Affiliation:
1. Indiana University -- Purdue University Indianapolis, Indiana
Abstract
Energy efficiency is one of the most critical issues in the design and deployment of Wireless Sensor Networks (WSNs). Data compression is an important approach to reducing energy consumption of data gathering in multihop sensor networks. Existing compression algorithms only apply to either lossless or lossy data compression, but not to both. This article presents a generalized predictive coding framework for unified lossless and lossy data compression. In addition, we devise a novel algorithm for lossless compression to significantly improve data compression performance for various data collections and applications in WSNs. Rigorous simulations show our proposed framework and compression algorithm outperform several recent popular compression algorithms for WSNs such as Lossless Entropy Compression (LEC), S-Lempel-Ziv-Welch (LZW), and Lightweight Temporal Compression (LTC) using various real-world sensor datasets, demonstrating the merit of the proposed framework for unified temporal lossless and lossy data compression in WSNs.
Funder
National Science Foundation
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
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