Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/1077
Title: | A Genetic Algorithm Based Solution To the Minimum-Cost Bounded-Error Calibration Tree Problem | Authors: | Akcan, Hüseyin | Keywords: | Genetic algorithm Wireless sensor networks Energy efficiency Calibration tree |
Publisher: | Elsevier Science Bv | Abstract: | Sensors in wireless sensor networks are required to be self-calibrated periodically during their prolonged deployment periods. In calibration planning, employing intelligent algorithms are essential to optimize both the efficiency and the accuracy of calibration. The Minimum-Cost Bounded-Error Calibration Tree (MBCT) problem is a spanning tree problem with two objectives, minimizing the spanning tree cost and bounding the maximum post-calibration skew. The decision version of the MBCT problem is proven to be NP-Complete. In this paper, the GAWES algorithm is presented as a novel genetic algorithm based solution to the optimization version of the MBCT problem. GAWES adopts extreme efficient solution generation within the genetic algorithm to improve the search quality. It is demonstrated through experimentation that GAWES is superior to the existing state of the art algorithm, both in energy efficiency and calibration accuracy. (C) 2018 Elsevier B.V. All rights reserved. | URI: | https://doi.org/10.1016/j.asoc.2018.08.013 https://hdl.handle.net/20.500.14365/1077 |
ISSN: | 1568-4946 1872-9681 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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