Bi-scale temporal sampling strategy for traffic-induced pollution data with Wireless Sensor Networks

Abstract

Carbon Monoxide (CO) induced by traffic pollution is highly dynamic and non-linear. In a pilot research, we collected some fine-grained 1Hz CO pollution data from a residential road and a busy motorway in Hyderabad, India, in preparation of the deployment of a larger scale, longer term wireless sensor monitoring system. Power conservation is an important issue as the sensor nodes are battery operated. We studied the characteristics of the collected data and designed an adaptive sampling algorithm, Bi-Scale temporal sampler, which adapts the sampling frequency to the statistics collected in real time. This design has incorporated practical engineering considerations including minimising electronic noise, sensor warm-up time and data characteristics. Results show that Bi-Scale sampler achieves better energy saving and statistical deviation ratio for our requirements than burst sampling and eSENSE sampling strategies, which are techniques popularly used in environmental monitoring applications.