.png){kind=icon}
Return
Distributed Clustering Using Wireless Sensor Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/JSTSP.2011.2114324.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Clustering spatially distributed data is well motivated and especially challenging when communication to a central processing unit is discouraged, e. g., due to power constraints. Distributed clustering schemes are developed in this paper for both deterministic and probabilistic approaches to unsupervised learning. The centralized problem is solved in a distributed fashion by recasting it to a set of smaller local clustering problems with consensus constraints on the cluster parameters. The resulting iterative schemes do not exchange local data among nodes, and rely only on single-hop communications. Performance of the novel algorithms is illustrated with simulated tests on synthetic and real sensor data. Surprisingly, these tests reveal that the distributed algorithms can exhibit improved robustness to initialization than their centralized counterparts.
Keywords:
Clustering methods
distributed algorithms
expectation-maximization (EM) algorithms
iterative methods
wireless sensor networks
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.9K
Citations:
1.1W
Organization
No organization information available
Cited Papers
ShareSaveShareSaveDevelopment of top-level requirements for regional aircraft based on the needs of the Russian market
OAAI
OAAIShareSaveShareSave
The humanitarian system is not just broke, but broken: recommendations for future humanitarian action.png)
The Lancet
The LancetIF0
PREAI
ShareSave
View more