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On compressing data in wireless sensor networks for energy efficiency and real time delivery

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Abstract

Wireless sensor networks possess significant limitations in storage, bandwidth, processing, and energy. Additionally, real-time sensor network applications such as monitoring poisonous gas leaks cannot tolerate high latency. While some good data compression algorithms exist specific to sensor networks, in this paper we present TinyPack, a suite of energy-efficient methods with high-compression ratios that reduce latency, storage, and bandwidth usage further in comparison with some other recently proposed algorithms. Our Huffman style compression schemes exploit temporal locality and delta compression to provide better bandwidth utilization important in the wireless sensor network, thus reducing latency for real time sensor-based monitoring applications. Our performance evaluations over many different real data sets using a simulation platform as well as a hardware implementation show comparable compression ratios and energy savings with a significant decrease in latency compared to some other existing approaches. We have also discussed robust error correction and recovery methods to address packet loss and corruption common in sensor network environments.

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Authors and Affiliations

  1. Department of Computer Science, Missouri S&T, Rolla, MO, 65401, USA

    Tommy Szalapski & Sanjay Madria

Authors
  1. Tommy Szalapski
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  2. Sanjay Madria
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Corresponding author

Correspondence to Sanjay Madria.

Additional information

Communicated by Dipanjan Chakraborty.

This research is supported by DOE grant number P200A070359.

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Szalapski, T., Madria, S. On compressing data in wireless sensor networks for energy efficiency and real time delivery. Distrib Parallel Databases 31, 151–182 (2013). https://doi.org/10.1007/s10619-012-7111-5

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