Data overload! So many IoT applications are generating a large amount of data, so that alone can not rely on human analysis and utilization of real-time.
In the IoT system response, data scientists identify patterns and define rules by analyzing large amounts of data. In spite of the vicissitudes of the world, the emergence of new factors has always been affecting the right actions. How you can make sure your IoT system is still able to respond optimally in a changing environment.
"Machine learning gives computers a lot of learning ability to create algorithms, learn algorithms and make predictions from data, without having to show program instructions," says Arthur Samuel.
Defining a rule for a simple IoT application - such as turning the engine off when the temperature is too high - is a straightforward rule. However, it is very difficult to determine the correlation between the inputs of multiple sensors and external factors. Now consider only one scenario where you have to decide when to send a truck to replenish the vending machine's merchandise, based on sensor data for vending machine sales reports, inventory levels, local weather forecasts, local events, and promotional ads. Judging the wrong time or sending the wrong product, you will fail the sales because you did not replenish the vending machine's volume with the right amount of correct product.
Most leading IoT platforms, including Azure, IBM Watson, Splunk, AWS, and Google, now offer machine learning capabilities. This allows the IoT system to analyze sensor data, look for relevance, and make the best response. The system constantly observes whether its predictions are accurate or not while constantly refining its own training algorithms. Currently, there are two main types of machine learning methods:
Supervised learning. It refers to the development of an algorithm based on a set of examples. For example, a simple use case might be a daily sales record for a product. The algorithm calculates a correlation between how many of each product may be sold during the day. This information helps determine when to transfer the truck to replenish the vending machine.
Unsupervised learning does not provide labels (such as sales / day), the system needs its own analysis. Instead, it provides all the data that is useful for analysis and allows the system to proactively identify less obvious correlations. For example, price discounts, local events, and the weather affect the vending machine sales. Therefore, when determining the replenishment schedule These factors need to be taken into account.
Many companies are beginning to manually define business rules for their IoT systems. Then, as they gather more data and information about external influences, they begin to add rules based on machine learning.
Machine Learning System Resources:
Microsoft Azure
IBM Watson
Google TensorFlow
Splunk
If you think applying the theory of machine learning to the Internet of Things is more advanced, check out the latest Kaytranada video to see what the machine can learn in a single day!
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