Machine Learning has become synonymous to Artificial Intelligence today. Most of the internet and software applications are already using AI and ML to great extent. Surprising fact is Machine Learning has entered the industrial sphere in the form of IoT (Internet of Things).
Now, Internet of Things is a huge network of devices connected to the internet and they use internet and its protocols to communicate with other devices in the same network. Devices under IoT are usually data collecting devices. This data will be stored in a cloud network and then later requested for further analysis.
How is Machine Learning relevant to Industrial IoT
Most of the industries are trying to automate the process of developing or manufacturing products. This kind of large scale automation needs a high-level security and risk analysis systems. To collect data regarding these security issues, industries use IoT. This data is gradually collected, stored and analysed. Machine Learning can recognize the repeated patterns and teach the device to be vigilant next time. Also, ML can help you automate the process of data analysing and give you direct updates on latest security risks. In vast majority of the industrial IoT applications produce terabytes of data only in few hours. Imagine, how much data those devices will generate over a period of time. In one month it could go up to tens of zeta bytes of data. Manually analysing this kind of voluminous data is not possible and even if it is possible it involves a lot of human errors. Automation is done only to reduce the human error, however not using Machine Learning can create such error even in fully automated industrial systems since the volume of data generated is huge.
Pipelining the data
As discussed above IoT systems are mostly responsible for security and risk detection systems in fully automated industries. However, whenever an IoT platform detects a threat it should act on it almost immediately. With this kind of data generated every hour, it is nearly impossible to deal with all the data in a queue. Therefore, pipelining technique called Hot Path Analysis, makes the process seamless by parallelly analysing multiple threads of data. This accelerates the process of detecting and acting upon threats much faster. These IoT systems are required to be near real-time to take actionable decisions. Imagine this whole process with the help of Machine Learning. Your IoT systems will be intelligent enough to predict the trends of security and risk patterns and alert you beforehand, so that you can do something about it. Acting in near real-time is one game and predicting what would happen and take action before it happens is a totally another game.
Which Industries are benefited by Machine Learning in Industrial IoT
By the intervention of Machine Learning in Industrial IoT, mainly Healthcare, Stock market, Financial firms, Nuclear plants, automobile manufacturing industries, or any industry which is more prone to security risks. In these industries you really can’t do anything after knowing about the issue just after it happened. In some cases containing the risk will be a very difficult task. For example, the data from the patient’s wrist band constantly sends the pulse and other types medically relevant data over to the hospital, but your data processing system fails to recognize the error in time, that is a situation any healthcare service does not want to get in. In a matter of seconds, the situation may accelerate to the patient’s life or death. If the situation is predicted much before then the patient can take preventive measures. These are just a few ways in which we can integrate Machine Learning in Industrial IoT.