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Combining IoT and Machine Learning makes our future smarter
Introduction
The Internet of Things (IoT) is the network of embedded devices, smart devices, and computers infused with sensors that can communicate with each other as well as send and receive packets of data through the network. These devices can communicate with the real world through sensors and can control or move a system using actuators that are the heart of an IoT system.
Machine learning and IoT have a very association in the sense that many organizations using machine learning and Ai based applications rely on terabytes of data captured through IoT and embedded devices. This da can be used for data analysis, and predictive analysis, as well as create a smart world where few of the IoT devices connected to the network and internet can make their own decisions based on the type of AI algorithms running on them.
What is the idea behind combining IOT with Machine learning and AI?
Both IoT and Machine Learning have their strengths, advantages, and disadvantages. Combining both strengths to produce smarter devices is the need of the hour and is openly researched and developed by many big organizations today. Nowadays organizations have the capacity and efficiency of resources, computation power on SOCs, and the power of the internet to manufacture highly smart devices running efficient machine learning algorithms at the user's end and can have a variety of sensors capable of capturing diverse kinds of data.
The data thus received through IOT devices serves as fuel for the industries to do intense research, analysis, and development of AI models enabling them to make informed decisions. through this many such industries have skyrocketed their businesses. They can now provide better services with higher efficiency and accuracy and boost client satisfaction by combining IoT with Machine Learning.
Operating IOT and Machine Learning together
IoT devices having capable sensors produces a ton of data. This data is used to analyze trends and run powerful Machine learning algorithms that can predict and take useful decisions. The fuels innovation and research. Many of these algorithms can be packaged and deployed on IOT devices that can run inference and such devices can take decisions in real−time based on the input.
For example, a smart road traffic camera powered by AI and devices such as Raspberry Pi can run image detection/video feed detection models that can identify persons not wearing a helmet. Such technology can assist Traffic police in nabbing the guilty persons who breach traffic rules.
Another example can be algorithms and IoT devices that can assist farmers to maximize irrigation and utilization of fertilizer in real time which can increase harvest and reduce waste.
Advantages of IOT with Machine Learning
Increase in the productivity of businesses and organizations by automating several processes For example enabling predictive maintenance to set an alarm for repair of machine parts to reduce wear and tear and thus preventing machine failure.
Better data analysis and processing since IOT devices can generate a tremendous amount of data that acts as fuel for AI research and analysis. For example, organizations can derive useful insights from the data and take informed decisions.
It helps to reduce overall costs as it helps in preventing downtime in manufacturing and improves the efficiency of machines. Organizations can highly benefit from the integration.
What are the most powerful industries using IOT with Machine Learning?
In the Manufacturing industry, IOT with Machine Learning can automate production processes and predict machine downtime. It can help to mitigate issues and reduce operational and repair costs.
In Healthcare, valuable information about the state of patients received through smart gadgets like Fitness bands can produce valuable data highly useful to doctors and in general to patients where they can monitor their health conditions.
In Logistics and transportation, we can use real−time locational capturing devices powered by GPS that can do route optimization and thus reduce fuel usage.
They are used in Agriculture with smart irrigation devices that can assist farmers in producing a better yield of crops and also help in pest control.
Conclusion
A combination of IOT with Machine Learning is a hot and trending area of research and application that has its roots in every sector nowadays. As hardware and resource have become increasingly cheaper today together with easy access to the internet for every individual and industries are innovating and reaping the benefits of this association.
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