Efficient Z-order Encoding Based Multi-model Data Compression in WSNs
Xiaofei Cao, Sanjay Madria and Takahiro Hara
Missouri University of Science and Technology, Missouri University of Science and Technology, Osaka University

Wireless sensor networks have significant limitations in available bandwidth and energy. The limited bandwidth in sensor networks can cause higher message delivery latency in applications such as monitoring poisonous gas leak. In such applications, there are multi-modal sensors whose values such as temperature, gas concentration, location and CO2 level need to be transmitted together for faster detection and timely assessment of gas leak. In this paper, we propose novel Z-order based data compression schemes (Z-compression) to reduce energy and save bandwidth without increasing the message delivery latency. Instead of using the popular Huffman tree style based encoding, Zcompression uses Z-order encoding to map the multidimensional sensing data into one-dimensional binary stream transmitted using a single packet. Our experimental evaluations using real-world data sets show that Z-compression has a much better compression ratio, energy saving, streaming rate than known schemes like LEC (and adaptive LEC), FELACS and TinyPack for multi-modal sensor data.