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Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007)
Multiobjective Optimal Design of MEMS-Based Reconfigurable and Evolvable Sensor Networks for Space Applications
University of Edinburgh, Scotland, United Kingdom
August 05-August 08
ISBN: 0-7695-2866-X
ASCII Text
x 
 
Erfu Yang, Nakul Haridas, A. El-Rayis, A. T. Erdogan, Tughrul Arslan, Nick Barton, "Multiobjective Optimal Design of MEMS-Based Reconfigurable and Evolvable Sensor Networks for Space Applications," Adaptive Hardware and Systems, NASA/ESA Conference on, pp. 27-34, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007), 2007.
 
 
BibTex
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@article{ 10.1109/AHS.2007.76,
author = {Erfu Yang and Nakul Haridas and A. El-Rayis and A. T. Erdogan and Tughrul Arslan and Nick Barton},
title = {Multiobjective Optimal Design of MEMS-Based Reconfigurable and Evolvable Sensor Networks for Space Applications},
journal ={Adaptive Hardware and Systems, NASA/ESA Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2866-X},
pages = {27-34},
doi = {http://doi.ieeecomputersociety.org/10.1109/AHS.2007.76},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Adaptive Hardware and Systems, NASA/ESA Conference on
TI - Multiobjective Optimal Design of MEMS-Based Reconfigurable and Evolvable Sensor Networks for Space Applications
SN - 0-7695-2866-X
SP27
EP34
A1 - Erfu Yang,
A1 - Nakul Haridas,
A1 - A. El-Rayis,
A1 - A. T. Erdogan,
A1 - Tughrul Arslan,
A1 - Nick Barton,
PY - 2007
KW - null
VL - 0
JA - Adaptive Hardware and Systems, NASA/ESA Conference on
ER -
 
Erfu Yang, University of Edinburgh
Nakul Haridas, University of Edinburgh
A. El-Rayis, University of Edinburgh
A. T. Erdogan, University of Edinburgh
Tughrul Arslan, University of Edinburgh
Nick Barton, University of Edinburgh
In this paper, the multiobjective optimal design of spacebased reconfigurable sensor networks with novel adaptive MEMS antennas is investigated by using multiobjective evolutionary algorithms. The non-dominated sorting genetic algorithm II (NSGA-II) is employed to obtain multi-criteria Pareto-optimal solutions, which allows system designers to easily make a reasonable trade-off choice from the set of non-dominated solutions according to their preferences and system requirements. As a case study, a cluster-based satellite sensing network is simulated under multiple objectives. Most importantly, this paper also presents the application of our newly designed adaptive MEMS antennas together with the NSGA-II to the multiobjective optimal design of space-based reconfigurable sensor networks.
Citation:
Erfu Yang, Nakul Haridas, A. El-Rayis, A. T. Erdogan, Tughrul Arslan, Nick Barton, "Multiobjective Optimal Design of MEMS-Based Reconfigurable and Evolvable Sensor Networks for Space Applications," ahs, pp.27-34, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007), 2007
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