processing and decision-making at the edge, ensuring alignment with public sector workflows and governance for government applications.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers, system architects, and industry professionals who aim to leverage Edge AI to enhance IoT applications with advanced data processing and analytics capabilities.

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

• Comprehend the core principles of Edge AI and its application in IoT systems.
• Establish and configure Edge AI environments for IoT devices.
• Create and deploy AI models on edge devices to support IoT applications.
• Execute real-time data processing and decision-making within IoT frameworks.
• Integrate Edge AI with a variety of IoT protocols and platforms.
• Address ethical considerations and best practices in the deployment of Edge AI for government and industry use cases.

This instructor-led, live training (online or onsite) is aimed at product managers and developers who wish to utilize Edge Computing to decentralize data management for enhanced performance, leveraging smart devices located at the source network. This training aligns with public sector workflows and governance practices for government. By the end of this training, participants will be able to: - Understand the fundamental concepts and benefits of Edge Computing. - Identify use cases and examples where Edge Computing can be effectively applied. - Design and implement Edge Computing solutions to accelerate data processing and reduce operational costs.

Establish a robust foundation in designing and managing a resilient edge computing infrastructure for government. Gain insights into open hybrid cloud infrastructures, the management of workloads across multiple clouds, and the importance of flexibility and redundancy. This training offers critical knowledge on developing a scalable and secure infrastructure that supports the evolving needs of modern applications through edge computing.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who wish to apply Federated Learning to enhance IoT and edge computing solutions for government. By the end of this training, participants will be able to: - Understand the principles and benefits of Federated Learning in IoT and edge computing environments. - Implement Federated Learning models on IoT devices to enable decentralized AI processing. - Reduce latency and improve real-time decision-making capabilities in edge computing scenarios. - Address challenges related to data privacy and network constraints in government IoT systems.

In this instructor-led, live training for government participants in [location], attendees will gain a foundational understanding of the Internet of Things (IoT) as they work through the development of an Arduino-based IoT sensor system. By the end of this training, participants will be able to: - Comprehend the core principles of IoT, including its components and communication methods. - Utilize various Arduino communication modules suitable for different IoT applications. - Develop and program a mobile application to control Arduino devices. - Integrate a Wi-Fi module to establish connectivity between Arduino and other devices. - Construct and deploy their own IoT Sensor System tailored for government use.

Unlike other technologies, the Internet of Things (IoT) is far more complex, encompassing a broad range of core engineering disciplines, including Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics, and Mobile. Each layer of IoT engineering involves various aspects of economics, standards, regulations, and the evolving state of the art. For government and other sectors, this advanced course offers a comprehensive overview of these critical IoT engineering aspects.

Summary

An advanced training program covering the current state of the art in Internet of Things (IoT) technology. This program cuts across multiple technology domains to develop awareness of an IoT system and its components, highlighting how it can benefit businesses and organizations.

The course includes live demonstrations of model IoT applications, showcasing practical IoT deployments across different industry sectors such as Industrial IoT, Smart Cities, Retail, Travel & Transportation, and use cases involving connected devices and things.

Target Audience

This training is designed for managers responsible for business and operational processes within their respective organizations who want to understand how to leverage IoT to enhance system and process efficiency. It is also suitable for entrepreneurs and investors looking to build new ventures and seeking a better understanding of the IoT technology landscape to effectively utilize it.

Estimates for the value of the Internet of Things (IoT) market are substantial, given that IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays consumer, business-to-business, and government industries. The number of IoT connections is expected to grow from 1.9 billion today to 9 billion by 2018, roughly equaling the combined total of smartphones, smart TVs, tablets, wearable computers, and PCs.

In the consumer space, many products and services have already integrated into the IoT, including kitchen and home appliances, parking solutions, RFID systems, lighting and heating products, and various applications in the Industrial Internet. While the underlying technologies of IoT are not new, as machine-to-machine (M2M) communication has existed since the birth of the internet, recent advancements include the emergence of inexpensive wireless technologies and widespread adoption of smartphones and tablets. The explosive growth of mobile devices has driven the current demand for IoT.

Due to the vast opportunities in the IoT business, a significant number of small and medium-sized enterprises have entered the market. The availability of open-source electronics and IoT platforms has made the development and management of IoT systems more affordable. Existing electronic product owners are also under pressure to integrate their devices with the internet or mobile apps.

The primary objective of this training is to provide a technology and business review of the emerging IoT industry, enabling enthusiasts and entrepreneurs to grasp the basics of IoT technology and its business applications.

Course Objective

The main goal of the course is to introduce participants to emerging technological options, platforms, and case studies of IoT implementation in various sectors such as home and city automation (smart homes and cities), Industrial Internet, healthcare, government, mobile cellular networks, and other areas.

Key topics covered include:

• A basic introduction to all elements of IoT: Mechanical, Electronics/sensor platforms, wireless and wireline protocols, mobile-to-electronics integration, mobile-to-enterprise integration, data analytics, and total control plane.
• M2M wireless protocols for IoT, including WiFi, Zigbee/Zwave, Bluetooth, ANT+: Understanding when and where to use each protocol.
• Mobile/Desktop/Web app development for registration, data acquisition, and control, along with available M2M data acquisition platforms like Xively, Omega, and NovoTech.
• Security issues and solutions specific to IoT.
• Open-source and commercial electronics platforms for IoT, such as Raspberry Pi, Arduino, and ArmMbedLPC.
• Open-source and commercial enterprise cloud platforms for IoT, including AWS-IoT apps, Azure-IOT, Watson-IOT cloud, and other minor IoT clouds.
• Case studies of business and technology in common IoT devices like home automation systems, smoke alarms, vehicles, military applications, and home health solutions.

Unlike other technologies, the Internet of Things (IoT) is notably more complex, encompassing a wide range of engineering disciplines such as Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics, and Mobile. Each layer of IoT engineering involves aspects of economics, standards, regulations, and the evolving state of the art. For government and other sectors, this course provides a comprehensive overview of these critical aspects of IoT Engineering.

For professionals in manufacturing, understanding advancements in the Industrial Internet of Things (IIoT) is crucial. This includes predictive and preventative maintenance, condition-based monitoring of machines, production optimization, energy management, supply-chain efficiency, and ensuring the uptime of manufacturing utilities.

Summary

• An advanced training program covering the latest advancements in IoT for smart factories.
• Crosses multiple technology domains to develop awareness of an IoT system and its components, and how it can benefit manufacturing managerial professionals.
• Live demonstrations of model IIoT applications for smart factories.

Target Audience

• Managers responsible for business and operational processes within their respective manufacturing organizations who wish to understand how to leverage IoT to enhance system and process efficiency.

Duration 3 Days (8 hours/day)

Estimates of the market value for Internet of Things (IoT) are substantial, as by definition, IoT is an integrated and pervasive layer of devices, sensors, and computing power that spans consumer, business-to-business, and government industries. By 2018, the number of IoT connections was projected to reach 9 billion, roughly equivalent to the combined total of smartphones, smart TVs, tablets, wearable computers, and PCs.

In the consumer sector, numerous products and services have already transitioned into the IoT domain, including kitchen and home appliances, parking solutions, RFID systems, lighting and heating controls, and various Industrial Internet applications.

While the underlying technologies of IoT are not new—machine-to-machine (M2M) communication has existed since the inception of the internet—the recent surge in inexpensive wireless technologies and the widespread adoption of smartphones and tablets have driven current demand for IoT. The rapid growth of mobile devices has significantly contributed to the present demand for IoT.

Industrial IoT, or IIoT for manufacturing, has been widely adopted since 2014, with numerous innovations occurring in this field. This course will introduce all important aspects of these innovations in the Industrial IoT area.

The training aims to provide a technology and business overview of an emerging industry, enabling IoT enthusiasts and entrepreneurs to grasp the basics of IoT technology and business.

Course Objective

The primary objective of this course is to introduce emerging technological options, platforms, and case studies of IoT implementation in smart factories for manufacturing sectors.

1. Analysis of the business and technology aspects of common IIoT platforms like Siemens MindSphere and Azure IoT.
2. Exploration of open-source and commercial enterprise cloud platforms such as AWS-IoT, Azure-IoT, Watson-IoT, Mindsphere IIoT cloud, and other minor IoT clouds.
3. Overview of open-source and commercial electronics platforms for IoT, including Raspberry Pi, Arduino, and ArmMbedLPC.
4. Discussion of security issues and solutions for IIoT.
5. Development of mobile/desktop/web applications for registration, data acquisition, and control.
6. Examination of M2M wireless protocols for IoT, including WiFi, LoPan, BLE, Ethernet, Ethercat, PLC, with guidance on their appropriate use.
7. Basic introduction to all elements of IoT, covering mechanical, electronics/sensor platforms, wireless and wireline protocols, mobile-to-electronics integration, mobile-to-enterprise integration, data analytics, and total control plane.

The Internet of Things (IoT) is a network infrastructure that connects physical devices and software applications wirelessly, enabling them to communicate and exchange data through network communications, cloud computing, and data capture.

In this instructor-led, live training, participants will gain a foundational understanding of IoT as they work through the process of creating an IoT sensor system using the Raspberry Pi.

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

• Understand the principles of IoT, including its components and communication techniques
• Learn how to configure the Raspberry Pi for IoT applications
• Design and deploy their own IoT Sensor System

Audience

• Hobbyists
• Hardware and software engineers and technicians
• Technical professionals from various industries
• Beginner developers

Format of the Course

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

Note

• The Raspberry Pi supports multiple operating systems and programming languages. This course will use the Linux-based Raspbian as the operating system and Python as the programming language. To request a different setup, please contact us to arrange.
• Participants are responsible for purchasing the necessary Raspberry Pi hardware and components.

This training is designed to enhance technical skills and align with the needs of public sector workflows, governance, and accountability for government applications.

In this instructor-led, live training, participants will gain a comprehensive understanding of NB-IoT (also known as LTE Cat NB1) and develop and deploy a sample NB-IoT-based application. This training is designed to align with the needs and standards for government. By the end of this training, participants will be able to: - Identify the various components of NB-IoT and understand how they integrate to form an ecosystem. - Understand and articulate the security features inherent in NB-IoT devices. - Develop a simple application to track NB-IoT devices, enhancing operational efficiency and accountability for government.

ThingsBoard is an open-source IoT platform designed to offer comprehensive device management, data collection, processing, and visualization capabilities for your IoT solutions.

In this instructor-led, live training, participants will learn how to integrate ThingsBoard into their IoT projects for government use.

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

• Install and configure ThingsBoard
• Understand the core features and architecture of ThingsBoard
• Develop IoT applications using ThingsBoard
• Integrate ThingsBoard with Kafka for efficient telemetry data routing from devices
• Integrate ThingsBoard with Apache Spark to aggregate and analyze data from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Format of the Course

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

Note

• To request a customized training for government use, please contact us to arrange.