Applying machine learning for IoT data analysis

Machine learning and the internet of things are the trending technologies in today’s digital age. They have significantly revolutionized the way we live and work. Their applications are evolving rapidly across diverse industries ranging from powering digital assistants to eliminating human errors and automation. Integrating machine learning into an industrial operation can eradicate human errors. It can enable big data to generate real-time insights and allow IoT devices to reach their full potential. Essentially, using machine learning for IoT solves data analysis challenges and brings automation opportunities. 

Capabilities of Machine Learning and IoT

Machine learning mimics the way the human brain processes inputs to create logical responses. It takes more information and generates meaningful insights for business growth. Machine learning is an advanced subset of artificial intelligence. It is beneficial in cases where the desired outcome is known, or the data is unknown beforehand. By applying ML algorithms, businesses can solve many problems such as classification, regression, and clustering.

The internet of things is a system of internet-connected objects that communicate wirelessly. An IoT data platform provides connectivity, control, device management, and meaningful and valuable data. Despite this, IoT has a set of challenges including security and privacy, accuracy, big data, and interconnectivity.   

Why Machine Learning for IoT?

As IoT produces a voluminous amount of data via millions of devices, machine learning derives insight from it. ML algorithms can identify and analyze hidden patterns in data generated by IoT devices. These algorithms can help businesses ingest and transform data into a consistent format. Using machine learning for IoT also helps build an ML model and allows companies to deploy this model on the cloud, edge, and device.

Machine learning for IoT allows the automation of industrial processes. It can assist workers to stay away from perilous areas by using IoT. Machine learning enables data analysis automation. For instance, sensors in a car record thousands of data points that require to be processed in real-time to avoid accidents and provide comfort to passengers. This might be a challenging job for a human analyst to perform such tasks. Automation can perform so easily.

Combining machine learning and IoT enables efficient risk management. Machine learning can envisage risks by using past data and automate responses to these risks. Applying machine learning for IoT can spot outliers and atypical activities and alarm for a relevant response. As it will learn more and more about a phenomenon, it will become more exact and efficient.

Machine learning can be constructive in securing the internet of things in smart homes. As more and more homes are adopting connected devices, security is a paramount concern. According to Statista, there will be 50 billion IoT devices in use worldwide by 2030. These devices will create an enormous web of interconnected devices spanning everything from smartphones to kitchen appliances. Network-based solutions, instead of using per-device security, can be effective in protecting IoT devices. This means users can use only those devices that are registered and allowed to access a network.

So, as machine learning for IoT is already changing the way we interpret data and use connected devices, it is expected to see more technological advancements in this field.