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Vol. 5 No. 2 (2018): JITA - APEIRON

Viktor Denkovski, Biljana Stojcevska, Toni Dovenski, Veno Pachovski, Adrijan Bozinovski

Performance Evaluation of Routing Protocols in a Wireless Sensor Network for Targeted Environment

Original scientific paper

DOI: https://doi.org/10.7251/JIT1502110D

Abstract

This paper investigates the performance of reactive and proactive routing protocols in a wireless sensor network for targeted enviroment. AODV and DSR are chosen as representatives for the reactive routing protocols and DSDV for the proactive. A wireless sensor network application for farm cattle monitoring is created. The proposed solution meets a desired requirement for periodically observing the condition of each individual animal, processing the gathered data and reporting it to the farmer. However, an implementation of a WSN needs to meet particular technical challenges before it can be suitable to be applied in cattle management. For this, multiple scenarios are presented with various situations to evaluate the performance of routing protocols in the WSNs. Finally, the results concerning data transportation from the mounted sensory devices to the mobile nodes are discussed and analyzed.

Keywords: wireless sensor network, herd management, cattle health monitoring, routing protocol, cattle monitoring application, mobile nodes

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.