Evolutionary techniques for sensor networks energy optimization in marine environmental monitoring

Grimaccia, Francesco, Johnstone, Ron, Mussetta, Marco, Pirisi, Andrea and Zich, Riccardo E. (2012). Evolutionary techniques for sensor networks energy optimization in marine environmental monitoring. In: Bormin Huang and Antonio J. Plaza, High-Performance Computing in Remote Sensing II. Proceedings. SPIE Remote Sensing 2012, Edinburgh, UK, (85390H.1-85390H.8). 24-27 September, 2012. doi:10.1117/12.974631

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Author Grimaccia, Francesco
Johnstone, Ron
Mussetta, Marco
Pirisi, Andrea
Zich, Riccardo E.
Title of paper Evolutionary techniques for sensor networks energy optimization in marine environmental monitoring
Conference name SPIE Remote Sensing 2012
Conference location Edinburgh, UK
Conference dates 24-27 September, 2012
Proceedings title High-Performance Computing in Remote Sensing II. Proceedings   Check publisher's open access policy
Journal name Proceedings of SPIE   Check publisher's open access policy
Place of Publication Bellingham, WA, United States
Publisher S P I E - International Society for Optical Engineering
Publication Year 2012
Sub-type Fully published paper
DOI 10.1117/12.974631
Open Access Status File (Publisher version)
ISBN 9780819492791
ISSN 0277-786X
1996-756X
Editor Bormin Huang
Antonio J. Plaza
Volume 8539
Start page 85390H.1
End page 85390H.8
Total pages 8
Language eng
Formatted Abstract/Summary
The sustainable management of coastal and offshore ecosystems, such as for example coral reef environments, requires the collection of accurate data across various temporal and spatial scales. Accordingly, monitoring systems are seen as central tools for ecosystem-based environmental management, helping on one hand to accurately describe the water column and substrate biophysical properties, and on the other hand to correctly steer sustainability policies by providing timely and useful information to decision-makers. A robust and intelligent sensor network that can adjust and be adapted to different and changing environmental or management demands would revolutionize our capacity to wove accurately model, predict, and manage human impacts on our coastal, marine, and other similar environments. In this paper advanced evolutionary techniques are applied to optimize the design of an innovative energy harvesting device for marine applications. The authors implement an enhanced technique in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches, Particle Swarm Optimization and Genetic Algorithms. Here, this hybrid procedure is applied to a power buoy designed for marine environmental monitoring applications in order to optimize the recovered energy from sea-wave, by selecting the optimal device configuration.
Subjects 2604 Applied Mathematics
1706 Computer Science Applications
2208 Electrical and Electronic Engineering
2504 Electronic, Optical and Magnetic Materials
3104 Condensed Matter Physics
Keyword Computational Intelli-gence
Energy Harvesting Devices (EHDs)
Marine environment
Optimization techniques
Wireless Sensor Network
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Presented during Session 3: "High Performance Computing III".

 
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