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Understanding AIoT Analysis at the Edge

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Chandni U
Chandni U
Assistant Editor

Combining Artificial Intelligence-driven IoT with edge analytics can improve performance, reduce costs, and maintain deliverability.

Internet of Things (IoT) technology has allowed manufacturers to connect machines, store data, and acquire enhanced connectivity and machine-to-machine communication. But can IoT be improved for making decisions, too? 

While every technology can provide spectacular contributions, theoretically, combining two must be revolutionary. Philosopher Nick Bostrom once said, “Machine intelligence is the last invention that humanity will ever need to make.” In a broader perspective, he might be right. Improved use of technological intelligence and several combinations can enhance the technology to become the ultimate invention. 

Artificial intelligence (AI) decision-making combined with real-time data communication and analytics can augment IoT devices. Merging AI and IoT can create better devices with magnified benefits. While the IoT device would be the nervous system receiving and sending signals, AI would be the genius brainpower processing the data and allowing users to make informed decisions. Enter Artificial IoT(AIoT), a technology that can automatically take action.

These solutions help achieve outstanding quality, reduce costs, and maintain deliverability. As an added benefit, companies can gain transparency by using the required data sources, which can be further utilized in other applications, resulting in a positive feedback loop. Research indicates that the global AIoT market will reach $65 billion by 2025, growing at 40% CAGR.

To make AIoT more viable, manufacturers require a data management system to support automatic decision-making. Cloud storage is the rational option; thinking outside the box, or, to be precise, at the edge, will allow AIoT to become glorious. 

Bringing AIoT To The Edge

In most AI integrations, activities must occur locally and at high speed. For instance, if a machine is at fault, the security or the development team would want to know and correct the error as soon as possible. Integrating an AI system at the edge can improve performance, improve latency issues, and, as a result, avoid product damage.

Introducing AIoT to real-time data that has not been filtered or categorized can create chaos. Technology experts recommend edge analytics as the top approach to dealing with this. It can extract higher-value features by processing the raw volumes of data and only sending the filtered information to the cloud. 

Another significant benefit of processing data and integrating AI at the edge is increased security. While cloud computing introduces several security concerns, edge computing can complement them by filtering information at the source and enabling an on-premise storage system that requires fewer cloud data transformations.

Experts recommend that industry leaders first develop an AI model offline and train it using stored databases for improvements until it meets business requirements. Finally, the model can be exported and introduced to new live data. Edge analytics can harmonize live data from multiple sources in various formats. It will streamline data before interacting with AIoT.

AIoT at the edge can greatly help, especially when visual inspection systems are used. For instance, cameras and sensors produce surplus data, making AIoT at the edge for analysis and filtration a better and safer choice. Even in more complex situations, where mobile devices are connected to AIoT devices, the double data system can also be better served at the edge, as sending massive data to the cloud may not be effective. 

For instance, edge analytics provider Crosser platform can help companies harmonize the data from multiple sources before it reaches the AIoT. Even if the data comes in irregularly, the edge analytics solution can align it through regular time boundaries. Additionally, suppose the data sources have multiple sampling rates, in that case, the platform’s solution can help fill in the values, and the AIoT model can be updated from time to time and create windows over time series data. 

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These applications can be most beneficial in maintenance and monitoring. For instance, organizations can use AIoT for condition monitoring solutions, predictive maintenance, and predictive management of maintenance qualities. Automation company Festo offers such solutions with its AX and Smartenance products. 

Today, the largest share of AIoT applications is in monitoring production facilities in the industrial sector. Soon, the integration of AIoT and edge analytics can be increasingly visible in energy efficiency and robotics.

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