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资源说明：Data compression algorithms for TinyOS.
README for tinyPack ================================================================================ Description This is a library of data compression algorithms for TinyOS. The implemented algorithms can compress arrays of up to 256 bytes at a time. To make compression suitable to low-end embedded systems, the primary objective is to reduce the RAM usage. None of these implementations use more than a few hundred bytes of RAM. The second most important object is speed of compression and decompression. Popular compression algorithms such as LZW, typically require several kilobytes of RAM to achieve useful compression, making them unsuitable. The code is still in the experimental stages. Here is a brief introduction to the compression algorithms available in tos/lib/tinypack. PC based decompression is available in scripts/tinypack/tinypack.py. LzrwEgaChainC 512 Bytes RAM, Fast, Good compression ratio This is an LZRW-like compression algorithm, which means that is uses a hash table to find repeat sequences of bytes that can be compressed down. LZRW encoding is similar to LZSS, but the hash table method makes compression fast, at the cost of a 128 byte table in RAM. Bytes that are not part of a repeated sequence, are encoding individually using an EGA (Exponential Golomb Adaptive) codebook. This codebook is fast, achieves good compression when bytes fit the exponential model, and uses 384 bytes of RAM. LzrwChainC 128 Bytes RAM, Fast, Moderate compression ratio Same as LzrwEgaChainC, except no codebook is used to encode literal bytes. This uses less RAM, but reduces the compression ratio. LzssChainC No RAM, Slow, Moderate compression ratio This is an LZSS-like compression algorithm. The advantage of LZSS is that is can be implemented without using any RAM. This is because the search for repeated sequences is performed iteratively over the previously compressed data. The cost is that this search is slow. The TinyOS code is compatible with TinyOS 2.x. ================================================================================ License For licensing details, see individual file headers. Licensed under the Apache License, Version 2.0.
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