Publication: Energy-aware Task Scheduling in Wireles...

Master data
Title: Energy-aware Task Scheduling in Wireless Sensor Networks based on Cooperative Reinforcement Learning
Subtitle:
Abstract: Wireless sensor networks (WSN) are an attractive platform for cyber physical systems. A typical WSN applications composed of different tasks which need to be scheduled on each sensor node. However, the severe energy limitations pose a particular challenge for developing WSN applications, and the scheduling of tasks has typically a strong influence on the achievable performance and energy consumption. In this paper we propose a method for scheduling the tasks using cooperative reinforcement learning (RL) where each node determines the next task based on the observed application behavior. In this RL framework we can trade the application performance and the required energy consumption by a weighted reward function and can therefore achieve different energy/performance results of the overall application. By exchanging data among neighboring nodes we can further improve this energy/performance trade-off. We evaluate our approach in an target tracking application.Our simulations show that cooperative approaches are superior to non-cooperative approaches for this kind of applications.Index Terms—Reinforcement learning, tasks scheduling, energy efficiency, wireless sensor networks, target tracking.
Keywords:
Publication type: Article in compilation (Authorship)
Publication date: 2014 (Online)
Published by: Proceedings of the IEEE International Conference on Communications (ICC)
Proceedings of the IEEE International Conference on Communications (ICC) (2014)
to publication
 ( IEEE; )
Title of the series: -
Volume number: -
First publication: Yes
Version: -
Page: pp. 871 - 877

Versionen
Keine Version vorhanden
Publication date: 2014
ISBN (e-book): -
eISSN: -
DOI: http://dx.doi.org/10.1109/ICCW.2014.6881310
Homepage: http://www.epics-project.eu/publications/2014_khan_iccw.pdf
Open access
  • No open access

Assignment
Organisation Address
Fakultät für Technische Wissenschaften
  Institut für Vernetzte und Eingebettete Systeme
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Austria
  -993640
   kornelia.lienbacher@aau.at
https://nes.aau.at/
To organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation
Subject areas
  • 102 - Computer Sciences
  • 202 - Electrical Engineering, Electronics, Information Engineering
Research Cluster
  • Self-organizing systems
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: II)
Classification raster of the assigned organisational units:
working groups No working group selected

Cooperations
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