Data compression plays a significant role in Body Sensor Networks (BSN). This is true since the sensors in BSNs have limited battery power and memory; sensor data needs to be transmitted regularly, and in lossless manner to provide prompt, accurate feedback. The paper evaluates lossless data compression algorithms including Run Length Encoding (RLE), Lempel Zev Welch (LZW), and Huffman on data from wearable devices and compares them in terms of Compression Ratio, Compression Factor, Savings Percentage and Compression Time. It also evaluates a data deduplication technique used for Low Bandwidth File Systems (LBFS), Two Thresholds Two Divisors (TTTD) algorithm, to determine if it is suitable for BSN data. First, through experiments s we arrive at a set of parameter values that give compression ratio above 50 on BSN data. Next, based on performance evaluation results of TTTD and classical compression algorithms including RLW, LAW, and Huffman, it proposes a technique to combine multiple algorithms in sequence. Upon comparison of the performance, it is found that the new algorithm, TTTD-H, which executes TTTD and Huffman in sequence, significantly improves the compression factor against both TTTD and Huffman. Performance evaluation has been carried out in two sets of BSN data.
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