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. © 2012 Springer Science+Business Media, LLC.
Recommended Citation
T. Szalapski and S. Madria, "On Compressing Data in Wireless Sensor Networks for Energy Efficiency and Real Time Delivery," Distributed and Parallel Databases, vol. 31, no. 2, pp. 151 - 182, Springer, Jun 2013.
The definitive version is available at https://doi.org/10.1007/s10619-012-7111-5
Department(s)
Computer Science
Keywords and Phrases
Compression; Latency; Real-time; Wireless sensor network
International Standard Serial Number (ISSN)
1573-7578; 0926-8782
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jun 2013
Comments
U.S. Department of Energy, Grant P200A070359