Efficient Z-Order Encoding Based Multi-Modal Data Compression in WSNs

Xiaofei Cao, Sanjay Madria, Takahiro Hara

Abstract: Wireless sensor networks have significant limitationsin available bandwidth and energy. The limited bandwidthin sensor networks can cause higher message delivery latencyin applications such as monitoring poisonous gas leak. In suchapplications, there are multi-modal sensors whose values such astemperature, gas concentration, location and CO2 level need tobe transmitted together for faster detection and timely assessmentof gas leak. In this paper, we propose novel Z-order based datacompression schemes (Z-compression) to reduce energy and savebandwidth without increasing the message delivery latency. Insteadof using the popular Huffman tree style based encoding, Zcompressionuses Z-order encoding to map the multidimensionalsensing data into one-dimensional binary stream transmittedusing a single packet. Our experimental evaluations using realworlddata sets show that Z-compression has a much bettercompression ratio, energy saving, streaming rate than knownschemes like LEC (and adaptive LEC), FELACS and TinyPackfor multi-modal sensor data.