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Henry Ponti Medeiros 1 and Marcos Costa Maciel 2 and Richard Demo Souza 3 and Marcelo Eduardo Pellenz 4
Academic Editor:Wan-Young Chung
1, Purdue University, 465 Northwestern Avenue, West Lafayette, IN 47907-2035, USA
2, Federal Institute of Education, Science and Technology of Amazonas (IFAM), Campus Manaus Industrial District, Avenida Danilo Areosa, 1672, 69075-351 Manaus, AM, Brazil
3, Federal University of Technology-Paraná (UTFPR), Avenida Sete de Setembro, 3165, 80230-901 Curitiba, PR, Brazil
4, Pontifical Catholic University-Paraná (PUC-PR), R. Imaculada Conceição, 1155, 80215-901 Curitiba, PR, Brazil
Received 5 August 2013; Revised 22 November 2013; Accepted 10 December 2013; 23 January 2014
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
One of the greatest challenges to the construction of large scale wireless sensor networks (WSNs) with practical applicability is the development of mechanisms that allow the network to operate for prolonged periods of time relying solely on the limited amounts of energy that can be stored in or harvested by wireless sensor nodes. Since data communication is generally the main factor responsible for draining the energy reserves of the network, techniques to reduce the amount of information transmitted by the sensor nodes are of great interest. One effective approach to reduce data communication in the network is to compress the information locally before it is transmitted.
Although data compression is a well-established research area, despite the extraordinary advances in the computational capability of embedded devices, most existing algorithms still cannot be directly ported to wireless sensor nodes because of the limited hardware resources available, particularly program and data memory [1]. Even though many of the time-honored compression algorithms could be executed in modern wireless sensor nodes, they would leave few resources available for the nodes to carry out other tasks such as sensing and communication. More importantly, these nodes would have significantly fewer opportunities to enter deep sleep modes and attain the energy efficiency that motivated the use of a compression algorithm in the first place. Therefore, a number of data compression methods specifically designed for WSNs have been proposed in the past few years [2-11]. What many...