Name
Abstract | ||
|---|---|---|
Clustering is an important mechanism in large multi-hop wireless sensor networks for obtaining scalability, reducing energy consumption and achieving better network performance. Most of the research in this area has focused on energy-efficient solutions, but has not thoroughly analyzed the network performance, e.g. in terms of data collection rate and time. The main objective of this paper is to provide a useful fully-distributed inference algorithm for clustering, based on belief propagation. The algorithm selects cluster heads, based on a unique set of global and local parameters, which finally achieves, under the energy constraints, improved network performance. Evaluation of the algorithm implementation shows an increase in throughput in more than 40% compared to HEED scheme. This advantage is expressed in terms of network reliability, data collection quality and transmission cost. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-77690-1_14 | EWSN |
Keywords | Field | DocType |
network reliability,data collection rate,efficient clustering,algorithm implementation,better network performance,wireless sensor network,data collection quality,improved network performance,network performance,sensor network,energy constraint,useful fully-distributed inference algorithm,clustering,energy efficient,wireless sensor networks,belief propagation,data collection | Key distribution in wireless sensor networks,Computer science,Computer network,Brooks–Iyengar algorithm,Real-time computing,Mobile wireless sensor network,Cluster analysis,Wireless sensor network,Belief propagation,Scalability,Network performance | Conference |
Volume | ISSN | ISBN |
4913 | 0302-9743 | 3-540-77689-3 |
Citations | PageRank | References |
14 | 0.75 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tal Anker | 1 | 112 | 13.51 |
| Danny Bickson | 2 | 1105 | 46.91 |
| Danny Dolev | 3 | 6925 | 1305.43 |
| Bracha Hod | 4 | 53 | 3.27 |