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What is 5G?

5G is the fifth set (or generation) of standards for mobile network technology defined by the 3rd Generation Partnership Project (3GPP). The first specifications that define the global minimum standard for 5G were made available in 3GPP's Release 15. Since then, several enhancements and new features have been added to the standard through subsequent releases, Release 16 and Release 17. Currently, Release 18 is in the proposal phase, with completion scheduled for 2024.

The technologies, architectures, and performance indicators (KPIs) defined for 5G have as their main objective meeting a wide diversity of use cases from various sectors of the Industry, or verticals, and also consumers of telecommunications services:

  • Enhanced Mobile Broadband (eMBB)
    • High throughput rates
    • Enhanced spectral efficiency
    • Extended coverage
  • Ultra-Reliable Low-Latency Communications (URLLC)
    • Low latency
    • Ultra-reliable
    • Precise location
    • Mission-critical

What is the Internet of Things (IoT)?

The Internet of Things (IoT) is a concept that concerns the connection of any type of device (thing) to the internet and/or other devices. This connection allows, among other things, the connected devices to be monitored and/or controlled remotely effectively.

Because it is a concept, there is not a single technological IoT solution but many possibilities and various technologies and implementations that meet specific needs.

Among the reference models that best describe the architecture of an IoT system is the Five-Layer Architecture:

  • Layer 1: Devices (or Perception)
    • Sensors, actuators, smart devices, gateways, etc.
    • They capture data, perform actions, or both in the environment.
  • Layer 2: Connectivity (or Network)
    • Wi-Fi, Bluetooth, RFID, LoRaWAN, Sigfox, NFC, 2G, 3G, 4G, and 5G.
    • Transmits device data to higher layers and vice versa.
  • Layer 3: Cloud (or Processing
    • Public or private cloud, edge computing, or a dedicated bare-metal server.
    • Data accumulation and abstraction, storage, calculations and processing, and application software programs.
  • Layer 4: Applications and Integration
    • Management software, Artificial Intelligence, and decision-making.
    • Management of configuration, performance, failures, and integration with other business platforms.
  • Layer 5: Business
    • Monitoring of KPIs, Analytics, and decision-making.
    • Management and visualization of the entire IoT system, including applications and data-driven decision-making.

In summary, IoT enables seamless access to unprecedented amounts of data, which is collected, transmitted, stored, processed, and used for increasingly accurate and effective monitoring and decision-making. The adoption of IoT has become essential for Industry 4.0, Agribusiness, Smart Cities, among other sectors, since the potential impact on business results is enormous, driving new investments in research and development of these technologies and creating a virtuous cycle of innovation and sustainable growth.

What is Cloud Computing?

Cloud Computing is the sale of computer resources as a service, and despite it being a simple concept, this technology has completely revolutionized the development and use of computing. This model does not require companies' physical servers, which provides the service's contracting party with many advantages. Among them, it is possible to mention very high scalability, remote access to applications, lower initial acquisition costs, which allow smaller companies to access the very best in computing, more reliability, and ease of maintenance.

Cloud services can be hired with different resource availabilities, several types of requirements, and by varied users.

  • Private Cloud
    • The infrastructure is owned and managed by the same company that uses it
  • Public Cloud
    • The infrastructure is owned and managed by the service provider and can be accessed by anyone via the internet.
  • Hybrid Cloud
    • It contains at least one instance of each previous type, allowing the contracting company to enjoy the security of a private cloud with the scalability of a public one.
  • Multicloud
    • It uses clouds of the same type from different providers.

The Cloud service is available in different models, which may vary as needed by the contracting party; what changes is basically what the user manages.

  • IaaS
    • The provider makes the infrastructure as a service available to users.
  • PaaS
    • The provider makes the infrastructure (hardware) and part of the software (e.g., virtualization, operating system, middleware, etc.) available as a service to the user.
  • SaaS
    • In addition to all the features of PaaS, the provider makes cloud-based application software available to the user, who does not manage any technical aspects and only uses all the features practically and directly.

Architecture

  • Virtualization and Containerization
    • Mechanisms used to host applications on a server system. Virtualization uses the concept of virtual machines as a fundamental unit and creates virtual or software-based versions of a computing resource. On the other hand, containerization encapsulates an application in a 'container' with its own operating environment. Thus, multiple containers may use the same host operating system instead of installing one operating system for each virtual machine.
  • Edge Computing
    • It consists of bringing part of the processing and data closer to the application, as traveling shorter physical distances makes communication faster and more affordable.
  • Fog Computing
    • It offers an intermediate solution between Cloud and Edge. It can be used to filter unwanted data and store only relevant information in the Cloud, among other benefits.

In summary, Cloud Computing is essential in digital transformation, and the tendency is that more and more sectors will use this technology capable of providing and democratizing what is most advanced in computing with the flexibility to adapt to the project as needed.

What is Artificial Intelligence?

Artificial Intelligence (AI) consists of sophisticated technological solutions, more precisely algorithms that mimic the human mind with regard to problem-solving and decision-making, i.e., carrying out activities in a way that is considered intelligent. This technology is one of the most disruptive today and can potentially transform the processes of organizations profoundly.

AI can also learn autonomously by analyzing large volumes of data and identifying patterns that expand its 'knowledge.'

Currently, there are different types of solutions that meet different needs.

  • Machine Learning
      It consists of algorithms that gradually evolve their accuracy and have a behavior similar to that of humans in terms of problem-solving and is the technology behind recommendation systems, predictive text, chatbots, etc. In the first step, a dataset is provided to the program, such as images, sounds, documents, among others. After cleaning the data and choosing the Machine Learning model, which determines the expected output data, an initial training of the algorithm begins. The bigger the dataset, the greater the accuracy. Subsequently, the model is reassessed, and it is verified whether the results obtained are as expected. This process repeats itself, and fine adjustments to the parameters are made to improve their accuracy; finally, it is possible to use the Machine Learning algorithm for its main purposes, such as forecasting and recognition.
  • Artificial Neural Networks
      Artificial Neural Networks (ANN) are a specific type of Machine Learning algorithm. Its structure is based on brain function and how neurological signs are sent, hence its name. In ANN, cells or nodes are connected through layers: data input, hidden layer(s), and output layer. Each node represents an 'artificial neuron' and has its own weight and threshold. When passing through a node, the algorithm checks if the received amount is above the limit established. If it is, it passes to the next node of the neural network, reaching the output layer. Through this process, neural networks become powerful tools for high-speed data classification, such as internet search tools.
  • Deep Learning
      It is another subset of Machine Learning, which is based on the neural network algorithm but more sophisticated, also designed like the human brain, but with more layers. These additional layers allow the processing of large amounts of data and determine the weight of each link more accurately. This greater processing power also requires a very high computational power, unfeasible for regular computers. With the massive amount of data currently available added to the availability of Cloud Computing, it is possible to create multi-layered Deep Learning algorithms, which allow several applications, mainly in the field of image processing and natural language and in medicine.

In short, AI leverages virtually any activity, whether in the field of research or decision-making. For example, it automates logistics processes, assists healthcare professionals in complex diagnoses, performs pest identification through remote sensing or harvest estimates on farms, and the identification of parts with manufacturing defects in the industry, and has many other applications.