Rate-distortion Balanced Data Compression for Wireless Sensor Networks

TL;DR

This paper introduces an adaptive neural network-based data compression algorithm for wireless sensor networks that balances data size and error bounds, improving efficiency and extending network lifespan.

Contribution

It proposes a novel rate-distortion balanced compression algorithm utilizing neural networks, optimized for energy efficiency and scalability in WSNs.

Findings

Outperforms existing methods in compression efficiency

Reduces energy consumption significantly

Extends network lifespan by several folds

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 datasets 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.