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.
Wireless sensor networks, Data compression, Correlation, Compression algorithms, Monitoring, Sensor phenomena and characterization
Software Engineering | Theory and Algorithms
Software and Cyber-Physical Systems
IEEE Sensors Journal
Institute of Electrical and Electronics Engineers (IEEE)
ALSHEIKH, Mohammad Abu; LIN, Shaowei; NIYATO, Dusit; and Hwee-Pink TAN.
Rate Distortion Balanced Data Compression in Wireless Sensor Networks. (2016). IEEE Sensors Journal. 16, (12), 5072-5083. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3423
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