What is the place of machine learning in IoT？

There has been lively discussion about the opportunities and threats of technologies such as big data and artificial intelligence, and concerns about their future. At the same time, the company hopes to increase efficiency and reduce costs by installing more and more sensors. However, without proper data management and analysis strategies, these techniques will just create more noise and fill up more servers, according to machine learning consultants at InData Labs. without realizing their actual potential. Is there a way to convert simple sensor recordings into actionable industry insights?
The simple answer is yes, and it lies in machine learning (ML).
machine learning capability
The scope of ML is to mimic the way the human brain processes input to produce a logical response. If people rely on learning, training, or experience, machines need an algorithm. Furthermore, as each of us increases our store of knowledge, we adjust our responses, become more proficient and begin to apply it selectively. Replicating this self-regulating behavior in machines is the endpoint of ML development. конвертер серийного номера в wifi
To facilitate learning, the computer will display raw data and try to make more sense. As it develops, it becomes more experienced, producing more and more complex feedback.
IoT Challenges
In the broad realm of the Internet of Things (IoT), we can find everything from smartphones to smart refrigerators to sensors that monitor industrial processes.
However, there are at least four fundamental issues related to IoT implementation that need to be addressed:
Security and Privacy: Any algorithm that handles this type of data needs to embed some way to keep all communications secure, especially when we're talking about personal data collected by like medical sensors.
Accuracy of operation: Under harsh conditions, the implemented sensors may send wrong data, or no data, corrupting the algorithm.
The 3v's of Big Data: Most IoT devices generate what can be categorized as Big Data because it examines the 3v's: Volume, Velocity and Variation. Solving a 3v problem means finding the best algorithm for the type of data you're working with, and the problem you're trying to solve.
Connectivity: The value of IoT lies in enabling disconnected objects and tools to "talk" to each other. However, since these are all created in different ways, they need to use a common language, which is often the least common denominator. How would your refrigerator communicate with your coffee maker if your computer already had TCP/IP?
Why Use Machine Learning for IoT?
Machine learning is an appropriate solution in the IoT space for at least two main reasons. The first is data volume and automation opportunities. The second has to do with predictive analytics.
Data Analysis Automation
Let's take automotive sensors as an example. As the car moves, sensors record thousands of data points that need to be processed in real time to prevent accidents and provide comfort for passengers. Human analysts cannot perform such a task for every vehicle, so automation is the only solution.
Through machine learning, the vehicle's central computer can learn about dangerous situations, such as speed and friction parameters, that could be harmful to the driver, and employ safety systems on the spot.
The predictive power of ML
Going back to the car example, the real power of IoT lies not just in detecting immediate dangers, but in recognizing more pervasive patterns. For example, the system can learn about a driver who has tight turns or has difficulty parallel parking, and can assist him or her by providing additional guidance on these matters.
The most useful feature of ML for IoT is that it can detect outliers and anomalous activity and trigger necessary red flags. As it learns more about a phenomenon, it becomes more accurate and effective. A good example is what Google has done with its HVAC system, significantly reducing energy consumption.
Last but not least, there is also the opportunity to create models that can predict future events with great accuracy by identifying the factors that lead to a particular outcome. This provides an opportunity to play with the input and control the outcome.
How should it work?
It is crucial to understand that when an IoT system relies on human input, it can fail. It needs the support of machine learning to be a fully consistent system that is resistant to human error.
In an interconnected world, human errors are quickly corrected by algorithms. This helps optimize the entire process through a feedback mechanism. The predictive component of the system identifies the correct input to obtain the expected output. TCP-шлюз modbus
When powered by ML, IoT can work flawlessly on an individual level so that you don't mess up your morning routine on, say, a collective level. The latter scenario can be illustrated by interconnected cars that can communicate with each other and perform dynamic rerouting to avoid traffic jams.
From big data to smart data
The “smarter, harder” advice is perfect for managing IoT-generated data and turning it into useful insights. Although big data is all about overcoming the challenges brought by 3v, smart data can refer to:
Clean sensor data on-site before sending it to the cloud for analysis
Preprocessed batches of sensor information, ready to be turned into actionable insights
In both cases, the added value of machine learning is that it takes intelligent data and makes ML models work faster and more accurately.