Publication Type

Journal Article

Publication Date

4-2016

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.

Keywords

Wireless sensor networks, Data compression, Correlation, Compression algorithms, Monitoring, Sensor phenomena and characterization

Discipline

Software Engineering | Theory and Algorithms

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Sensors Journal

Volume

16

Issue

12

First Page

5072

Last Page

5083

ISSN

1530-437X

Identifier

10.1109/JSEN.2016.2550599

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1109/JSEN.2016.2550599

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