How We Can Use Machine Learning Technology for IoT Analysis?

The modernization in this world is going on about at an epic rate. With technology growing at such a massive scale, the world is soon bound to be like a scene from “The Jetsons.” The two most exciting aspects of this revolution are IoT and machine learning.

These two are the dream faces of technology. Something that has always fascinated the humans and seemed too farfetched now seems within grasp. This ideology is what leads to such rapid advancements in these fields.

However, with IoT being popular at such a massive scale on rising of concern for the netizens is the privacy or the lack of it thereof it poses. With the whole world gratifyingly accepting IoT and making it an integral part of their lives, managing the data it produces is a sure hurdle.

With this industry growing at such a massive rate, according to the estimations, the amount of IoT devices by 2022 will be around 50 billion. With such an impressive number of devices being connected in the form of a network, the amount of data exchange is also humongous.

The data which is generated from this network is now challenging to analyze if done through the mainstream ways. Especially with a massive shortage, of data analysts, the only way out there is to put in machine learning to use.

Machine learning and the modern world

Machine learning is one of the most essential and crucial parts of technology being devised today. All around the world the scientist and the engineers are working up ways to make the machines smarter. This is done so by engineering ways to replicate the human learning and cognition process for devices.

The human brain is by far the most potent computer if viewed from a tech enthusiast eye. The amount of information it collects imparts and replicates is indeed impressive.

The stimulus or signal is received to the human muscles which are then intercepted and translated by the brain. The brain then decides what the output or the actions of the message are. The fact that this whole process occurs within seconds is indeed fascinating and is what the engineers are trying to work up.  

In humans, once the brain receives a signal, there are high chances that it will cause a predictable result. This because the human brain is always on its process of growth and evolution- a process termed as learning.

Now in humans learning occurs through evolution or through feedbacks. In machines, however, learning is referred to as the algorithms on which the computer runs updates independently on what the outcome of the input will be.

In machine learning what the scientist is trying to achieve is to allow the algorithm to automatically update itself and provide an outcome or feedback. In short, the algorithm is provided with the raw data and the objective, and it would have to derive the results itself.

Now the working of machine learning is evident, let’s have an outlook on its role in IoT.

Machine learning and IoT

As aforementioned the field of IoT is rapidly moving towards advancements. Due to this, the number of devices being connected to a single network makes the data handling a difficult task to manage. However, machine learning can help implement the following methods to achieve data analysis.

1.     Data analysis automation

As data analysis is the main issue in advancements of IoT, machine learning can help automatize it. This would cut down the time taken for each of the human data analysts going through the data manually. The machine algorithm would itself go through it, in search of patterns and inconsistencies.

With the proper use of machine learning new ways of data analysis can be implemented. The algorithms can be designed or taught give the desired outcome if it is not present in the given factors and variables

2.     Predictive Analysis

The algorithms in machine learning are designed to adapt and evolve. With continuous learning, they can recognize regular patterns and update themselves accordingly. This would allow them to predict the future outcome. Instead it is a positive or a negative one.

Predictive analysis is basically all about supervised learning. This means that the machine makes predictions based on the previous data based on statistics.

Similarly, a machine while working is under the close supervision of an engineer. This supervisor adds up the relevant or raw information within it. The algorithm then responds by producing a result from a formula It derived itself.

This seemingly cognitive process gives the machine a liability to recognize irregularities that may have been overlooked or may have consumed a lot of time if in the hands of a human data analyst.

Moreover, apart from playing a significant role in identifying abnormal behavior machine learning aids in other things too. It selects, analyzes, sorts and associates a large amount of data and make predictions of the future and thus help in setting up trends.

3.     Prescriptive analysis

Apart from being apt in making predictions, machine learning can also be prescriptive. This means that as efficient it is in finding problems, it also works on solving these problems.

That is by predicting futures from simple raw data, they can also contribute to making the IoT devices better and efficient. They could assist in finding out the flaws in an IoT network and then come up with the changes that can help overcome them.

To simplify it, machine learning can help sand off the rough edges to get the smooth desired product. 

There are numerous examples of how prescriptive analysis in machine learning s being used. One such example is Google’s self-driving cars which can predict when pedestrians are about to cross the road just by their body movements.

However, the most famous example of this is Google’s cooling systems. In this event, the company’s engineers came up with almost 120 sensors that would someway affect the cooling system. After that, the machine learning algorithm was left to draw out analysis.

The machine responsible for the analysis was able to produce a model of the cooling system that reduced Google’s cooling expenses by a whopping 40 percent. This was indeed a task that was only achievable by a machine.

Why combine the two?

Although machine learning is by far a very vast field it still needs to grow. The algorithms present still require the assistance of humans.  The system is particularly useful by undergoing continuous corrections and supervision. However, there is no denying that fact that machine learning can be an effective tool to help evolve the field of IoT.