Name
Abstract | ||
|---|---|---|
Although current proposed compression schemes achieve a better performance compared with traditional data compression schemes, they have not fully exploited the spatial and temporal correlations among the data. Well-designed clustering algorithms are needed to explore strong spatial correlation. In this paper, we propose a k-means based Kronecker supported two-dimensional (spatio-temporal) compression scheme to achieve better compression results. Our scheme first leverages a k-means based clustering algorithm that explores the spatial correlation among sensory data. Then it develops a novel two-dimensional data compression mechanism, which can recover the original data from the compressed data with a high precision. Simulation results show that our proposed scheme is energy-efficient and can achieve better clustering results and recovery performance compared with other schemes for sensory data. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 27TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND NETWORKS (ICCCN) | Kronecker product, k-means clustering, data collection, two-dimensional compression, wireless sensor networks |
Field | DocType | Citations |
Kronecker delta,Data mining,Data collection,Spatial correlation,Computer science,Correlation,Cluster analysis,Data compression,Wireless sensor network,Sparse matrix,Distributed computing | Conference | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siguang Chen | 1 | 63 | 12.91 |
| Wang Zhihao | 2 | 39 | 13.76 |
| Kewei Sha | 3 | 206 | 21.29 |