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Artificial Intelligence in Internet of Things presents an array of Predictive solution to the manufacturers, thus showcasing an intelligent behavior, with smart data-driven decisions.
Technology is driven by the Internet of Things (IoT). Cloud computing acts as the foundation of IoT to connect, store, and compute data. The Internet of Things (IoT), is an interrelated network of computing devices that enables the system to transfer data without any human interaction.
A PwC report states that 73% of global companies invest in IoT. It is estimated that IoT will have a total expenditure of US$1.2 billion by the year 2022.
IoT enables the devices to analyze the problem, increase its efficiencies, and improve device interactions without any human intervention. It is a machine to machine communication, with Telemetry Data as its key.
However, IoT in collaboration with Artificial Intelligence would facilitate the devices to make data-driven decisions without any human intervention.
Artificial Intelligence of Things (AIoT), a combination of Artificial Intelligence and IoT uses dynamic responses instead of relying on algorithmically programmed decisions. This enables the devices to display intelligent behavior, with effective predictable solutions.
A major difference between an IoT and AIoT is in their application. While IoT is designed to react to a particular situation or event, AIoT is designed to detect the event, and provide an insight to the manufacturer, by providing predictive solutions.
Unlike IoT, AIoT removes the additional amount of data in a cloud computing set up, thus enabling the devices to take decisions with readiness. This makes the entire process of data gathering between two devices less tedious.
IoT enables the devices to detect a failure during the line of production. With the help of the sensors embedded within the devices, the manufacturers can visualize the amount of output that's been compromised. However, this needs human intervention.
AIoT, thus regulates the devices to maintain their algorithm, detect the existing problems, display smart behaviors, and make the data-driven decision without any human intervention.
AIoT enables qualitative data transfer amongst interconnected devices without disruptions.
A report by Deloitte states that by the year 2025, 470 million "Connected cars" will be running on the road. A connected car is a vehicle that optimizes its operations, maintenance, and security of the passengers with the help of onboard sensors and internet connectivity. Such cars are governed by IoT.
The sensory devices installed in vehicles send an alert to the driver when the operation within the system is compromised,
According to an analysis by Deloitte, with the help of the gathered and shared data, the vehicle can detect the repair required for the sustainable output of the car. However, this process becomes time-consuming especially when the consumer demands are high, as it requires a strategic solution enabled by the human mind. Human intervention can become laborious to dissect the plethora of data available, to plan out a solution.
AIoT thus analyses the data pattern and behavior in the vehicles. It maps out a strategic solution by using the necessary data. This enables the functioning of an advanced predictive engine. Manufacturers, suppliers, and consumers are also presented with insight regarding customer satisfaction, early detection of problems, and the necessary changes that can help in improving the vehicle.
Better features, continuous up-gradation, and improvising older system is a necessity that enables any industry to thrives. IoT collects, stores, processes, analyses, and controls the data that is gathered from devices. It is a proactive solution. In an industry set up, IoT only detects the failures, within a given device setting. The amount of data gathered with IoT will only access the problems, sending a signal to the consumer. But AIoT is a reactive solution, will take action to provide an array of the solution, without any human intervention. As it only collects necessary data, the entire process becomes feasible especially when Industrial demands are high.
It will provide insight from the telemetry data for predictive solutions.
