| dc.contributor.author |
Ferentinos, KP |
en |
| dc.contributor.author |
Tsiligiridis, TA |
en |
| dc.date.accessioned |
2014-06-06T06:47:35Z |
|
| dc.date.available |
2014-06-06T06:47:35Z |
|
| dc.date.issued |
2007 |
en |
| dc.identifier.issn |
13891286 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.comnet.2006.06.013 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/3698 |
|
| dc.subject |
Adaptive network design |
en |
| dc.subject |
Energy conservation |
en |
| dc.subject |
Genetic algorithms |
en |
| dc.subject |
Optimal design |
en |
| dc.subject |
Wireless sensor networks |
en |
| dc.subject.other |
Adaptive systems |
en |
| dc.subject.other |
Constraint theory |
en |
| dc.subject.other |
Energy conservation |
en |
| dc.subject.other |
Genetic algorithms |
en |
| dc.subject.other |
Systems analysis |
en |
| dc.subject.other |
Telecommunication networks |
en |
| dc.subject.other |
Adaptive network design |
en |
| dc.subject.other |
Optimal design |
en |
| dc.subject.other |
Wireless sensor networks |
en |
| dc.subject.other |
Wireless telecommunication systems |
en |
| dc.title |
Adaptive design optimization of wireless sensor networks using genetic algorithms |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.comnet.2006.06.013 |
en |
| heal.publicationDate |
2007 |
en |
| heal.abstract |
We present a multi-objective optimization methodology for self-organizing, adaptive wireless sensor network design and energy management, taking into consideration application-specific requirements, communication constraints and energy-conservation characteristics. A precision agriculture application of sensor networks is used as an example. We use genetic algorithms as the optimization tool of the developed system and an appropriate fitness function is developed to incorporate many aspects of network performance. The design characteristics optimized by the genetic algorithm system include the status of sensor nodes (whether they are active or inactive), network clustering with the choice of appropriate clusterheads and finally the choice between two signal ranges for the simple sensor nodes. We show that optimal sensor network designs constructed by the genetic algorithm system satisfy all application-specific requirements, fulfill the existent connectivity constraints and incorporate energy-conservation characteristics. Energy management is optimized to guarantee maximum life span of the network without lack of the network characteristics that are required by the specific application. © 2006 Elsevier B.V. All rights reserved. |
en |
| heal.journalName |
Computer Networks |
en |
| dc.identifier.issue |
4 |
en |
| dc.identifier.volume |
51 |
en |
| dc.identifier.doi |
10.1016/j.comnet.2006.06.013 |
en |
| dc.identifier.spage |
1031 |
en |
| dc.identifier.epage |
1051 |
en |