Title :
Adaptive Linear Filtering Compression on realtime sensor networks
Author :
Kiely, Aaron B. ; Xu, Mingsen ; Song, Wen-Zhan ; Huang, Renjie ; Shirazi, Behrooz
Author_Institution :
Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA
Abstract :
We present a lightweight lossless compression algorithm for realtime sensor networks. Our proposed adaptive linear filtering compression (ALFC) algorithm performs predictive compression, using adaptive linear filtering to predict sample values followed by entropy coding of prediction residuals, encoding a variable number of samples into fixed-length packets. Adaptive prediction eliminates the need to determine prediction coefficients a priori and, more importantly, allows compression to dynamically adjust to a changing source. The algorithm requires only integer arithmetic operations and thus is compatible with sensor platforms that do not support floating-point operations. Significant robustness to packets losses is provided by including small but sufficient overhead data to allow samples in each packet to be independently decoded. Real-world evaluations on seismic data from a wireless sensor network testbed show that ALFC provides more effective compression and uses less resources than some other lossless compression approaches such as S-LZW. Experiments in a multi-hop sensor network also show that ALFC can significantly improve raw data throughput and energy efficiency.
Keywords :
adaptive filters; arithmetic codes; data compression; decoding; encoding; prediction theory; wireless sensor networks; adaptive linear filtering compression; decoding; encoding; entropy coding; fixed-length packet; integer arithmetic operation; lightweight lossless compression algorithm; predictive compression; realtime sensor network; Adaptive systems; Compression algorithms; Decoding; Encoding; Entropy coding; Filtering algorithms; Floating-point arithmetic; Maximum likelihood detection; Prediction algorithms; Robustness;
Conference_Titel :
Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on
Conference_Location :
Galveston, TX
Print_ISBN :
978-1-4244-3304-9
Electronic_ISBN :
978-1-4244-3304-9
DOI :
10.1109/PERCOM.2009.4912775