Cognitive Positioning Technologies for IoT Network Devices

Nowadays, wireless technologies are increasingly being used for human needs. Increasingly, technologies are emerging that are used by people not only for the need to transfer data. One of these technologies is the Internet of Things, which often uses wireless sensors with ZigBee data transmission technology as end devices.There are areas that require deployment of these networks on the territory, a large number of sensors are required, which must with sufficient accuracy “know” their position on the deployment area. Usually, devices with built-in GPS modules are used for this, but devices containing this module are significantly more expensive than without it. And if in a large distributed network with many segments, more than 1000 such devices are required, then a device with a GPS module can only be at most one for each segment. Therefore, if this is a forest where there are many thousands of trees and it is necessary to monitor fires at the initial stage, which take place in many US states in the summer, then the cognitive task of teaching those devices, that do not contain a GPS module, to determine their position is relevant. This paper proposes a mathematical formulation of the cognitive task of learning to determine the coordinates of devices in wireless sensor networks. The study of the mathematical model has been carried out. The purpose of these studies was to find new alternative teaching methods for determining the distance between objects of IoT sensor networks, using the function of localizing objects where an emergency occurred.