Multi-model Z-compression for high speed data streaming and low-power wireless sensor networks

Abstract

Wireless Sensor Networks (WSNs) have significant limitations in terms of available bandwidth and energy. The limited bandwidth in WSNs can cause delays in message delivery, which does not suit the real-time sensing applications such as a gas leak. Moreover, in such applications, there are multi-modal sensors whose values like temperature, gas concentration, location, and CO$$_2$$ levels must be transmitted together for correct and timely detection of a gas leak. In this paper, we propose a novel Z-order based data compression scheme, Z-compression, to reduce energy consumption and save bandwidth without increasing message delivery latency. Instead of using the popular Huffman tree style based encoding, the Z-compression uses Z-order encoding to map the multidimensional sensing data into one-dimensional binary stream transmitted using a single packet. Our experimental evaluations using different real-world datasets show that the Z-compression has a much better compression ratio, energy, and bandwidth savings than known schemes like LEC, Adaptive-LEC, FELACS, and TinyPack for multi-modal sensor data. Through the extensive experiments, we show that Z-compression is suitable for real-world sensing applications requiring high-stream rate WSNs and delay-tolerant low-power listening WSNs. In high-stream rate WSNs, the Z-compression can save bandwidth and increases the throughput. In low-power listening WSNs, by concatenating the Z-compressed data at selected reporting nodes, we can reduce the duty cycles of the nodes in WSNs, thus prolong the lifetime of the network, and still maintain the low distortion rate.