Abstract—This work presents a study of energy reduction technique using data compression based on arithmetic coding in clustered wireless sensor networks to maximize the network’s lifetime. Initially, a simulation approach is used to investigate the effect of multiple data types found in environmental monitoring application on data compression and the effect of cluster’s parameters on their energy consumption. This study points out the important of probability models of multiple sensor data such as temperature and relative humidity on the arithmetic coding’s performance. The investigation results provide insights for designing an energy-efficient arithmetic coding framework that is suitable for compressing multiple data types in clustered multi-hop wireless sensor networks. Finally, an implementation of an adaptive local data compression algorithm derived from our findings and design framework on a set of four Tiny OS based Tmote Sky wireless sensor nodes equipped with temperature and relative humidity sensors is presented with approximately 54 percent data compression results.
Index Terms—Arithmetic Coding, Data Compression, Energy-Efficient, Wireless Sensor Network.
T. Srisooksai is with TAIST-Tokyo Tech, ICT for Embedded System Program, Department of Electrical Engineering, Faculty of Engineering, Kasetsart University, Thailand (email: email@example.com).
K. Kaemarungsi is with Embedded System Technology Laboratory, National Electronics and Computer Technology Center, Thailand.
P. Lamsrichan is with Department of Electrical Engineering, Faculty of Engineering, Kasetsart University, Thailand.
K. Araki is with Department of Electrical and Electronic Engineering, Tokyo Institute of Technology, Japan.
Cite: Tossaporn Srisooksai, Kamol Kaemarungsi, Poonlap Lamsrichan, and Kiyomichi Araki, "Energy-Efficient Data Compression in Clustered Wireless Sensor Networks using Adaptive Arithmetic Coding with Low Updating Cost,” International Journal of Information and Electronics Engineering vol. 1, no. 1, pp. 85-93 , 2011.