Abstract
Clustering is an effective topology control approach in sensor networks. This paper proposes a distributed and adaptive clustering architecture for dynamic sensor networks. The proposed architecture comprises an approach for energy-efficient clustering with adaptive node activity for achieving a good performance in terms of system lifetime and network coverage quality. This architecture demonstrates a uniform cluster head distribution across the network in addition to a desirable network coverage. Furthermore, the paper presents an analytical approach to disclose the relationship between network density and coverage quality. Experiments were conducted to validate the proposed architecture. The analytical and simulation results demonstrate that the proposed architecture prolongs network lifetime meanwhile preserving a highly coverage quality.
| Original language | English |
|---|---|
| Title of host publication | SOFA 2007 - 2nd IEEE International Workshop on Soft Computing Applications Proceedings |
| Pages | 221-225 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2007 |
| Event | 2nd IEEE International Workshop on Soft Computing Applications, SOFA 2007 - Oradea Duration: Aug 21 2007 → Aug 23 2007 |
Other
| Other | 2nd IEEE International Workshop on Soft Computing Applications, SOFA 2007 |
|---|---|
| City | Oradea |
| Period | 8/21/07 → 8/23/07 |
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ASJC Scopus subject areas
- Computer Science Applications
- Software
Cite this
Adaptive and energy efficient clustering architecture for dynamic sensor networks. / Saad, E. M.; Awadalla, M. H.; Saleh, M. A.; Keshk, H.; Darwish, R. R.
SOFA 2007 - 2nd IEEE International Workshop on Soft Computing Applications Proceedings. 2007. p. 221-225 4318333.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptive and energy efficient clustering architecture for dynamic sensor networks
AU - Saad, E. M.
AU - Awadalla, M. H.
AU - Saleh, M. A.
AU - Keshk, H.
AU - Darwish, R. R.
PY - 2007
Y1 - 2007
N2 - Clustering is an effective topology control approach in sensor networks. This paper proposes a distributed and adaptive clustering architecture for dynamic sensor networks. The proposed architecture comprises an approach for energy-efficient clustering with adaptive node activity for achieving a good performance in terms of system lifetime and network coverage quality. This architecture demonstrates a uniform cluster head distribution across the network in addition to a desirable network coverage. Furthermore, the paper presents an analytical approach to disclose the relationship between network density and coverage quality. Experiments were conducted to validate the proposed architecture. The analytical and simulation results demonstrate that the proposed architecture prolongs network lifetime meanwhile preserving a highly coverage quality.
AB - Clustering is an effective topology control approach in sensor networks. This paper proposes a distributed and adaptive clustering architecture for dynamic sensor networks. The proposed architecture comprises an approach for energy-efficient clustering with adaptive node activity for achieving a good performance in terms of system lifetime and network coverage quality. This architecture demonstrates a uniform cluster head distribution across the network in addition to a desirable network coverage. Furthermore, the paper presents an analytical approach to disclose the relationship between network density and coverage quality. Experiments were conducted to validate the proposed architecture. The analytical and simulation results demonstrate that the proposed architecture prolongs network lifetime meanwhile preserving a highly coverage quality.
UR - http://www.scopus.com/inward/record.url?scp=47949127853&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47949127853&partnerID=8YFLogxK
U2 - 10.1109/SOFA.2007.4318333
DO - 10.1109/SOFA.2007.4318333
M3 - Conference contribution
AN - SCOPUS:47949127853
SN - 9781424416080
SP - 221
EP - 225
BT - SOFA 2007 - 2nd IEEE International Workshop on Soft Computing Applications Proceedings
ER -