Signal compression is crucial for reducing the amount of communication, and hence power consumption of wireless sensors. Lossless compression techniques, such as Huffman coding, are often used in healthcare applications since they do not compromise the integrity of vital signals. Techniques that adapt to changing signal patterns have been proposed. However, most of them involve significant computation overhead or are too simple to maintain high compression rates under changing signal patterns. In this letter, we propose a technique that makes use of multiple codebooks, which are generated offline based on the signal context. In the applications we study, we observe that the symbols that compose a big variety of signals follow Laplacian distributions in which the spread changes over time. This can be effectively utilized to generate a set of codebooks. Then, appropriate codebooks are selected online depending on the currently measured spread, which ensures high compression efficiency and the adaptability to changing signal patterns. Our experiments on real-world medical datasets show that our approach is computationally very efficient, and exhibits competitive compression rates. Our proposed technique outperforms a state-of-the-art compression algorithm, FAS-LEC, in terms of average data reduction by 4.3%, while consuming a similar amount of energy. Compared to the adaptive Huffman method, which achieves near-optimal compression rates, our results indicate energy savings of 19% due to the reduced computational complexity, while the compression rate is improved by 0.6%.
- Huffman coding
- body sensor networks
- compression algorithms
- maximum likelihood estimation
- wearable computers