Fast and efficient lossless adaptive compression scheme for wireless sensor networks. (January 2015)

• Record Type: Journal Article
• Authors: Kolo, Jonathan Gana; Shanmugam, S. Anandan; Lim, David Wee Gin; Ang, Li-Minn
• Is Part Of: Computers & electrical engineering. Volume 41(2015)
• Page Start: 275
• Page End: 287
• Publication Date: 2015-01
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2014.06.008
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A fast and low memory data compression scheme is proposed for WSNs. The scheme performs compression losslessly using 8 variable-length code options. The scheme which uses a very simple data model is fast and computationally simple. The scheme is efficient and requires no coding dictionary. The scheme achieve better compression performance than previously proposed schemes. Abstract: The number of wireless sensor network deployments for real-life applications has rapidly increased in recent years. However, power consumption is a critical problem affecting the lifetime of wireless sensor networks (WSNs). A number of techniques have been proposed to solve this power problem. Among the proposed techniques, data compression scheme is one that can be used to reduce the volume of data to be transmitted. This paper therefore proposes a fast and efficient lossless adaptive compression scheme (FELACS) for WSNs. FELACS was proposed to enable a fast and low memory compression algorithm for WSNs. FELACS generates its coding tables on the fly and compresses data very fast. FELACS is lightweight, robust to packet losses and has very low complexity. FELACS achieved compression rates of 4.11 bits per sample. In addition, it achieved power savings up to 70.61% using the real-world test datasets.