Title: Energy-efficient adaptive data compression in wireless sensor networks

Authors: Jonathan Gana Kolo; Li-Minn Ang; Kah Phooi Seng; S. Anandan Shanmugam; David Wee Gin Lim

Addresses: Department of Electrical and Electronics Engineering, Federal University of Technology, PMB 65 Minna, Niger State, Nigeria ' School of Computing and Mathematics, Charles Sturt University, NSW 2650, Australia ' School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia ' Department of Electrical and Electronics Engineering, The University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia ' Department of Electrical and Electronics Engineering, The University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia

Abstract: In wireless sensor networks (WSNs), a large number of tiny, inexpensive and computable sensor nodes are usually deployed randomly to monitor one or more physical phenomena. The sensor nodes collect and process the sensed data and send the data to the sink wirelessly. Energy consumption is however a serious problem affecting WSNs lifetime. Radio communication is often the major cause of energy consumption in wireless sensor nodes. Thus, applying data compression before transmission can significantly help in reducing the total power consumption of a sensor node. In this paper, we propose an efficient and robust adaptive data compression scheme (ADCS). The proposed scheme independently compresses each block of source data losslessly or lossily on local nodes based on the given application. Simulation results show the merits of the proposed compression scheme in comparison with other recently proposed compression algorithms for WSNs including S-LZW, LEC, MPDC, Two-modal GPC and LTC.

Keywords: wireless sensor networks; WSNs; energy efficiency; data compression; signal processing; adaptive entropy encoding; AEE; Huffman coding; energy consumption; network lifetime; simulation.

DOI: 10.1504/IJSNET.2016.080371

International Journal of Sensor Networks, 2016 Vol.22 No.4, pp.229 - 247

Available online: 18 Nov 2016 *

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