Future of Internet of Things (IoT) Depends on Machine Learning of Big Data
Mahesh Kashyap
Building Tech for Investment Management Industry | CEO & Co-Founder @ OVTLYR | Stocks & Options Trader
The internet of things has opened up a new frontier for innovation. Through the application of smart connected devices, sensors and gateways, IoT is changing agriculture, automotive, consumer electronics, healthcare, logistics, retail, telecom and more.
In the world of IoT, car sensors will gather fuel, speed and usage data. Our healthcare providers will collect real-time health vitals. Industries will track production, workloads, and maintenance. All of these sounds great. However, there is the problem of handling this massive amount of data.
IoT Generated Big Data
All the data collected from IoT devices can become a challenge for IT infrastructure. By 2020, IoT generated data will be 600 ZB per year. IoT and related data will account for 22 percent of the business work loads on the cloud.
The sheer volume of data is overwhelming. Current business analytics can’t handle it. Without the ability to analyze it, there will be no valuable insights gained from the collected data. Also, IoT devices need the analysis to work with each other.
Relationship of Big Data and Machine Learning
Traditional analytics is still human dependent. Even when the analysis is performed on computers, it is limited by the thinking of the expert who is running the calculations. Also, there are limits to how deep humans can think when dealing with massive amount of data.
Machine learning can be the savior in dealing with big data analytics. It doesn’t have the same human limitations. It can run through calculations faster. It doesn’t get tired. It can delve deep into the datasets and emerge with valuable insights.
Dependencies of IoT on Machine Learning
IoT gathered information can be passed to machine learning algorithms to gain better insights. Machine learning can provide analytics in pattern anomaly detection, predictive analytics, and prescriptive analytics.
Car sensors detecting loss of tire pressure would be pattern anomaly detection. Noticing usage to determine when tires need to be changed would be predictive analytics. Recommendation to not use certain roads because it’s bad for the tires would be an example of prescriptive analytics.
Machine learning can be used for more complicated detections. If a healthcare system collects remote health data of sick patients living near a certain river, the healthcare professionals can easily miss any environmental connections. Because the river is not the focal point, location based analysis will not notice this pattern. However, machine learning algorithms can go through a deep analysis of the data and recognize that all the patients with similar symptoms live near the bank of the river. So machine learning can use the IoT information to create concrete analysis.
Conclusion
IoT generated data can help us figure out solutions to complicated problems. It will give us access to huge amount of information about our environments and our bodies that can be translated into insight using machine learning. Through the use of pattern anomaly detection, predictive analytics and prescriptive analytics of machine learning algorithms, we can totally transform our lives. We should set up the infrastructure to handle the volume, velocity, and variety of IoT-produced data.