When Internet of Things (IoT) meets Artificial Intelligence (AI)

Machine learning, as part of AI, and the Internet of Things (IoT) have a wide range of applications in both research and industry.

IoT Capabilities and Challenges 

As the Internet of Things (IoT) becomes more mainstream, there is a growing requirement to comprehend the function of AI approaches in acquiring insights about the market environment and the rivals' readiness to solve the situation by then. 

In a Harvard Business Review article, it was said that IoT should be capable of handling the following four key issues: 

  1. Monitoring
  2. Control 
  3. Optimization 
  4. Autonomy

This will help make IoT more logical for the customer's usage in a smart connected environment. 

While monitoring is required for the efficient functioning of sensor nodes in a controlled working environment, optimization is required to improve performance based on the information gathered in the first two phases. 

Finally, autonomy allows IoT devices to self-diagnose and fix themselves. 

Internet of Things Won't Work Without AI 

IoT generates large data, which may be used to forecast accidents and crime using city traffic data, as well as assisting in the construction of smart homes with digitally linked household appliances and much more. 

In order to ensure that IoT meets our expectations, extracting information from the massive amounts of data produced in such IoT scenarios is a serious issue. 

Dealing with such a large amount of data (even if only a sample of it) with standard methods is also expected to be excessively time intensive. 

As a result, AI approaches included into IoT data must be used to improve speed and accuracy. 

The negative implications, such as all linked gadgets not functioning together in home applications, would undoubtedly upset the consumer; similarly, traffic may be mismanaged with hundreds of automobiles in queue, or health may be devastating with pacemaker malfunction, and the list goes on.

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