dc.contributor.author | Pardeiro, Jose | en_US |
dc.contributor.author | Gomez, Javier V. | en_US |
dc.contributor.author | Brunete, Alberto | en_US |
dc.contributor.author | Sandnes, Frode Eika | en_US |
dc.date.accessioned | 2015-02-10T09:38:22Z | |
dc.date.available | 2015-02-10T09:38:22Z | |
dc.date.issued | 2014 | en_US |
dc.identifier.citation | Pardeiro, J., Gomez, J.V., Brunete, A. & Sandnes, F.E. (2014). Evolutionary optimization algorithms for sunlight-based positioning sensor networks. International Journal of Distributed Sensor Networks, 2014(564072). doi: 10.1155/2014/564072 | en_US |
dc.identifier.issn | 1550-1329 | en_US |
dc.identifier.other | FRIDAID 1178617 | en_US |
dc.identifier.uri | https://hdl.handle.net/10642/2381 | |
dc.description.abstract | The sunlight intensity-based global positioning system (SGPS) is able to geolocate outdoor objects by means of the sunlight
intensity detection. This paper presents the integration of SGPS into a sensor network in order to improve the overall accuracy
using evolutionary algorithms. Another contribution of the paper is to theoretically solve both global and relative positioning of
the sensors composing the network within the same framework without satellite-based GPS technology. Results show that this
approach is promising and has potential to be improved further. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Hindawi Publishing Corporation | en_US |
dc.relation.ispartofseries | International Journal of Distributed Sensor Networks;Article ID 564072 | en_US |
dc.subject | Sunlight intensity-based global positioning system | en_US |
dc.subject | SGPS | en_US |
dc.subject | Integration | en_US |
dc.subject | Sensor networks | en_US |
dc.title | Evolutionary optimization algorithms for sunlight-based positioning sensor networks | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | Copyright © 2014 Jose Pardeiro et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
dc.identifier.doi | http://dx.doi.org/10.1155/2014/564072 | |