Name
Abstract | ||
|---|---|---|
Distributed inference schemes for detection, estimation and learning comprise an attractive approach to Wireless Sensor Networks (WSNs), because of properties such as asynchronous operation and robustness in the face of failures.Belief Propagation (BP) is a method for distributed inference which provides accurate results with rapid convergence properties. However, applying a BP algorithm to WSN is challenging. Many papers that proposed using BP for WSNs do not consider all of the constraints which these networks impose.This paper presents a framework that implements both localized and data-centric approaches to improve the effectiveness and the robustness of this algorithm in the WSN environment. The proposed solution is empirically evaluated, as applied to the clustering problem, and it can be easily extended to suit many other applications that use BP as an underlying algorithm. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-88582-5_44 | WASA |
Keywords | Field | DocType |
attractive approach,proposed solution,practical approach,wsn environment,asynchronous operation,belief propagation,inference scheme,bp algorithm,accurate result,wireless sensor networks,underlying algorithm,wireless sensor network | Key distribution in wireless sensor networks,Inference,Computer science,Computer network,Asynchronous operation,Robustness (computer science),Rapid convergence,Cluster analysis,Wireless sensor network,Distributed computing,Belief propagation | Conference |
Volume | ISSN | Citations |
5258 | 0302-9743 | 7 |
PageRank | References | Authors |
0.49 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tal Anker | 1 | 112 | 13.51 |
| Danny Dolev | 2 | 6925 | 1305.43 |
| Bracha Hod | 3 | 53 | 3.27 |