Name
Abstract | ||
|---|---|---|
Data compression is extremely important for all kinds of networking applications, but most existing compression algorithms are only based on utilizing the redundancy characteristics of data source. This paper proposes a novel compression paradigm, which explores another kind of redundancy that is provided by the correlated state information shared between sender and receiver of a local transmission. Such kind of redundancy reflects the knowledge of two peers about each other before a transmission really takes place, with the aid of possibly existing cloud data center or sensing interfaces equipped. We formulate a typical representation of shared context as inequality constraints for the general case in n-dimensional Euclidean space, and provide a constructive proof for the existence of a bijective mapping used for compression and decompression with the shared context. By experimenting on compressing geographic spatial-temporal data for efficient transmissions, analysis and simulation demonstrate that the proposed scheme outperforms the well-known Huffman coding and Delta algorithms in terms of compression ratio. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JIOT.2019.2910600 | IEEE Internet of Things Journal |
Keywords | Field | DocType |
Redundancy,Decoding,Receivers,Data models,Robot sensing systems,Data centers | Constructive proof,Bijection,Computer science,Communication source,Euclidean space,Huffman coding,Redundancy (engineering),Compression ratio,Data compression,Distributed computing | Journal |
Volume | Issue | ISSN |
6 | 4 | 2327-4662 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Longjiang Li | 1 | 74 | 5.50 |
| Jianjun Yang | 2 | 0 | 2.37 |
| Yuming Mao | 3 | 1 | 2.73 |