JITA

JITA Journal of Information Technology and Applications

Vol. 10 No. 2 (2020): JITA - APEIRON

Goran Djukanovic, Goran Popovic, Dimitris Kanellopoulos

Scaling complexity comparison of an ACO-based routing algorithm used as an IoT network core

Original scientific paper

DOI:https://doi.org/10.7251/JIT2002073DJ

Abstract

This paper proposes a routing method that is based on an Ant Colony Algorithm (ACO) for minimizing energy consumption in Wireless Sensor Networks (WSNs). The routing method is used as the backbone of the Internet of Things (IoT) platform. It also considers the critical design issues of a WSN, such as the energy constraint of sensor nodes, network load balancing, and sensor density in the field. Special attention is paid to the impact of network scaling on the performance of the ACO-based routing algorithm.

Keywords: ant colony algorithm (ACO), energy consumption, internet of things (IoT), network lifetime, optimal path, wireless sensor network (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.