Rate-distortion balanced data compression for wireless sensor networks

Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, Hwee Pink Tan

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

This paper presents a data compression algorithm with error bound guarantee for wireless sensor networks (WSNs) using compressing neural networks. The proposed algorithm minimizes data congestion and reduces energy consumption by exploring spatio-temporal correlations among data samples. The adaptive rate-distortion feature balances the compressed data size (data rate) with the required error bound guarantee (distortion level). This compression relieves the strain on energy and bandwidth resources while collecting WSN data within tolerable error margins, thereby increasing the scale of WSNs. The algorithm is evaluated using real-world data sets and compared with conventional methods for temporal and spatial data compression. The experimental validation reveals that the proposed algorithm outperforms several existing WSN data compression methods in terms of compression efficiency and signal reconstruction. Moreover, an energy analysis shows that compressing the data can reduce the energy expenditure and, hence, expand the service lifespan by several folds.

Original languageEnglish
Article number7447657
Pages (from-to)5072-5083
Number of pages12
JournalIEEE Sensors Journal
Volume16
Issue number12
DOIs
Publication statusPublished - 5 Apr 2016
Externally publishedYes

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