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Vol. 13 No. 1 (2023): JITA - APEIRON

Almir Ahmetspahic, Goran Popovic, Goran Djukanovic

Linear Wireless Sensor Networks as the Physical Layer of Smart Street Parking Systems

Original scientific paper

Abstract

Wireless sensor networks (WSN) represent a set of different technologies that, in cooperation with each other, form the basis for the realization of the physical layer of the concept of smart cities. Miniaturization of sensor devices and decreasing energy consumption, both for data processing and for mutual communication, as well as simple implementation and low cost, make WSN indispensable for a large number of different applications. Many of the applications imply a linear infrastructure that is subject to monitoring, and as such requires a linear deployment of sensor nodes. This form of WSN represents a special class of networks that we call Linear Wireless Sensor Networks LWSN. In this paper, we will describe the characteristics of these networks, the problems that are specific, as well as possible applications, and we will pay special attention to the application of LWSN in smart street parking lots.

Keywords: IoT, LWSN, Smart City, Street Parking, WSN

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.