The Internet of Things has become an integral part, transforming our reality into a more convenient place to live. Focusing on analytics is necessary to improve productivity and efficiency to survive in business among competitors. According to the research, there will be more than 55 billion IoT devices by 2025, and machine learning is now integrated with most industrial IoT platforms, such as Microsoft Azure IoT, Amazon AWS IoT or Google. Cloud IoT Edge.
The combination of Machine Learning in IoT is interpreting data from different sources, and changing industries and companies’ relationships with their customers. It transforming business models to move companies from simply producing products and services to those companies that can deliver the results they want.
Amazon CEO Jeff Bezos described the power of Machine Learning: “Over the past decades, computers have automated many processes that programmers could describe through precise rules and algorithms. Today’s ML techniques allow us to do the same with tasks for which it is much harder to set clear rules.”
While the IoT deals with devices interacting over the Internet, ML forces devices to learn from their data and experiences. This blog will explain why ML and IoT should work together.
Why Is Machine Learning Valuable?
Data is the lifeblood of business. Data-driven decisions make the difference between keeping up with competitors or falling further behind. Machine Learning will then be the key to unlocking the value of enterprise data and making decisions to stay ahead of the competition. You can quickly build models to analyze large and more complex data. And then provide fast, accurate results regardless of scale. This gives you a better chance to identify company opportunities and avoid risks.
Data models in traditional analytics are static and have limited use for rapidly changing and unstructured information. Such analytics requires a model based on past information and general evaluation to establish the relationship between factors. Machine learning starts with outcome factors and then naturally searches for indicator factors and their associations. ML is valuable when you understand what you need but don’t present the most relevant information factors to choose from. So you give the Machine Learning algorithm a goal, then it “learns” from the information what factors are relevant to achieve that goal.
The practical application of ML allows achieving business results that affect the net profit of the company. Industries that depend on big data use Machine Learning as the best way to build models, develop strategy and planning. Startups and large companies alike are already using new technologies to unlock the full potential of the Internet of Things. Oracle, Microsoft, Amazon, and others have begun to consolidate AI capabilities into IoT applications.
Why use Machine Learning for IoT?
ML can be applied when the desired outcome is known, so-called supervised learning. Or the data are not known in advance – unsupervised learning. Or when learning is the result of interaction between the model and the environment – reinforcement learning. The principle is to automate the creation of analytical models for continuous learning algorithms using available data.
So, there are reasons why Machine Learning is appropriate for the IoT. One of them has to do with the volume of data and automation capabilities. The second has to do with predictive analytics. Machine learning helps find patterns in IoT data by analyzing it with sophisticated algorithms. ML inference can supplement or replace manual processes with automated ones that use statistically derived actions in important processes.
As for data volume automation, it is a systematic process of checking, transforming, modeling data to find valuable information, offer conclusions, and support decision-making for further analysis.
Take automotive sensors, for example. When a car moves, sensors record thousands of data points, processing them in real-time to prevent accidents and ensure passenger comfort. With ML, the vehicle’s central computer can learn about dangerous situations for the driver, such as high speed, and activate safety systems on the spot.
An important part of machine learning for the IoT is that it can recognize default values, irregular actions, and trigger necessary alerts. For example, Google has used ML for its HVAC system to reduce energy consumption. Plus you can create models that accurately anticipate future events by recognizing the components that lead to a particular outcome.
There is a very high chance of failure when an IoT system depends on human factors. IoT needs Machine Learning support to become a perfectly matched system.
Benefits of Machine Learning for IoT
The Internet of Things offers businesses and consumers a myriad of benefits. Let’s present the most popular ones for businesses:
There Machine Learning helps businesses understand and predict the range of risks and automate operational responses. Which allows them to better manage cyber threats, financial losses, or prevent them altogether and keep employees safe.
Introducing ML into IoT applications will improve efficiency. Machine Learning processes data streams and identifies patterns. It is also possible to predict operating conditions and identify parameters that can be changed to provide better results in a short period. Enterprises are already investing in such technologies to improve productivity.
Improved customer relationships
When implemented correctly, ML helps improve the customer experience. Businesses use technology to collect real-time customer data and then develop products and services that meet customer needs. Businesses are also automating the process of organizing data to provide the customer with a quick and relevant answer to a question.
New and improved products and services
Companies can create new products/services or improve existing ones, allowing businesses to process and analyze data quickly. This addition to an IoT deployment improves offerings and operations and provides a competitive advantage in business productivity.
Increased scalability of the Internet of Things
Internet of Things devices start from mobile devices to low-level sensors. The most common IoT ecosystem includes low-level sensors that provide data streams. An AI-based ecosystem analyzes and summarizes data from one device before transmitting it to other devices. It reduces large amounts of data to a manageable level and allows a large number of IoT devices to be connected.
Machine Learning and IoT: Challenges
Implementing a Machine Learning algorithm to work successfully in an industrial environment цршср will produce excellent results is quite difficult. There are several unique aspects of IoT data that make it difficult to use. Problems include, for example, choosing the right algorithm to implement ML, where the wrong choice can lead to garbage after months of effort; choosing the right data set, or pre-processing it.
There is often noise due to errors both during data collection and during transmission. IoT data is highly volatile, because of the enormous inconsistency in the data flow between the various components and because of the existence of temporal patterns. Also, the value of the data itself depends largely on the underlying mechanisms, the frequency of collection, and its processing. Even if data from a certain device is considered reliable, one must remember that different devices may behave differently even under the same conditions. It is impossible to account for all possible scenarios when collecting training data.
Difficulties in processing large amounts of data
Machine Learning helps the Internet of Things and is the key to analyzing the vast amounts of data coming from constantly operating IoT networks. ML systems recognize normal data flow patterns and focus on patterns outside the norm. This is how ML can separate signal from noise in huge data streams and allows organizations to focus on what matters.
The truth is that ML algorithms must then continuously perform calculations on a huge scale in milliseconds. Such computing puts pressure on conventional data center processors and computing platforms.
Therefore, systems need processors with multiple integrated cores, fast memory subsystems, and architectures that can parallelize processing for next-generation analytical intelligence to operate at huge real-time scales. These are platforms with built-in analytical processing mechanisms, as well as the ability to run complex algorithms in memory for real-time results and immediate application of analytical data. Machine Learning platforms are becoming increasingly necessary, and businesses are increasingly basing their success on knowledge discovered in machine-to-machine interactions.
As connected device technology extends the existing Internet, the data generated is very universal and causes privacy concerns. About two-thirds of IoT devices are in the consumer space, and how personal some of the shared data can be is easy to see why. Users are demanding safeguards to protect their data. A Thales Group report found that 90% of consumers don’t believe IoT devices are safe. And about 63% of users find these devices “creepy.” With data breaches on the rise, users are increasingly concerned about whether their data is being misused.
Applications in IoT
This is one industry where new technologies like the Internet of Things, ML, and AI are already being used. Robots in factories are getting smarter with the support of implanted sensors that make data transfer easier. Robots are also equipped with artificial intelligence algorithms and are learning from new data. This saves time and money and improves the production process.
Tesla cars are probably the best example of the Internet of Things and AI working together. With it, unmanned cars predict the behavior of pedestrians and maps in different circumstances. They can determine road conditions, optimal speed, weather, etc. Then they get smarter with each trip by learning from the received data.
IoT devices like smart wristbands are being used in healthcare to track patient data and send it to a local hospital database. Cloud-based repositories in which vast amounts of data can be stored and exchanged can be used to keep such information current and relevant. Many devices can track heartbeat and blood pressure by entering physical parameters such as height and weight.
Retail analytics includes multiple data points from cameras and sensors to monitor customer movements. They predict when customers will reach the cash register. In this way, the system can offer dynamic staffing levels to reduce checkout time and improve cashier productivity.
Examples of use cases abound. What we have described is only a small fraction of them. But they are all valuable applications of machine learning in the IoT right now.
IoT services bring the Internet closer to users and touch all aspects of life, enabling the collection of contextual and personal data. Collected IoT data tells the life story of its users and makes it more accessible to understand people’s needs and preferences. Using Machine Learning for the Internet of Things is therefore becoming the ideal solution for creating personalized, customized applications.