Shared Context-Based Compression Paradigm for Ubiquitous Local Transmissions

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.