The Workflow of IoT

 - Overview

With the rapid growth of IoT networks in various industries, a large amount of unstructured machine data also exists. Increasingly human-centred and machine-generated information creates a need for AI support to handle unstructured data analysis. The data generated by IoT-enabled systems is critical to meeting business needs and solving functional issues related to product lifecycle management. 

AI and IoT contain similar interconnected components. When these two sets of technologies go hand in hand, they mutually benefit and add value to each other. AI adds value to IoT through machine learning and decision-making processes, and IoT adds value to AI through data exchange, signaling, and connectivity. 

AIoT demonstrates the need for interoperability of devices (chipsets), software (programs and operating systems), and platforms. As before, AI implementations are ensembles of vertically engineered solutions that require APIs to make devices, software, and platforms highly interoperable.

- The Entire Workflow of AIoT

Below are the sequential steps for any AIoT-enabled solution.

• Data Collection: The first step involves device creation and data collection. Collect data with the help of sensors in the device to collect multiple sets of data.
• Data transfer: Once collected data is usually stored in its respective collection, usually in the cloud. Storing large amounts of data in the cloud is easier and less expensive than installing hardware.
• Data processing: Data is stored in cloud servers for processing. Data is processed in stages based on the extraction and cleaning of important data.
• Data projection: The processed data is communicated through different networks. It is aggregated and analyzed into actionable information.
• Information Execution: Actionable information collected is then put into practical use.