Feb. 3, 2020
Deploying IoT involves the installation of infrastructure, specific sensors, computers and cameras. However, to successfully deploy IoT this must also include real-time analytics, and introducing AI technologies into the mix creates AIoT.
AI on the network edge detects and reports on events detected by sensors and results in four distinct benefits.
- Real Time Analytics
- Fully Deployed Intelligence
- Combining AI Technologies
- Completing the Lifecycle
Event stream processing analyzes different types of data and is able to successfully identify which data is relevant. To handle such data, event stream processing can:
- Identify important events and prompt the necessary action: Detection of all relevant events or events that are of some interest is possible through event stream processing. These include unusual activities during bank transactions or actions on mobile.
- Constantly monitor the information that is being gathered: Event stream processing can quickly detect any irregularities that can become potential problems. If such situations arise, smart devices can immediately alert the concerned operator and use corrective measures.
- Make sure that the sensor data is clean and authentic: You might find certain inconsistencies in the sensor data. This can be due to network errors or even dirty data. Data streams have techniques to check for discrepancies and troubleshoot if required.
- Improve operations in real-time: Optimization of operations in real-time is possible with advanced algorithms. For example, the arrival time of a train can be constantly updated, especially if there is a delay in any particular station.
Using Intelligence Where the Application Requires It
Data is constantly being generated by AIoT devices. Therefore analytics should be applied in different ways to get the best possible outcome. These different methods of deploying intelligence include:
- High-performance analytics: Heavy performance analytics can be deployed on data that is not moving or is in the storage. It can be also used when the data is in the cloud.
- Streaming analytics: When large amounts of moving data need to be analyzed for a few items of interest, streaming analytics should be used. Streaming analytics can also be used if the speed is critical and alerts for an imminent crash or component failure that needs to be sent.
- Edge computing: Edge computing immediately triggers necessary action on any data. It does not wait to ingest, store or move data anywhere without acting on it first.
Combining AI Technologies
A combination of AI technologies can provide many opportunities and the best outcome. For example, machine learning, language processing, and computer vision can happen simultaneously.
Here’s an example/ Deep learning and computer vision can be used by clinics or hospitals for accurate radiographs, CT scans, and MRIs. To build patient profiles, detailing the family history of medical issues, natural language processing can be easily used along with computer vision to make the data far more accessible and accurate.
Unifying the Complete Analytics Life Cycle
To predict what will happen and to analyze what is happening in real-time, AI systems should have access to various kinds of data. If IoT is successfully implemented, the AI systems will be able to link all the following capabilities:
- Data analysis on the fly: This is event stream processing where large amounts of data are analyzed to find any relevant information.
- Real-time decision making: In case of data in motion or streaming data, if an event of interest occurs then immediately the necessary action should be triggered.
- Big data analytics: Large amounts of data can be ingested and processed when intelligence is obtained from IoT. This usually happens in a computing environment and running more iterations and using all of the data can also improve the precision of the model.
- Data management: Proper data management can clean and validate all kinds of data even when it is available in different formats.
- Analytical model management: Analytical model management is consistent and covers everything, from registration to retirement. The evolution of models can also be tracked and the performances are constantly improved.
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