Integrating artificial intelligence into the IoT - Use cases and trends

Below, we will show you different trends with some use cases where artificial intelligence is being used in the Internet of Things:

Preventive maintenance: AI in the IoT can help with preventive maintenance by analysing sensor data in real time. By collecting data from sensors in the equipment and system, AI can detect patterns and trends that may indicate a malfunction. Some examples:

• Sensor data and analysis: IoT sensors can collect data from a variety of sources, such as machinery, equipment, security systems, etc. This data can include information such as temperature, humidity, pressure, vibration, energy flow, etc. For example, if an engine begins vibrating in excess, the AI in the IoT can predict that the engine is about to fail and alert technicians to perform maintenance before the engine fails.
• Data analysis: AI in the IoT can analyse data collected in real time to identify patterns and trends in the performance of equipment or systems. This can be done using machine learning and data mining techniques. AI can identify patterns that indicate deterioration of equipment or systems and alert users to perform maintenance before a failure occurs.
• Maintenance planning: AI in the IoT can also help in maintenance planning. By analysing performance data, AI can identify when maintenance will be needed in the future and plan to perform maintenance tasks before a failure occurs.

A related article is "From the analytical model based on knowledge to big data: a novel decision support system based on IoT and machine learning for predictive maintenance in Industry 4.0", which talks about the Internet of Things and cyber-physical system, two key technologies in Industry 4.0 that enable the implementation of smart production and predictive maintenance (PdM), which aims to predict and address maintenance issues to minimise downtime and associated costs. Poor maintenance can lead to emergency breakdowns and downtime on production lines, which affects production capacity and decreases profit margins. Maintenance decision support systems (DSS) powered by IoT, Big Data and Machine Learning can help ensure the maintenance and reliability of equipment in industries. However, the application of PdM in production environments presents unresolved challenges, such as the difficulty of recovering tagged training data from failures.

Detection of data anomalies: it can also help detect abnormalities in sensor data. AI can analyse large amounts of data and detect patterns that may indicate malfunction or imminent failure. For example:

Data analysis: IoT sensors can collect data from a variety of sources, such as machinery, equipment, security systems, etc. This data can include information such as temperature, humidity, pressure, vibration, energy flow, etc. AI in the IoT can analyse data collected in real time to identify abnormal patterns. This can be done using machine learning and data mining techniques. AI can identify patterns that do not conform to standards and alert users to potential problems.

Predictive maintenance: The detection of anomalies can be used in predictive maintenance. When identifying abnormal patterns in data, AI can predict when there may be a problem in a system. For example, if a building's ventilation system shows abnormal airflow patterns, AI can predict that the system is about to fail and alert technicians to perform maintenance before that happens.

Security: The detection of anomalies can also be used to improve safety. For example, if a security system in a company shows abnormal patterns of access or employee behaviour, AI can detect these anomalies and alert security personnel to potential threats.

A related article is "Detection and prediction of anomalies in IoT devices in l'Edge computing", which talks about: Today, Internet of Things devices (Yate) are capable of running machine learning (ML) models. Taking advantage of this potential, the aim is to incorporate an ML model in a Yate device to detect and predict anomalies in the data (time series) captured, in real time, by the sensors connected to the device. Detecting and predicting anomalous data within the device can offer advantages, such as reducing the amount of erroneous data sent and thus achieving savings in transmission and also in subsequent processing of this data in the cloud, as well as filtering erroneous data. The scope of the work is environmental, in this case, to measure air quality. The sensors measure air particles. The Yate device is managed using the https://www.particle.io / platform, and two types of sensors are available to measure several diameters of air particles. The sensors are: Particulate Matter Sensor SPS30 and Laser PM2.5 Dust Sensor. This work aims to develop an ML model for the detection and prediction of anomalous data captured by the sensors connected to the Yate device, and to run it within the Yate device.

Improve efficiency and automation: The integration of AI into the IoT allows devices to be more efficient and autonomous. For example:

• Manufacturing: AI in the IoT can be used to improve efficiency and automation in manufacturing. For example, IoT sensors can collect real-time data on manufacturing processes, and AI can analyse the data to identify potential problems or inefficiencies in production. This can help manufacturers take preventive measures before problems arise and thus increase production efficiency.
• Logistics: AI in the IoT can also be used to improve efficiency and automation in logistics. For example, IoT sensors can monitor the location and status of shipments, and AI can analyse the data to optimise shipping routes and reduce transport costs. Automated monitoring systems can also be used to send status updates to clients.
• Maintenance: AI in the IoT can also be used to improve efficiency and automation in the maintenance of equipment and machinery. For example, IoT sensors can collect real-time data on equipment performance, and AI can analyse the data to detect potential problems before they occur. This can alert maintenance technicians to take preventive measures to keep equipment in good working order and avoid costly downtime.

A related article is: "Design of a monitoring system with IoT to control physical-chemical parameters in a greenhouse for hydroponic crops located in the Tigrera township in the district of Santa Marta", which talks about the context of hydroponic crops in Colombia and reveals factors that affect this business due to the growth of the urban population. The purpose of the research is to identify the problems affecting the hydroponics industry and to gather information on efficient cultivation techniques that can make water use more efficient and increase productivity. The article proposes an IoT-based monitoring system to monitor the main environmental conditions of the production process through an embedded system that is linked to a website where the farmer can visualise the information from the sensors.

Improve security: AI is being used to improve security across areas:

• On IoT devices. For example, AI can detect patterns of suspicious behaviours in sensor data to identify potential security threats.
• Physical security: It can be used to monitor environments and detect security emergencies. For example, security cameras equipped with AI can detect suspicious behaviour, such as a person loitering in one place or carrying an unusual object. By detecting these situations, measures can be sent and taken to prevent potential security emergencies.
• Cybersecurity: it can also be used to improve cybersecurity. By analysing large amounts of data generated by IoT devices, AI can detect patterns and anomalies that may indicate a cyber threat. For example, AI can detect unusual traffic on a network, which can indicate a denial of service (DoS) attack. By detecting these threats, preventive measures can be taken to avoid an attack.
• Supply chain flexibility: AI in the IoT can also be used to improve supply chain security. By tracking products through IoT sensors, AI can detect any anomalies in the shipping process, such as possible product tampering. By detecting such situations, measures can be taken to ensure product safety and prevent possible risks to consumers.

A related article is "Smart devices in industrial safety for the prevention of accidents and occupational diseases", which focuses on the importance of industrial safety in companies and constructions, and how technological solutions can contribute to improve it. The research focuses on determining a set of technologies in smart devices to prevent occupational accidents and diseases through a literature review. It concludes that technologies can capture and process data to increase security, monitor personal online status and make decisions in real time.