NDLI: Intelligent task scheduling in sensor networks

Content Provider	IEEE Xplore Digital Library
Author	van Norden, W. de Jong, J. Bolderheij, F. Rothkrantz, L.
Copyright Year	2005
Description	Author affiliation: Fac. of Electr. Eng., Math. & Comput. Sci., Delft Univ. of Technol., Netherlands (van Norden, W.)
Abstract	Ever more complex sensors have become available to create and maintain situational awareness during missions. Choosing the most suited sensor for the execution of a sensor function is based on sensor capabilities and function attributes. When these characteristics change rapidly, sensor allocation for sensor functions will shift. To increase performance of the entire sensor network, the total set of sensors should be scheduled in a single system. This paper puts forward and compares three new methods for scheduling prioritised tasks in sensor networks. The first is based on fuzzy Lyapunov synthesis. The other two use a genetic algorithm (GA) to optimize the set of schedules. The second scheduler uses this set to (re)train a neural network to be used online. The third approach is a novel online use of the GA. Tests showed that the novel online GA leads to a robust scheduling algorithm with high overall performance.
File Size	5240438
File Format	PDF
ISBN	0780392868
DOI	10.1109/ICIF.2005.1592013
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent sensors Intelligent networks Sensor phenomena and characterization Sensor systems Network synthesis Genetic algorithms Neural networks Testing Robustness Scheduling algorithm fuzzy Lyapunov Sensor Management Task Scheduling Command & Control Online Genetic Algorithms
Content Type	Text
Resource Type	Article

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