Intelligent System of Limited Resource Allocation for Large-Scale Agent Systems

Jakub Węcławski , Stanisław Jankowski

Abstract

This paper describes an intelligent decision support system for semi-autonomous agents. The solution is developed for a large-scale network of double-interface mobile routers. The network is a part of advanced metering infrastructure deployed in the north of Poland. Limited capacity of cellular networks makes the system stability and performance dependent on smart management of radio resource utilization. A model of system phenomena was based on observations of a real system consisting of more than 10,000 devices. A network simulator was developed as a tool for solution testing outside of the real system. Computational Intelligence methods were found suitable for a large system scale. The paper introduces an iterative method based on cellular neural network model for determining the optimal resource allocation for the system.
Author Jakub Węcławski ISE
Jakub Węcławski,,
- The Institute of Electronic Systems
, Stanisław Jankowski ISE
Stanisław Jankowski,,
- The Institute of Electronic Systems
Pages201-215
Publication size in sheets0.7
Book Ryżko Dominik Paweł, Gawrysiak Piotr, Kryszkiewicz Marzena, Rybiński Henryk (eds.): Machine Intelligence and Big Data in Industry, Studies in Big Data, vol. 19, 2016, Springer International Publishing Switzerland, ISBN 978-3-319-30314-7, [978-3-319-30315-4], 236 p., DOI:10.1007/978-3-319-30315-4
Keywords in EnglishIntelligent decision support systems Computational intelligence Cellular neural network Advanced metering infrastructure Smart grid
DOIDOI:10.1007/978-3-319-30315-4_17
URL http://link.springer.com/chapter/10.1007/978-3-319-30315-4_17
projectResearch on measurement, circuit and signal theory and electronic circuits and systems. Project leader: Romaniuk Ryszard, , Phone: +48 22 234 7986, +48 22 234 5360, start date 15-06-2015, end date 31-12-2016, ISE/2015/DS, Completed
WEiTI Działalność statutowa
Languageen angielski
Score (nominal)15
ScoreMinisterial score = 15.0, 27-03-2017, BookChapterSeriesAndMatConfByIndicator
Ministerial score (2013-2016) = 15.0, 27-03-2017, BookChapterSeriesAndMatConfByIndicator
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