IoT Programming with Java Training Course

Technological advancements and the exponential growth of information are reshaping business practices across various sectors, including government. The surge in mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals is driving a significant increase in data generation and digital archiving within government entities. As the volume and complexity of digital information expand, the challenges related to managing, processing, storing, securing, and disposing of this data become more intricate. However, new tools for capturing, searching, discovering, and analyzing unstructured data are enabling organizations to derive valuable insights. The government sector is at a critical juncture, recognizing that information is a strategic asset. To better serve the public and meet mission requirements, government agencies need to protect, leverage, and analyze both structured and unstructured information. As leaders strive to transform their organizations into data-driven entities, they are establishing frameworks to identify and correlate dependencies across events, people, processes, and information.

High-impact government solutions will emerge from the integration of the most transformative technologies:

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

Big Data is a key intelligent industry solution that enables government agencies to make informed decisions by analyzing large volumes of data—both structured and unstructured. By identifying patterns within these vast datasets, governments can take more effective actions.

However, achieving these outcomes requires more than just collecting massive amounts of data. "Interpreting such vast quantities of Big Data necessitates cutting-edge tools and technologies capable of analyzing and extracting meaningful insights from diverse streams of information," Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a blog post.

In 2012, the White House took a significant step toward helping agencies find these necessary tools by launching the National Big Data Research and Development Initiative. This initiative allocated over $200 million to capitalize on the surge in Big Data and develop the required analytical tools.

The challenges posed by Big Data are as formidable as its potential is promising. Efficient data storage is a major challenge, especially given budget constraints. Agencies must reduce the cost per megabyte of storage while ensuring easy access for users when and how they need it. Backing up large volumes of data further complicates this task.

Effective data analysis presents another significant challenge. Many agencies use commercial tools to sift through vast 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 achieving their mission objectives.

Custom-developed Big Data tools are also aiding agencies in analyzing their data. 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 a critical link that can alert doctors to potential aortic aneurysms before they occur. It is also used for more routine tasks, such as filtering through resumes to match job candidates with hiring managers.

Insurtech, or Digital Insurance, represents the integration of insurance with cutting-edge technologies. In this domain, "digital insurers" leverage technological innovations to streamline their business and operational models, aiming to cut costs, enhance customer experiences, and boost operational agility.

This instructor-led training equips participants with a comprehensive understanding of the technologies, methodologies, and mindset required for digital transformation within their organizations and across the industry. The training is designed for managers who need an overarching view to separate fact from hype, demystify jargon, and take initial steps in formulating an Insurtech strategy.

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

• Engage in intelligent and systematic discussions about Insurtech and its various components.
• Clearly identify and understand the role of each key technology within Insurtech.
• Develop a general strategy for implementing Insurtech within their organization.

Audience

• Insurance companies
• Technologists in the insurance sector
• Insurance stakeholders
• Consultants and business analysts

Format of the course

• A combination of lectures, discussions, exercises, and group activities centered around case studies.

This instructor-led, live training in Taiwan (online or onsite) is aimed at 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 Taiwan (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 Taiwan (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.

This instructor-led, live training in Taiwan (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:

• 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.

In this instructor-led, live training in Taiwan, participants will learn the fundamentals of IoT as they step through the creation of an Arduino-based IoT sensor system.

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

• Understand the principles of IoT, including IoT components and communication techniques.
• Learn how to use Arduino communication modules that can be used for different IoT systems.
• Learn how to use and program a mobile app to control Arduino.
• Use a Wi-Fi module to connect the Arduino to another device.
• Build and deploy their own IoT Sensor System.

Unlike other technologies, the Internet of Things (IoT) is far more intricate, encompassing nearly every branch of core engineering—mechanical, electronics, firmware, middleware, cloud, analytics, and mobile. Each layer of IoT involves various aspects of economics, standards, regulations, and evolving state-of-the-art advancements. This course is a comprehensive introduction to the critical aspects of IoT Engineering, offering a unique and detailed exploration of this multifaceted technology.

Summary

An advanced training program covering the latest developments in Internet of Things (IoT)

This course spans multiple technological domains to provide an understanding of IoT systems and their components, as well as how they can benefit businesses and organizations.

Live demonstrations of model IoT applications will showcase practical deployments across various industry sectors, such as Industrial IoT, Smart Cities, Retail, Travel & Transportation, and use cases involving connected devices and things.

Target Audience

This course is designed for managers responsible for business and operational processes within their organizations who want to learn how to leverage IoT to enhance the efficiency of their systems and processes.

Entrepreneurs and investors interested in building new ventures will gain a deeper understanding of the IoT technology landscape, enabling them to effectively leverage it in their projects.

The market value estimates 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 is projected to reach 9 billion, roughly equivalent to the combined total of smartphones, smart TVs, tablets, wearable computers, and PCs.

In the consumer market, numerous products and services have already integrated with the IoT, including kitchen and home appliances, parking systems, RFID, lighting and heating solutions, and various applications in the Industrial Internet.

While the underlying technologies of IoT are not new—machine-to-machine (M2M) communication has existed since the inception of the internet—the recent emergence of inexpensive wireless technologies and the widespread adoption of smartphones and tablets have driven the explosive growth of mobile devices, leading to the current demand for IoT solutions.

The vast opportunities in the IoT business have attracted a large number of small and medium-sized entrepreneurs. The availability of open-source electronics and IoT platforms has made it increasingly affordable to develop and manage IoT systems on a significant scale. Existing electronic product manufacturers are under pressure to integrate their devices with the internet or mobile apps.

This training program aims to provide a technology and business review of this emerging industry, helping IoT enthusiasts and entrepreneurs grasp the basics of IoT technology and its business potential.

Course Objective

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

It will cover a basic introduction to all elements of IoT, including mechanical components, electronics/sensor platforms, wireless and wireline protocols, integration of mobile devices with electronics, enterprise integration, data analytics, and the total control plane.

The course will also delve into M2M wireless protocols for IoT, such as WiFi, Zigbee/Zwave, Bluetooth, and ANT+, discussing when and where to use each one.

It will explore mobile/desktop/web applications for registration, data acquisition, and control, along with available M2M data acquisition platforms like Xively, Omega, and NovoTech.

The training will address security issues and solutions specific to IoT.

Participants will learn about open-source and commercial electronics platforms for IoT, such as Raspberry Pi, Arduino, and ArmMbedLPC.

The course will also cover open-source and commercial enterprise cloud platforms for AWS-IoT apps, Azure-IOT, Watson-IOT cloud, and other minor IoT clouds.

Finally, it will include case studies of 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 significantly more intricate, encompassing a wide range of core engineering disciplines such as Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics, and Mobile. Each layer of IoT involves various aspects of economics, standards, regulations, and evolving state-of-the-art advancements. This course aims to provide a comprehensive overview of these critical elements in IoT Engineering.

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

Summary

• An advanced training program that covers the current state-of-the-art in IoT for Smart Factories.
• Spans multiple technology domains to develop a broad understanding of an IoT system and its components, and how it can benefit manufacturing management professionals.
• Live demonstrations of model IIoT applications designed for smart factories.

Target Audience

• Managers responsible for business and operational processes within their respective manufacturing organizations who wish to learn how to leverage IoT to enhance the efficiency of their systems and processes.

Duration 3 Days (8 hours per day)

The estimated market value of the Internet of Things (IoT) is substantial, as it represents an integrated and pervasive layer of devices, sensors, and computing power that spans consumer, business-to-business, and government industries. By 2018, the IoT was expected to account for a significant number of connections: from 1.9 billion devices at present to 9 billion. That year, this figure would be roughly equivalent to the combined total of smartphones, smart TVs, tablets, wearable computers, and PCs.

In the consumer sector, many products and services have already integrated IoT technology, including kitchen and home appliances, parking systems, RFID, lighting and heating solutions, and numerous applications in Industrial Internet.

The underlying technologies of IoT are not new; machine-to-machine (M2M) communication has existed since the inception of the internet. However, recent developments have been driven by the emergence of affordable wireless technologies and the widespread adoption of smartphones and tablets in households. The rapid growth of mobile devices has fueled the current demand for IoT.

Since 2014, Industrial IoT (IIoT) has been widely adopted in manufacturing, leading to numerous innovations. This course will explore all the significant aspects of these IIoT advancements.

The training is designed to provide a technology and business overview of an emerging industry, enabling IoT enthusiasts and entrepreneurs to understand the fundamentals of IoT technology and business.

Course Objective

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

1. Analysis of the business and technology behind common IIoT platforms such as Siemens MindSphere and Azure IoT.
2. Overview of open-source and commercial enterprise cloud platforms like AWS-IoT, Azure-IOT, Watson-IOT, Mindsphere IIoT cloud, and other minor IoT clouds.
3. Exploration of open-source and commercial electronics platforms for IoT, including Raspberry Pi, Arduino, and ArmMbedLPC.
4. Discussion of security issues and solutions in the context of 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, and guidance on when and where to use each protocol.
7. Basic introduction to all elements of IoT, covering mechanical components, electronics/sensor platforms, wireless and wireline protocols, mobile-to-electronics integration, mobile-to-enterprise integration, data analytics, and the total control plane.

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:

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

Audience

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

Format of the course

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

Note

• Raspberry Pi supports various operating systems and programming languages. This course will use Linux-based Raspbian as the operating system and Python as the programming language. If you have specific setup preferences, please contact us to arrange.
• Participants are responsible for purchasing the Raspberry Pi hardware and components.

Over the past three years, the field of IoT engineering has undergone significant transformations, primarily driven by Microsoft, Google, and Amazon. These tech giants have invested substantial amounts to develop more manageable and secure IoT platforms. Additionally, IoT edge computing has gained considerable traction in both research and deployment, becoming a crucial component for practical IoT implementation. The advent of 5G is also poised to revolutionize the business landscape of IoT, leading to an unprecedented surge in new areas of research funding.

Despite these advancements, large-scale adoption of IoT remains slow due to security concerns at various levels. Securing firmware and gateways continues to be far from optimal. One major issue is the lack of consensus among different large IoT vendors regarding security standards. While Microsoft Azure and Amazon AWS have pushed forward with their own security frameworks, NIST has advocated for a more comprehensive approach. The OWASP model of 10 layers of IoT security had some impact but failed to gain widespread adoption from major platforms like Azure or Google.

Another significant concern is the security of firmware. Most firmware remains vulnerable to patching, whether via over-the-air (OTA) updates or through local hardware ports.

Course Objective

1. Introduce all technology stacks, data models, and vulnerabilities associated with IoT.
2. Illustrate the layers of vulnerability at each stack and between stacks.
3. Explore vulnerabilities introduced by vendors and third-party devices.
4. Study the NIST standard for IoT security.

Machine-to-Machine (M2M) involves the automated exchange of information and commands between interconnected mechanical or electronic devices.

In this instructor-led, live training in Taiwan, participants will learn about the various aspects of NB-IoT (also known as LTE Cat NB1) as they develop and deploy a sample NB-IoT based application.

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

• Identify the different components of NB-IoT and how to fit together to form an ecosystem.
• Understand and explain the security features built into NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

In this instructor-led, live training in Taiwan, participants will learn how to maximize the performance of Nginx as they set up, configure, monitor and troubleshoot Nginx for handling various forms of HTTP / TCP traffic. Topics covered include how to configure the most important parameters in Nginx, the OS and a virtual machine to gain maximum value out of Nginx.

ThingsBoard is an open-source IoT platform that provides device management, data collection, processing, and visualization for your IoT solutions.

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

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

• Install and configure ThingsBoard
• Understand the core features and architecture of ThingsBoard
• Build IoT applications using ThingsBoard
• Integrate ThingsBoard with Kafka for telemetry data routing
• Integrate ThingsBoard with Apache Spark for aggregating data from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Format of the course

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

Note

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