Abstract
This paper proposes an energy-efficient nonlinear programming based dynamic clustering protocol (NLP-DC) unique to sensor networks to reduce the consumption of energy of cluster heads and to prolong the sensor network lifetime. NLP-DC must cover the entire network, which is another basic functionality of topology control. To achieve these goals, NLP-DC dynamically regulates the radius of each cluster for the purpose of minimizing energy consumption of cluster heads while the entire sensor network field is still being covered by each cluster. We verify both energy-efficiency and guarantee of perfect coverage. Through simulation results, we show that NLP-DC achieves the desired properties.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 460-469 |
| Number of pages | 10 |
| Volume | 3823 LNCS |
| Publication status | Published - 2005 Dec 1 |
| Event | EUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES - Nagasaki, Japan Duration: 2005 Dec 6 → 2005 Dec 9 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 3823 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | EUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES |
|---|---|
| Country | Japan |
| City | Nagasaki |
| Period | 05/12/6 → 05/12/9 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science
Cite this
On energy-aware dynamic clustering for hierarchical sensor networks. / Kim, Joongheon; Lee, Wonjun; Kim, Eunkyo; Kim, Joonmo; Lee, Choonhwa; Kim, Sungjin; Kim, Sooyeon.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3823 LNCS 2005. p. 460-469 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3823 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - On energy-aware dynamic clustering for hierarchical sensor networks
AU - Kim, Joongheon
AU - Lee, Wonjun
AU - Kim, Eunkyo
AU - Kim, Joonmo
AU - Lee, Choonhwa
AU - Kim, Sungjin
AU - Kim, Sooyeon
PY - 2005/12/1
Y1 - 2005/12/1
N2 - This paper proposes an energy-efficient nonlinear programming based dynamic clustering protocol (NLP-DC) unique to sensor networks to reduce the consumption of energy of cluster heads and to prolong the sensor network lifetime. NLP-DC must cover the entire network, which is another basic functionality of topology control. To achieve these goals, NLP-DC dynamically regulates the radius of each cluster for the purpose of minimizing energy consumption of cluster heads while the entire sensor network field is still being covered by each cluster. We verify both energy-efficiency and guarantee of perfect coverage. Through simulation results, we show that NLP-DC achieves the desired properties.
AB - This paper proposes an energy-efficient nonlinear programming based dynamic clustering protocol (NLP-DC) unique to sensor networks to reduce the consumption of energy of cluster heads and to prolong the sensor network lifetime. NLP-DC must cover the entire network, which is another basic functionality of topology control. To achieve these goals, NLP-DC dynamically regulates the radius of each cluster for the purpose of minimizing energy consumption of cluster heads while the entire sensor network field is still being covered by each cluster. We verify both energy-efficiency and guarantee of perfect coverage. Through simulation results, we show that NLP-DC achieves the desired properties.
UR - http://www.scopus.com/inward/record.url?scp=33744914342&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33744914342&partnerID=8YFLogxK
M3 - Conference contribution
SN - 3540308032
SN - 9783540308034
VL - 3823 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 460
EP - 469
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -