How to use Machine Learning for IoT Analysis - ReadWrite

How to use Machine Learning for IoT Analysis – ReadWrite


Machine Learning and the Internet of Things (IoT) have been the buzzwords for the decade. These technologies find application in almost all industries, from enabling artificially intelligent powered digital assistants to the supply chain’s automation. They have revolutionized not only how we interact on social media but also how we pay the bills. Here is how to use Machine Learning for IoT Analysis.

Looking at the google trends analysis below, one can be sure that these technologies offer a lucrative career, so many people are interested in learning about them.

You already know what Machine Learning and IoT are.

Machine Learning is the process of getting computers to learn and act as humans & automatically improve with experience, without explicitly programming it. On the other contrary, the Internet of Things refers to a system of internet-connected objects that can communicate over wireless networks.

Now, it is exciting to note that the base of both these technologies is ‘Data.’ IoT devices generate a lot of data, which may seem useless to us, but this is where the role of Machine Learning comes into the picture.

How Can Machine Learning be Applied to IoT?

Talking about data analytics, Predictive and Prescriptive Analytics both utilize machine learning and find application in the world of IoT.

  • Predictive Analytics uses different statistical and Machine Learning Models to predict future outcomes based on past data.
  • For example, in smart lighting systems, the sensors can collect information about illuminance, movement of people and vehicles and public transport schedule, time of the day, year, etc. Based on the data received coupled with the historical data, the Machine Learning Algorithms can predict the appropriate lighting based on the conditions & this will enable the city administration to cut down their electricity costs.
  • Prescriptive Analytics uses a combination of business rules, computational modeling, and Machine Learning to roll out individual recommendations to a user for any pre-specified outcome.
  • SmartWatch using a wide range of sensors is an example of Prescriptive Analytics. The watch would record all your information and utilize machine learning models to roll out individual recommendations for you and alert you when it finds an abnormality in the reading.

Tesla Vehicles have always been in the news and even more so now. Probably it is a dream car for many of us. It is a pioneer in technology & they also have rolled out the concept of ‘Self Driving Mode’ on a pilot basis in some of their vehicles.

READ ALSO  Computer Science vs. Data Science: Decoding Your Ideal Career Path

Have you ever imagined how these Self Driving Cars work? These vehicles have many sensors like lidars, radars, cameras, IoT devices that communicate with each other and send out the data in the form of images and numerical values to a dedicated server.

Based on the data received, various Deep Learning models like Convolutional Neural Network and VGG16 are applied to make the car learn automatically and improve over-time with experience.

Benefits of Using Machine Learning for IoT Data Analysis

  1. Machine Learning can be used to identify patterns in data and make real-time predictions. For example, it can help create a better user experience when coupled with appliances like Air Conditioning. The machine learning models can learn from the past data at what temperatures you are more comfortable with.

It can automatically optimize the room temperature according to your requirements when returning home from work by utilizing past data and current temperature.

2. Machine Learning and IoT can automate some industrial processes and ensure worker safety in hazardous areas by using IoT, and Machine Learning enabled instruments to monitor and optimize processes.

3. IoT Analysis helps in taking cost-saving measures in Industrial Applications. We are now done with the old school concept of ‘Scheduled Maintenance,’ and we are now looking forward to reducing the surprise downtime using Predictive Maintenance.

The problem with Scheduled Maintenance is that the production halts when the machine breaks down, resulting in a substantial revenue loss. It is also possible that while doing the maintenance, some parts which were working perfectly before were removed and exchanged with the new parts. It results in an overhead expenditure which no business person would find suitable in the right mind. This is where IoT is looking to cut the cost of Industrial Applications.

Sensors

Modern machines now use sensors that monitor a wide variety of data, including usage, uptime, energy consumption, and a log of system disruptions. In case of a problem, t the historical data coupled with the predictive analysis done by Machine Learning Models notifies the concerned person about the life cycle of the component and how the quality of the production due to the faulty component.

4. IoT and Machine Learning together can help in efficient Risk Management. Machine learning can be used to predict risks by utilizing past data and automate responses to this risk.

5. You can achieve process efficiency by utilizing Machine Learning along with IoT. Machine Learning models can optimize a process to maintain the desired output utilizing data from the past to adjust parameters in real-time. For example, In the case of a Smart Traffic Management System, CCTV Cameras fitted on the top of traffic lights can capture real-time images and, based on the Algorithm it is trained on, can detect whether a road is congested or not.

READ ALSO  Next Cyber Security Threats will Include Machine Learning and More Malicious Ransomware

At the same time, this information can be intimated to the citizen and suggest a better route to reach their destination.

An automated robot arm machine in a smart industrial factory with tablet real-time process control monitoring system application. Source – Canva.com

Security

The grass is always greener on the other side. While we have talked a lot about IoT’s advantages and how fantastic it is, there is a definite question mark in the form of its security.

A report published by Thales Group, one of the leaders in Cyber Security, says that 90% of the consumers lack confidence in IoT Devices’ security. Moreover, about 63% of the users from the developed world have termed these devices as ‘creepy.’ With increasing Data Breach cases reported now and then, the end-users are even more worried about whether their data is misused or not.

IoT Devices contain a lot of personal information and even the slightest breach might mean all your data is compromised. Therefore, there is an ever-increasing need to make these smart devices even more secure.

The first step for any IoT business is to undergo a thorough security risk assessment that examines vulnerabilities in devices and network systems and user and customer backend systems.

To address these security challenges, IoT devices and manufacturing companies should have a solid strategy.

Conclusion:

We have thus seen how the combination of ML & IoT is changing our lives and we expect to see some of the more technological advancements in this field. We also discussed the advantages and some challenges faced in implementing Machine Learning to IoT devices.

Soon, using IoT and ML, we might predict unfortunate events like train crashes and crimes even before they happen. These technologies are, for sure, opening the door to boundless opportunities.

Image Credit: andrew neel; pexels; thank you!

Katrine Osborne

Katrine Osborne is a head of the content department in an Artofvisualization. She is fond of technologies and programming and has experience working as a Data Scientist. She is a persistent traveler and dedicated to her family, work, and friends.



Source link

?
WP Twitter Auto Publish Powered By : XYZScripts.com