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The need for digital transformation in the industry and the related development of the Internet of Things (IoT) has led to the rapid development of various communication technologies and approaches for the implementation of flexible and intelligent communication networks. The main direction is wireless technologies, which make it possible to accelerate the wide range of new applications and services related to smart cities, industry, e-government and others. The possibilities are wide - use of different licensed or unlicensed frequency bands, channel bandwidths, modulation formats, power efficiency, reliability and security. Different network topologies could be applied for the access and collection of data from the sensor networks - star topology, mesh topology, tree topology, each of which has different advantages and disadvantages. This paper discusses the possibilities for deploying low-power mesh networks in an indoor scenario based on IQRF technology. Important communication parameters, approach advantages, and technological limitations will be highlighted. As an example, a study of network behaviour and efficiency in an indoor scenario will be considered.

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In this paper the structures of Adaptive Neuro-fuzzy interface system (ANFIS) are studied for noise identification. The system's structures are analyzed for different types of membership functions applied for input variables with root mean square errors variation. Hybrid algorithm and back propagation algorithm are applied. The input data are obtained through system simulation based on LabVIEW system design platform and development environment. The choice of ANFIS structure is based on the training results and minimum RMSE for identification of the signals with uniform white and inverse F Noises. Therefore, "gbellmf" membership function for input data variables is chosen. The accuracy classification is obtained at 100 %.