Performance comparison of data compression algorithms for environmental monitoring wireless sensor networks

Authors

J.G. Kolo
Li-minn K. Ang, Edith Cowan UniversityFollow
Kah Phooi Seng
S.R.S Prabaharan

Document Type

Journal Article

Keywords

Data compression, Energy efficient, Huffman coding, Wireless sensor networks, WSNsCompression algorithms, Compression performance, Data compression algorithms, Data sets, Energy efficient, Environmental data, Environmental Monitoring, Huffman coding, Limited bandwidth, Limited memory, Lossless data compression algorithm, Network lifetime, Performance comparison, Power supply, Processing speed, Resource limitations, Storage spaces, Total power consumption, Wireless sensor network (WSNs), WSNs, Algorithms, Data processing, Digital storage, Energy efficiency, Entropy, Environmental engineering, Sensor nodes, Telecommunication systems, Wireless sensor networks, Data compression

Publisher

Inderscience

Faculty

Faculty of Health, Engineering and Science

School

School of Engineering / Centre for Communications and Electronics Research

RAS ID

16484

Comments

Kolo, J., Ang, L. K., Seng, K. , & Prabaharan, S. (2013). Performance comparison of data compression algorithms for environmental monitoring wireless sensor networks. International Journal of Computer Applications in Technology, 46(1), 65-75. Available here

Abstract

Wireless sensor networks (WSNs) have serious resource limitations ranging from finite power supply, limited bandwidth for communication, limited processing speed, to limited memory and storage space. Data compression can help reduce memory and storage space requirements on sensor node. In WSNs, radio communication is the major consumer of energy. Therefore, applying data compression before transmission will significantly and directly help in reducing total power consumption of a sensor node thereby extending the network lifetime. In this article, we propose a simple lossless data compression algorithm designed specifically to be used by environmental monitoring sensor nodes for the compression of environmental data which are characterise by significant fluctuations in entropy. To verify the effectiveness of our proposed algorithm, we compare its compression performance with two existing WSNs compression algorithms using real-world environmental datasets. We show that our algorithm outperforms the other two algorithms when the entropy of the dataset is large.