## 1 Introduction  
The implementation of 5G technology is getting closer and closer. In selected cities around the world, action tests are already being carried out in terms of speed or the number of connected devices in a specific area. The selection of parameters during testing is important because thanks to them, it is possible to create a real Internet of Things. The word _real_ is important in the context of connecting and allowing sending data between a huge number of devices. This is especially important for intelligent cars, which should be able to exchange information with passing objects, e.g., lanterns. However, it is easier to write than to do. The reason for this is the amount of transferred data (in the case of images from cars cameras, the amount of data can be huge) or the number of objects wanting to send data in a small area (an example is a traffic jam). Using the 5 th generation mobile technology network, both problems should be solved. It is worth noting that the amount of data transmitted even in 5G network can be very burdensome for servers. Parallel recording of very large amounts of data can cause discs to jam. In addition, it will need enormous computing power to process this information. This is particularly important in the case of using artificial intelligence techniques, which learning process depends on the quality and amount of samples. And it can last up to several months. And practical application forces constant training of such solutions to enable their development because more and new data are downloaded. Another problem is overfitting. Imagine a situation that data flows to the server all the time for a few days. At this time, they will be processed, and then the classifier will be trained with them. After a long period, the required effectiveness will be achieved, but there might be a problem of overtraining. As a result, we could have automatic decisions that should not necessarily take place and may endanger people's lives. The solutions to such problems can be found under the name _edge artificial intelligence_, where it is understood primarily as performing the required operations on the local equipment, and not somewhere outside, an example of which is the server. The use of this type of technology ensures data security, as they are not distributed anywhere else than on their own. In addition, data processing can be performed in the background, regardless of other actions. Consequently, additional elements, such as training, can be repeated using more and more new data in the background with a predefined load of hardware power. This is important because of the basic operation of the device. This is especially important on the Internet of Things where the user can use one of the devices. An example is a smartwatch that records pressure, body temperature or even the outside temperature. This data can be very different if the environment is changed. The use of artificial intelligence in this case may require overtraining of the used classifier to best adapt to the user's needs. And this can be done locally, while continuing to use the device. An additional advantage will be the ability to do this without using the Internet, because there are situations when the user has no access to it. Of course, such a solution brings with it some disadvantages. The greatest of them is the computing power on given devices, because the application of artificial intelligence will require it. This problem can be solved by various methods. From the use of better technical components to implementation solutions. The mentioned implementation issue is a more interesting solution that does not require additional financial expenses. However, this requires the creation of supervisory mechanisms that enable parallel execution of two threads, as well communication and possible exchange of information between user's devices in the local network. As a local network, we understand several devices that can work together and belong to a single user.