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Hyperautomation is a process in which businesses automate as numerous commerce and IT forms as conceivable utilizing apparatuses like AI, machine learning, event-driven computer program, mechanical process automation, and other sorts of choice prepare and task automation instruments.

It is the key to both computerized operational greatness and operational resiliency for organizations. To empower this, organizations had to digitize their documents/artifacts and guarantee their trade and IT process workflows were advanced. They got to mechanize tasks, processes and coordinate computerization over utilitarian zones.

Hyperautomation is irreversible and inevitable. Everything that can and should be automated will be automated.

Brian Burke, Research Vice President, Gartner

Gartner prepared a Tech Trends 2021 summary with key features of the constantly changing market. Read more at: https://www.gartner.com/en/information-technology/trends/top-strategic-technology-trends-iot-gb-pd

Industrial IoT market evolution

Data generated over the Internet of Things is growing exponentially faster than the traditional cloud environment where data is stored, so just the amount of data can justify the acceleration. In addition, in the cloud as the destination, problems related to data transfer (delay and bandwidth) occur, so travel speed is the main issue. This edge is necessary as a solution to the inefficiency of IIoT to Cloud architecture.

Fast data processing of Industrial IoT devices

When industrial IoT devices and edge processing work together, digital information becomes more powerful. Especially in contexts where you need to collect data in a traditional edge context, such as a smart meter, a parking meter or a connected trash can in a street apartment. The installation of sensors with internet access in metropolitan garbage containers is becoming increasingly common for smart urban engineers. You can then remotely monitor the container using the sensor. When it is full, the city sanitation service receives a notification and can register an order and empty the container.

By introducing AI (artificial intelligence) into the device itself, edge computing can also make more context-sensitive, quick decisions at the edge. Data gathered from the sensors can be transferred to the cloud at any time after local work has been completed, contributing to a more global AI process, or archived. With the combination of industrial IoT devices and advanced technology, high quality analysis and small footprint will become the AI standard in 2020.

ModBerry AI GATEWAY with Raspberry Pi CM4 and Google Coral

Latest innovations used in industrial solutions

One of many uses of IoT can be edge devices, dedicated to data management, process control (e.g. with MQTT protocol) and monitoring. Latest ESP32-based eModGATE controller from TECHBASE company is a series utilizing MicroPython environment to provide data management solutions for end-points applications. The eModGATE has built-in Wi-Fi/BT modem and can be equipped with additional NarrowBand-IoT, LoRa, ZigBee, etc.

For example eModGATE eqipped with wireless NB-IoT modem are perfect for industrial automation solutions, e.g. data logging, metering, telemetrics, remote monitoring, security and data management through all Industrial IoT applications.

Updated ClusBerry device for Smart Home and developers

A new addition to TECHBASE’s Industrial IoT Ecosystem is a variation of recent cluster device, ClusBerry based on multiple Raspberry Pi Compute Module 4 and custom cluster board allowing free configuration from two up to eight modules. Each module can perform various tasks, from standard I/O gateway, wireless modem, Gigabit LAN router to NAS file server and AI Gateway with up to 4 Google Coral Edge TPU modules.

You can manage your cluster modules at ease, boot modules from one to another, upgrade firmware crosswise and provide safe operation of each module. The modules are connected to internal Ethernet Switch and USB OTG to provide such feature and allow quick heal of the cluster.

Raspberry Pi Compute Module 4 Cluster for Smart Home

For home applications and with the nod to software developers, we released ClusBerry device in less industrial casing, to be used in the comfort of own house – on your desk next to PC or wall-mounted in any convenient place. ClusBerry for Home is fully modular as it’s industrial version and offers the same performance and options.

More information here: https://clusberry.techbase.eu/

New features of multiple Compute Modules 4 brought to new ClusBerry series

Accompanying the release of ModBerry 500-CM4 and AI GATEWAY 9500-CM4, we present to you a cluster version of the device, called ClusBerry 9500-CM4. Main difference between standard Gateway and ClusBerry is the possibility to include multiple Compute Module 4 in one device, as well as the intended suitable amount of wired and wireless interfaces, suited for the project.

Fully configurable devices are something desirable in the IoT market, where high performance and low cost is a key factor to success of implementation. TECHBASE’s Industrial IoT Ecosystem gives the opportunity to adjust ordered devices with certain resources and cut unnecessary I/Os, lowering the total cost of the device. 

Reason for use of Raspberry Pi CM4 cluster in ClusBerry 9500-CM4

Various implementations must have guaranteed high hardware performance to react fast enough in real time. For this purpose, the arrays of processor blocks are constructed to be assigned to individual tasks. For several years now, attempts have been made to use various types of SBC for this purpose, including, of course, Raspberry Pi. However, the practical effectiveness of such solutions so far has not been of interest for several reasons. First of all, these solutions were most often associated with many mechanical limitations and the structure of the matrix itself required excessive wiring, preventing failure-free operation and the cost of the entire maintenance of the structure.

Raspberry Pi Compute Module 4 Cluster

This is where Raspberry Pi Compute Module can shine, but due to the hardware speed limitations of the buses in this module, it was not completely effective and was rather a development platform. Altho the introduction of new Compute Module 4 has opened the possibility to construct and maintain effective hardware matrix solutions with the use of both PCI-Express buses and 1GBps Ethernet. Therefore, the ClusBerry 9500-CM4 opens up completely new capabilities of utilizing cluster solutions for Industrial Automation and server applications.

Wide range of ClusBerry modules

ClusBerry 9500-CM4 supports up to 8 cluster modules and comes with a variety of interchangeable modules to choose from, including:

  • Standard 9500-CM4 cluster module with Compute Module 4 and chosen configuration:
    • I/O Controller with range of DI, DO, AI, 1-Wire, RS-232/485 and CAN interfaces
    • Communication Gateway with wired (1/2x Ethernet, Serial Ports) and wireless interfaces (LTE-cat.M1, 4G, 5G, LoRa, ZigBee, Z-Wave, Wireless M-Bus)
    • AI Gateway with 1x Coral Edge TPU via PCIe M.2, introduced in December 2020: https://iiot-shop.com/product/ai-gateway/ or up to 4x Coral Edge TPU via USB3.0
  • NAS File Server with 2x SSD SATA III and RAID support, managed with Nextcloud or ownCloud software
  • USB3.0 Hub for 5G communication, Modems, AI Cluster and peripherals
  • Gigabit LAN/WAN Router with additional 2.5GbE network card as an independent switch/router shielded from the mainboard cluster network
  • SuperCap / Power management module for backup power supply (supercapacitors / Li-Ion battery) and sleep mode management aided with ESP32-module
  • Additional expansion cards, with resources suited for the installation (DIO, AIO, Serial Ports and dedicated sensor cards, detailed below)
Raspberry Pi Compute Module 4 Cluster

ClusBerry 9500-CM4 with available expansion cards 

ClusBerry 9500-CM4 can be equipped with multiple expansion cards, e.g. serial RS-232/485 ports, range of digital and analog I/Os, USB, HDMI and Ethernet. Interfaces can be expanded with additional I/Os and opto-isolation, relays, Ethernet, 1-Wire, CAN, M-Bus Master and Slave, accelerometer and many more features like TPM Security Chip & eSIM. The device can also be equipped with additional SuperCap backup power source for continuous work and safe boot/shutdown in case of emergency.

ClusBerry 9500-CM4 series also offers a standard PCI module support for various wireless communication protocols, such as:

  • GSM modem (4G/LTE and fast 5G modem)
  • economic NarrowBand-IoT technology
  • LoRa, ZigBee, Z-Wave, Sigfox, Wireless M-Bus
  • secondary Wi-Fi/Bluetooth interface or Wi-Fi Hi-Power
  • custom wireless interfaces
Raspberry Pi Compute Module 4 Cluster for Smart Home

Software cluster management with Docker and K3s Lightweight Kubernetes

With use of Docker-based and Kubernetes solutions, installation and management of ClusBerry 9500-CM4 is easy and backed with a large community for further support and development. Kubernetes is a portable, extensible open-source software platform for managing containerized tasks and sites that enables declarative configuration and automation. The Kubernetes ecosystem is large and dynamically developing. Kubernetes services, support and tools are widely available.

Kubernetes provides:

  • Detection of new services and traffic. Kubernetes can balance the load and redirect the network traffic to ensure the stability of the entire installation.
  • Kubernetes data storage management enables you to automatically mount any type of storage system – on-premises, from cloud providers and others.
  • Automatic deployment and rollback. You can describe the expected state of your installation with Kubernetes, which will take care of bringing the actual state to the expected state in a controlled manner. For example, with Kubernetes, you can manage your cluster modules at ease, boot modules from one to another, upgrade firmware crosswise and provide safe operation of each module
  • Automatic management of available resources. ClusBerry 9500-CM4 provides a cluster of modules that Kubernetes can use to run tasks in containers. You determine the CPU power and RAM requirements for each container. Kubernetes arranges containers on machines in such a way as to make the best use of provided resources.
  • Self-healing Kubernetes reboots containers that have stopped working, replaces them with new ones, forces disabling containers that are not responding to certain status queries, and does not announce their availability until they are ready to run.
  • Managing confidential information and Kubernetes configuration with TPM Security Chip allows you to store and manage confidential information such as passwords, OAuth tokens and SSH keys. Secured data and configuration information can be provided and changed without having to rebuild the container image and without exposing sensitive data in the overall software configuration.
Raspberry Pi Compute Module 4 Cluster for Smart Home

ClusBerry 9500-CM4 availability

First prototypes are being developed, since Compute Module 4 is already available for the purchase. Delivery time for various configurations of ClusBerry will be approximately 2 months, depending on the CM4 supply on the market and chosen expansion cards. For more information contact TECHBASE’s Sales Department via email or Live Chat here or visit product website: https://clusberry.techbase.eu.

New features of multiple Raspberry Pi Compute Modules 4 brought to new ClusBerry series

Accompanying the release of ModBerry 500-CM4 and AI GATEWAY 9500-CM4, we present to you a cluster version of the device, called ClusBerry 9500-CM4. Main difference between standard Gateway and ClusBerry is the possibility to include multiple Raspberry Pi Compute Module 4 in one device, as well as the intended suitable amount of wired and wireless interfaces, suited for the project.

Fully configurable devices are something desirable in the IoT market, where high performance and low cost is a key factor to success of implementation. TECHBASE’s Industrial IoT Ecosystem gives the opportunity to adjust ordered devices with certain resources and cut unnecessary I/Os, lowering the total cost of the device. 

Raspberry Pi Compute Module 4 Cluster

Reason for use of Raspberry Pi CM4 cluster in ClusBerry 9500-CM4

Various implementations must have guaranteed high hardware performance to react fast enough in real time. For this purpose, the arrays of processor blocks are constructed to be assigned to individual tasks. For several years now, attempts have been made to use various types of SBC for this purpose, including, of course, Raspberry Pi. However, the practical effectiveness of such solutions so far has not been of interest for several reasons. First of all, these solutions were most often associated with many mechanical limitations and the structure of the matrix itself required excessive wiring, preventing failure-free operation and the cost of the entire maintenance of the structure.

Raspberry Pi Compute Module 4 Cluster

This is where Raspberry Pi Compute Module can shine, but due to the hardware speed limitations of the buses in this module, it was not completely effective and was rather a development platform. Altho the introduction of new Compute Module 4 has opened the possibility to construct and maintain effective hardware matrix solutions with the use of both PCI-Express buses and 1GBps Ethernet. Therefore, the ClusBerry 9500-CM4 opens up completely new capabilities of utilizing cluster solutions for Industrial Automation and server applications.

Wide range of ClusBerry modules

ClusBerry 9500-CM4 supports up to 8 cluster modules and comes with a variety of interchangeable modules to choose from, including:

  • Standard 9500-CM4 cluster module with Compute Module 4 and chosen configuration:
    • I/O Controller with range of DI, DO, AI, 1-Wire, RS-232/485 and CAN interfaces
    • Communication Gateway with wired (1/2x Ethernet, Serial Ports) and wireless interfaces (LTE-cat.M1, 4G, 5G, LoRa, ZigBee, Z-Wave, Wireless M-Bus)
    • AI Gateway with 1x Coral Edge TPU via PCIe M.2, introduced in December 2020: https://iiot-shop.com/product/ai-gateway/ or up to 4x Coral Edge TPU via USB3.0
  • NAS File Server with 2x SSD SATA III and RAID support, managed with Nextcloud or ownCloud software
  • USB3.0 Hub for 5G communication, Modems, AI Cluster and peripherals
  • Gigabit LAN/WAN Router with additional 2.5GbE network card as an independent switch/router shielded from the mainboard cluster network
  • SuperCap / Power management module for backup power supply (supercapacitors / Li-Ion battery) and sleep mode management aided with ESP32-module
  • Additional expansion cards, with resources suited for the installation (DIO, AIO, Serial Ports and dedicated sensor cards, detailed below)
Raspberry Pi Compute Module 4 Cluster

ClusBerry 9500-CM4 with available expansion cards 

ClusBerry 9500-CM4 can be equipped with multiple expansion cards, e.g. serial RS-232/485 ports, range of digital and analog I/Os, USB, HDMI and Ethernet. Interfaces can be expanded with additional I/Os and opto-isolation, relays, Ethernet, 1-Wire, CAN, M-Bus Master and Slave, accelerometer and many more features like TPM Security Chip & eSIM. The device can also be equipped with additional SuperCap backup power source for continuous work and safe boot/shutdown in case of emergency.

ClusBerry 9500-CM4 series also offers a standard PCI module support for various wireless communication protocols, such as:

  • GSM modem (4G/LTE and fast 5G modem)
  • economic NarrowBand-IoT technology
  • LoRa, ZigBee, Z-Wave, Sigfox, Wireless M-Bus
  • secondary Wi-Fi/Bluetooth interface or Wi-Fi Hi-Power
  • custom wireless interfaces
Raspberry Pi Compute Module 4 Cluster

Software cluster management with Docker and K3s Lightweight Kubernetes

With use of Docker-based and Kubernetes solutions, installation and management of ClusBerry 9500-CM4 is easy and backed with a large community for further support and development. Kubernetes is a portable, extensible open-source software platform for managing containerized tasks and sites that enables declarative configuration and automation. The Kubernetes ecosystem is large and dynamically developing. Kubernetes services, support and tools are widely available.

Kubernetes provides:

  • Detection of new services and traffic. Kubernetes can balance the load and redirect the network traffic to ensure the stability of the entire installation.
  • Kubernetes data storage management enables you to automatically mount any type of storage system – on-premises, from cloud providers and others.
  • Automatic deployment and rollback. You can describe the expected state of your installation with Kubernetes, which will take care of bringing the actual state to the expected state in a controlled manner. For example, with Kubernetes, you can manage your cluster modules at ease, boot modules from one to another, upgrade firmware crosswise and provide safe operation of each module
  • Automatic management of available resources. ClusBerry 9500-CM4 provides a cluster of modules that Kubernetes can use to run tasks in containers. You determine the CPU power and RAM requirements for each container. Kubernetes arranges containers on machines in such a way as to make the best use of provided resources.
  • Self-healing Kubernetes reboots containers that have stopped working, replaces them with new ones, forces disabling containers that are not responding to certain status queries, and does not announce their availability until they are ready to run.
  • Managing confidential information and Kubernetes configuration with TPM Security Chip allows you to store and manage confidential information such as passwords, OAuth tokens and SSH keys. Secured data and configuration information can be provided and changed without having to rebuild the container image and without exposing sensitive data in the overall software configuration.
Raspberry Pi Compute Module 4 Cluster

ClusBerry 9500-CM4 availability

First prototypes are being developed, since Compute Module 4 is already available for the purchase. Delivery time for various configurations of ClusBerry will be approximately 2 months, depending on the CM4 supply on the market and chosen expansion cards. For more information contact TECHBASE’s Sales Department via email or Live Chat here or visit product website: https://clusberry.techbase.eu.

New features of Edge TPU brought to ModBerry series

In October 2020, with the release of the latest Compute Module 4 from Rasbperry Pi Foundation, TECHBASE announced an upgraded device from ModBerry 500 series, called ModBerry 500 CM4. Thanks to the high-performance PCI-Express bus introduced in Compute Module 4 and Raspberry Pi community, the device itself presents support for a wide range of new applications, such as use of Google’s Artificial Intelligence modules at ease.

Therefore, TECHBASE designed a new device, called ModBerry AI GATEWAY 9500-CM4, utilizing the vertical format of ModBerry 9500, latest Compute Module 4 and Google’s Coral TPU. Installation-ready AI GATEWAY allows direct application in industrial fields.

TECHBASE’s AI GATEWAY series, world-first industrial gateway utilizing Raspberry Pi Compute Module 4 and Google Coral TPU

AI GATEWAY with Coral TPU enhancement 

Neuron network capabilities enhance CM4-based devices, not only collecting and sending data, but also allows local data change predictions and allows direct management on-site. This feature gives the possibility for various applications, such as data analysing and establishing trends predictions, smart alarms and smart monitoring, local notification control, etc.

Used Edge TPU coprocessor via PCI-Express bus is capable of performing 4 trillion operations per second (TOPS), using 0.5 watts for each TOPS (2 TOPS per watt). Google Coral easily integrates with Raspberry Pi Compute Module in Linux and optionally in Windows with full support of TensorFlow Lite framework and AutoML Vision Edge solution.

TECHBASE’s AI GATEWAY series, world-first industrial gateway utilizing Raspberry Pi Compute Module 4 and Google Coral TPU
TECHBASE’s AI GATEWAY series, world-first industrial gateway utilizing Raspberry Pi Compute Module 4 and Google Coral TPU

AI GATEWAY with available expansion cards 

ModBerry AI GATEWAY 9500-CM4 can be equipped with serial RS-232/485 ports, range of digital and analog I/Os, USB, HDMI and Ethernet. Interfaces can be expanded with additional I/Os and opto-isolation, relays, Ethernet, 1-Wire, CAN, M-Bus Master and Slave, accelerometer, OLED screen and many more features like TPM Security Chip, eSIM and SuperCap backup power support. 

ModBerry AI GATEWAY 9500-CM4 series also offers a standard PCI module support for various wireless communication protocols, such as:

  • GSM modem (4G/LTE and fast 5G modem, interchangeable with Coral TPU)
  • economic NarrowBand-IoT technology
  • LoRa, ZigBee, Sigfox, Wireless M-Bus
  • secondary Wi-Fi/Bluetooth interface or Wi-Fi Hi-Power
  • custom wireless interfaces

ModBerry AI GATEWAY 9500-CM4 availabilityFirst prototypes are being developed, since Compute Module 4 is already available for the purchase. Delivery time for various configurations of AI GATEWAY will be approximately 2 months, depending on the CM4 supply on the market and chosen expansion cards. For more information contact TECHBASE’s Sales Department via email or Live Chat here.

Advantages of Industrial IoT in modern manufacturing and smart environments

Industrial Internet of Things (Industrial IoT or just IIoT for short) uses Internet of Things technology to improve production and industrial processes. These processes increasingly require connected devices to perform their tasks effectively.

Data generated over the Internet of Things is growing exponentially faster than the traditional cloud environment where data is stored, so just the amount of data can justify the acceleration. In addition, in the cloud as the destination, problems related to data transfer (delay and bandwidth) occur, so travel speed is the main issue. This edge is necessary as a solution to the inefficiency of IIoT to Cloud architecture.

IIoT market predictions

Industrial IoT devices and edge computing have grown at impressive rates. Accenture predicts the IIoT market will reach $500 billion by 2020; and IIoT already generates 400 zetabytes a year. Gartner estimates that IoT currently generates about 10% of enterprise data; by 2022, Gartner has predicted this will increase to 50%.

According to IDC, IT’s annual investment on edge infrastructure will hit 18% of total IoT spending; and per last year’s Forrester Analytics Global Business Technographics Mobility Survey, 27% of global telecom decision-makers say their companies will either implement or expand edge computing this year.

Source: https://www.cisco.com/c/en/us/solutions/internet-of-things/industrial-iot-devices.html

Perimeter (edge) computing architectures bring computing processing closer to the users and devices that need it, rather than centrally processing it in a local data center or public cloud. This edge is important for industrial and production processes that use large amounts of data that require fast response times and tight security.

Fast data processing of Industrial IoT devices

When industrial IoT devices and edge processing work together, digital information becomes more powerful. Especially in contexts where you need to collect data in a traditional edge context, such as a smart meter, a parking meter or a connected trash can in a street apartment. The installation of sensors with internet access in metropolitan garbage containers is becoming increasingly common for smart urban engineers. You can then remotely monitor the container using the sensor. When it is full, the city sanitation service receives a notification and can register an order and empty the container.

By introducing AI (artificial intelligence) into the device itself, edge computing can also make more context-sensitive, quick decisions at the edge. Data gathered from the sensors can be transferred to the cloud at any time after local work has been completed, contributing to a more global AI process, or archived. With the combination of industrial IoT devices and advanced technology, high quality analysis and small footprint will become the AI standard in 2020.

Industrial IoT use of ESP32 chip in eModGATE

Latest innovations used in industrial solutions

One of many uses of IoT can be edge devices, dedicated to data management, process control (e.g. with MQTT protocol) and monitoring. Latest ESP32-based eModGATE controller from TECHBASE company is a series utilizing MicroPython environment to provide data management solutions for end-points applications. The eModGATE has built-in Wi-Fi/BT modem and can be equipped with additional NarrowBand-IoT, LoRa, ZigBee, etc.

For example eModGATE eqipped with wireless NB-IoT modem are perfect for industrial automation solutions, e.g. data logging, metering, telemetrics, remote monitoring, security and data management through all Industrial IoT applications.

The latest research results from IoT Newark developers reveal that 49% of respondents use AI in their IoT applications. There is also a growing concern about user privacy and the more frequent introduction of ready equipment.

35% of respondents think security is the major concern for any IoT implementation, mainly due to the type of data collected from the things (machines) and humans, which is very sensitive & personal. We can expect to see more and more encryption everywhere. Businesses who initiate IoT projects treat IoT security as their top priority.

SBCs the main platform for Industrial IoT

SBC is still the preferred hardware foundation for IoT gates, then 54%, followed by personal projects (30%) and silicon supplier platforms (13%). It is unclear whether the latter includes a commercial computing module. As shown in the graph above, many IoT programmers need third party help, especially for edge-to-cloud communication.

About 45% of respondents use environmental sensors for IoT devices, followed by motion sensors (26%) and optical / image sensors (15%). WiFi (67%) is the most popular wireless technology in Internet of Things projects. The next places are Low cellular energy and Bluetooth, followed by LoRa at 21%. The survey results also include responses to programming languages, cloud platforms, IoT data, project motivation and more.

Artificial Intelligence influencing Industrial IoT

From the end of 2017 to 2018, artificial intelligence-specific processors (AI) began to appear on mobile devices. The goal is to make smartphones more intelligent. As GPUs shrink, AI-related equipment becomes necessary for the Internet of Things.

Support for enterprises from platforms such as Google TensorFlow will be introduced in 2020 with equipment adapted to artificial intelligence. TensorFlow is already optimized for mobile devices and can be quickly launched on single-board computers. In many ways, AI frameworks are better than other mobile frameworks, such as ReactJS. The AI structure is not designed to work with the user interface. It’s perfect for the Internet of Things.

Until the end of 2020, artificial intelligence will be as important for IoT devices as the cloud.

Edge of 2020 in Industrial IoT - forecast

How to understand Internet of Things phenomenon?

The term Internet of Things is used to describe physical objects that have sensors that enable data acquisition and communicate with each other and the Internet. They belong to the following categories:

  • Wearable sensors – sensors built into clothing or smartphones and smart watches
  • Medical parameter sensors for monitoring health
  • Sources of geoinformation, allowing to determine the location of objects and people
  • Sensors of physical parameters of the environment, e.g. temperature, pressure, insolation, dust
  • Sensors for the operation of technical devices, e.g. measuring power consumption, performance, including specialized sensory networks in industrial installations.

Other factors that contribute to the popularity of IoT are the versatility of use (e.g. intelligent buildings and cities, healthcare, trade, sport) as well as the benefits obtained through their implementation, e.g. streamlining the delivery process, loss prevention, and improving customer experience. Therefore, we can expect a growing number of new IoT solutions appearing on the market for various sectors. Gartner estimates that IoT product and service providers will generate growing revenue of $300 billion in 2020.

Edge of 2020 in Industrial IoT - forecast

The Internet of Things (IoT) has rapidly become one of the most familiar — and perhaps most hyped — expressions across business and technology. We expect to see 20 billion internet-connected things by 2020. These “things” are not general-purpose devices, such as smartphones and PCs, but dedicated-function objects, such as vending machines, jet engines, connected cars and a myriad of other examples. The IoT will have a great impact on the economy by transforming many enterprises into digital businesses and facilitating new business models, improving efficiency and increasing employee and customer engagement.

Source: Gartner, https://www.gartner.com/imagesrv/books/iot/iotEbook_digital.pdf

With new possibilities, new challenges arise, such as the creation of an unprecedented amount of data. According to Oracle, there will be 40 trillion GB of IoT data by 2020.

Open source IoT solutions rushing gaining market

Referring to the results of the 2016 Future of Open Source Survey conducted by Black Duck and North Bridge, 65% of companies increased the use of open source solutions in 2016 compared to 2015. Not only small and medium enterprises use open source solutions. Open source solutions and technologies are used by large international corporations. They see no need to pay for solutions and services that they can use virtually free. Employing only people with appropriate qualifications.

Large corporations are not interested in paying for server logos, network devices, or mass storage. Instead of buying equipment from well-known suppliers, they prefer to set up an order in companies in East Europe and Asia. Companies where devices are designed and assembled (ODM original-design manufacturers).

Software-as-a-Service will be standard

As for IoT application trends, Software-as-a-Service is seen as a hot topic of discussion. SaaS is a service model. In this model, the service provider provides the desired application and makes it available to clients via the Internet. This helps organizations to outsource IT applications.

These Internet of Things trends provide companies with a marketing platform to promote their products. To this end, Stewart Butterfield, co-founder of Slack, a cloud-based instant messaging platform, said:

Every interaction with the customer is a marketing opportunity. When you go beyond the customer service page, people are more likely to recommend you.

SaaS is the preferred choice for the IT gaming industry due to the low investment cost. The emergence of SaaS has significantly contributed to the development of technology. When this trend of the Internet of Things appears on the market, people’s lives are better than ever.

Artificial Intelligence influencing Industrial IoT

From the end of 2017 to 2018, artificial intelligence-specific processors (AI) began to appear on mobile devices. The goal is to make smartphones more intelligent. As GPUs shrink, AI-related equipment becomes necessary for the Internet of Things.

Support for enterprises from platforms such as Google TensorFlow will be introduced in 2020 with equipment adapted to artificial intelligence. TensorFlow is already optimized for mobile devices and can be quickly launched on single-board computers. In many ways, AI frameworks are better than other mobile frameworks, such as ReactJS. The AI structure is not designed to work with the user interface. It’s perfect for the Internet of Things.

Until the end of 2020, artificial intelligence will be as important for IoT devices as the cloud.