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Proceedings Paper

An adaptive approach to space-based picosatellite sensor networks
Author(s): Tughrul Arslan; Erfu Yang; Nakul Haridas; Alicia Morales; Ahmed O. El-Rayis; Ahmet T. Erdogan; Adrian Stoica
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Paper Abstract

The rapid advancements in ad hoc sensor networks, MEMS (micro-electro-mechanical systems) devices, low-power electronics, adaptive hardware and systems (AHS), reconfigurable architectures, high-performance computing platforms, distributed operating systems, micro-spacecrafts, and micro-sensors have enabled the design and development of a highperformance satellite sensor network (SSN). Due to the changing environment and the varying missions that a SSN may have, there is an increasing need to develop efficient strategies to design, operate, and manage the system at different levels from an individual satellite node to the whole network. Towards this end, this paper presents an adaptive approach to space-based picosatellite sensor network by exploiting efficient bio-inspired optimization algorithms, particularly for solving multi-objective optimization problems at both local (node) and global (network) system levels. The proposed approach can be hierarchically used for dealing with the challenging optimization problems arising from the energy-constrained satellite sensor networks. Simulation results are provided to demonstrate the effectiveness of the proposed approach through its application in solving both node-level and system-level optimization problems.

Paper Details

Date Published: 29 April 2009
PDF: 12 pages
Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 73470P (29 April 2009); doi: 10.1117/12.820792
Show Author Affiliations
Tughrul Arslan, Univ. of Edinburgh (United Kingdom)
Erfu Yang, Univ. of Edinburgh (United Kingdom)
Nakul Haridas, Univ. of Edinburgh (United Kingdom)
Alicia Morales, Univ. of Edinburgh (United Kingdom)
Ahmed O. El-Rayis, Univ. of Edinburgh (United Kingdom)
Ahmet T. Erdogan, Univ. of Edinburgh (United Kingdom)
Adrian Stoica, Jet Propulsion Lab. (United States)

Published in SPIE Proceedings Vol. 7347:
Evolutionary and Bio-Inspired Computation: Theory and Applications III
Teresa H. O'Donnell; Misty Blowers; Kevin L. Priddy, Editor(s)

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