IoT in industry: the link between data and efficiency

Schwab (2016) states that “the internet of things (IoT) is one of the main bridges between physical and digital applications of the fourth industrial revolution”. Its goal is to enable and accelerate the adoption of internet-connected technologies across industries, whether manufacturing, extractive, grassroots, or services. IoT has in its basis Internet Protocols (IP) and Transmission Control Protocols (TCP). These parameters form the sensors, devices, and systems that allow the communication of these intelligent mechanisms with each other and with other internet-connected devices. It processes data from them and transmits it via wired or wireless networks, including Ethernet, Wi-Fi, Bluethooth, 5G, Radio Frequency Identifier (RFID), etc. In addition, it is common for these devices to connect with IoT Gateways, which act as an intermediaries linking them to a network or a cloud structure. The latter is important because it ensures data sharing, creating a connection between the physical and digital worlds as mentioned by Schwab.

There are two types of devices that can be integrated with the IoT. The first category consists of equipment already developed with integrated connectivity, such as cell phones, ATMs, agricultural harvesters, and streaming supports. They generate data and communicate with other devices through machine-to-machine (M2M) communication. The second category includes devices with microchips or sensors that allow this communication with other equipment. For example, a car can have a chip installed after the manufacturing process that allows it to be tracked.

IoT can be applied in a variety of ways, and its positive impact is already felt in several industries, such as manufacturing, transportation, agriculture, healthcare, etc. As the number of internet-connected devices grows, IoT plays an increasingly important role in our society.

IoT in Manufacturing

As seen, IoT facilitates communication between devices. In the manufacturing routine, this technology needs to be robust to provide the reliability and safety required by manufacturing processes. In consequence, the so-called Industrial Internet of Things (IIoT) has been developed. This type of technology is applied in processes focused on supply and demand, favoring interoperability between machines with different protocols and architectures. Some essential aspects of IIoT are the continuous collection of data and the use of technologies such as Artificial Intelligence to interpret it and provide insights. To this end, the structuring of the IIoT network is indispensable within the industry. It includes four layers: perception, network, analysis, and application.

The perception layer, also called data transmission, includes all the sensors and devices that collect the data from the industrial environment. They monitor parameters such as temperature, pressure, vibration, energy consumption. The second layer concerns the transmission of data collected by the sensors to the processing systems. This process can include local area networks (LAN), wide area networks (WAN), Wi-Fi, 5G, and other communication technologies. The analysis layer handles the processing and analysis of this data, which can be done locally (edge computing) or in the cloud (cloud computing). This results in the identification of patterns, prediction of failures, and optimization of processes. Finally, the application layer involves the use of data for informed decision-making and process automation. It is important to note that security is a critical topic. During the interaction of users with IoT systems through the web or mobile applications, there is a risk of attacks by Trojans, cross-site scripting, etc. Therefore, within the processing, it is necessary password authentication and other security features.

IIoT’s Communication protocols

IIoT is essential in Industry 4.0 as it provides the necessary infrastructure for real-time data collection, improving automation and efficiency. To achieve effective transmission between machines, communication protocols are used for different purposes. They are described with respect to operating frequency, IEEE (Institutes of Electrical and Electronics Engineers) standards, and transmission range. Some examples are:

ZigBee: wireless communication protocol, designed for low-power, low-data-rate devices. It is ideal for industrial sensors and lighting control, in addition to having low power consumption;
Bluetooth: wireless communication technology used for exchanging data over short distances. It is easy to implement, low power consumption and is widely used on mobile devices.
Wi-Fi: is a wireless network technology that allows devices to connect to the internet or other local networks. It has a high data throughput, wide coverage, and easy integration with existing network infrastructure;
MQTT (Message Queuing Telemetry Transport): It is a lightweight communication protocol, which has low power consumption and is ideal for networks with low bandwidth. In industry, the use of MQTT to track, monitor and analyze processes, improving efficiency, is a low-cost alternative with high scalability;
LoRaWAN (Long Range Wide Area Network): LoRaWAN is an M2M network protocol for long distances and low power, designed for long-range communication between IoT devices. It offers high coverage, it has low power consumption, and a single gateway capable of supporting many devices;

Challenges to the installation of IoT in industry

For the effective installation of IIoT, the industry needs to take some precautions. First, it is necessary to consider that IoT devices collect and process a wide range of data, which may be susceptible to cyberattacks. This scenario reinforces the need to implement security policies at all levels of the industry, as seen earlier. In this context, the first step is to install CIS (Center for Internet Security) security controls, and then move on to other barriers. It is also important that employees are trained and perform regular safety checks. The creation of standards and regulations is indispensable to protect users’ privacy and ensure the interoperability of IoT devices.

In addition, the adoption of IIoT must be planned on any factory floor. Otherwise, the industry may not take full advantage of this technology. The lack of strategic vision can result in low adherence to it by employees or even financial losses. With this in mind, when implementing IIoT, those responsible must map all the systems that need to be integrated, as well as the necessary technical resources. This way, it is possible to avoid technical problems that can cause unnecessary interruptions.

This last topic ties up in with the next one: the lack of KPIs. In adopting this type of technology, it is important to have clear goals. For example, in a food industry that aims to increase line efficiency, it is possible to monitor downtime and OEE rate. By monitoring these indicators, one can track the progress of the IIoT and understand its contributions, as well as what needs to be adjusted.

IIoT and Data Science

Data science is a great ally of the Industrial Internet of Things (IIoT) for several aspects, such as:

Data collection: The IIoT network utilizes sensors and devices to collect large volumes of real-time data from machinery and processes. Meanwhile, data science organizes this data to make it usable for deeper analysis;
Data Analytics: IIoT provides raw data on the status of industrial systems, while data science applies analytical methodologies to extract meaningful insights from this data;
Maintenance: With access to industrial data, data science can develop predictive models that anticipate failures and enable the implementation of predictive maintenance;
Energy Efficiency: IIoT allows the collection of data on energy consumption in different parts of the industrial process, from production to utilities. Data science, on the other hand, analyzes this data to identify opportunities for energy savings and improved sustainability;
Personalization and innovation: With data on product performance, a scenario of product and service personalization becomes possible. This also drives innovation, based on the trends and patterns identified;
Data history: Data collected by IIoT systems is recorded, creating a history that can be used to perform future analysis to increase productivity and focus on ESG goals;

Positive impacts of IoT on Manufacturing

According to the Industry Portal (2021), 69% of Brazilian industries make use of some digital technology, including IoT. Additionally, according to Fortune Business ( 2024), the global IoT market in manufacturing industries is projected to reach US$ 452.27 billion by 2032. The main applications of IoT in this segment include predictive maintenance, tracking, and asset management.

In the automotive industry, IIoT technology is used to track components and optimize internal logistics, ensuring that car parts are in the right place at the right time. An example of this is the recall, which resolved faster with specific batch tracking and historical data recording. Also in Brazil, approximately 30% of automotive industries are investing in IoT to optimize production processes and predictive maintenance.

In food and beverage industry, this scenario is no different. IoT sensors can be used to monitor food production and quality, ensuring that products meet all pre-established standards. For example, during a brewery, it is possible to monitor the fermentation process, ensuring the consistency of the final product. In addition to the production area, this type of connectivity in the utilities area ensures water and energy savings, contributing to ESG actions.

Finally, in the pharmaceutical and chemical sector, monitoring drug production conditions and chemical reactions is indispensable. For pharmaceuticals, the collection of temperature data provides more visibility into the storage conditions of medicines, ensuring that these are kept within the proper temperature range. For chemicals, the consistency of this indicator avoids adverse reactions in chemical compounds, reinforcing safety measures.

IIoT Trends

IIoT synonymous of more data and more connectivity, and consequently, a higher level of automation. With this, its evolution to keep up with the growing market changes is natural:

Artificial Intelligence and IIoT: When applied in a systematic and structured way, AI can increase the scalability of operations. AI also helps automate tasks, predict patterns, and detect problems in IoT systems;
Democratization of data access: In the manufacturing industry, line operators need to be familiar with the technologies used to fill gaps. The integration of people, processes, and products results in more effective collaboration, with easy access to information regardless of time and place. Also, this type of training decreases resistance to the implementation of new technologies;
IIoT governance: IIoT governance consists of broad control over the operations developed by systems and software. This strategy involves the implementation of audits, internal checks, updating systems and firmware, and device control. This ensures greater organization of systems and influences the storage, use, and deletion of information created by IoT mechanisms. It also contributes to the team’s conduct aligned with the industry’s objectives.
Machine Learrning: The combination of IIoT and ML is improving predictive maintenance. ML algorithms analyze the collected data to predict failures, reduce downtime, and lower maintenance costs. Besides, this combination allows the personalization of products and services by recording information about user preferences, increasing the satisfaction rate.

In short, by combining IoT with technological and cultural innovations, industries benefit in several ways. This includes increased productivity, cost reduction, optimization of the use of raw materials, and focus on ESG. Learn more about us and unlock results in your industry with the new ST-Spots™ line.