Monitoring the operating parameters of power grids is extremely important for their proper functioning as well as for ensuring the security of the entire infrastructure. As the idea of the Internet of Things becomes more ubiquitous, there are tools for monitoring the state of the complex electrical grid and means to control it. There are also developed new measuring devices and transmission technologies allowing for the transfer of performed measurements from many places to the network management center. However, there are still no devices that act as data concentrators, which would integrate many transmission technologies and protocols in one device, supporting the communication between those different transmission technologies and which would realize edge computing to assist the management center by prioritizing and combining transmitted data. In this article, the authors present a device that meets the above-mentioned requirements. There are presented research results leading to the development of a decision algorithm, called Multilink—ML, dedicated to the presented device. This algorithm enables the selection between LTE and NB-IoT interfaces for packet transmission without the need to burden the communication system with additional transmissions.
This paper presents an innovative method of locating airplanes, which uses only voice communication between an air traffic controller and the pilot of an aircraft. The proposed method is described in detail along with its practical implementation in the form of a technology demonstrator (proof of concept), included in the voice communication system (VCS). A complete analysis of the performance of the developed method is presented, including the results of simulation and measurement tests in real conditions. The obtained results are very optimistic and indicate that the proposed solution may constitute an alternative method of locating aircraft in emergency conditions, i.e., a backup solution in the case of failure of other positioning systems.
Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.
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