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Fuzzy-set-based decision making through energy-aware and utility agents within wireless sensor networks

Fuzzy-set-based decision making through energy-aware and utility agents within wireless sensor... Multi-agent systems (MAS) through their intrinsically distributed nature offer a promising software modelling and implementation framework for wireless sensor network (WSN) applications. WSNs are characterised by limited resources from a computational and energy perspective; in addition, the integrity of the WSN coverage area may be compromised over the duration of the network’s operational lifetime, as environmental effects amongst others take their toll. Thus a significant problem arises—how can an agent construct an accurate model of the prevailing situation in order that it can make effective decisions about future courses of action within these constraints? In this paper, one popular agent architecture, the BDI architecture, is examined from this perspective. In particular, the fundamental issue of belief generation within WSN constraints using classical reasoning augmented with a fuzzy component in a hybrid fashion is explored in terms of energy-awareness and utility. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Fuzzy-set-based decision making through energy-aware and utility agents within wireless sensor networks

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References (52)

Publisher
Springer Journals
Copyright
Copyright © 2008 by Springer Science+Business Media B.V.
Subject
Computer Science; Computer Science, general ; Artificial Intelligence (incl. Robotics)
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-008-9091-4
Publisher site
See Article on Publisher Site

Abstract

Multi-agent systems (MAS) through their intrinsically distributed nature offer a promising software modelling and implementation framework for wireless sensor network (WSN) applications. WSNs are characterised by limited resources from a computational and energy perspective; in addition, the integrity of the WSN coverage area may be compromised over the duration of the network’s operational lifetime, as environmental effects amongst others take their toll. Thus a significant problem arises—how can an agent construct an accurate model of the prevailing situation in order that it can make effective decisions about future courses of action within these constraints? In this paper, one popular agent architecture, the BDI architecture, is examined from this perspective. In particular, the fundamental issue of belief generation within WSN constraints using classical reasoning augmented with a fuzzy component in a hybrid fashion is explored in terms of energy-awareness and utility.

Journal

Artificial Intelligence ReviewSpringer Journals

Published: Oct 25, 2008

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