Design and evaluation of an adaptive sampling strategy for a wireless air pollution sensor network

We present the design of a novel adaptive sampling technique called Exponential Double Smoothing-based Adaptive Sampling (EDSAS), in which the temporal data correlations provide an indication of the prevailing environmental conditions and are used to adapt the sensing rate of a sensor node. EDSAS uses irregular data series prediction to reduce sampling rate in combination with change detection to maintain data fidelity. The prediction method employs Wright´s extension to Holt´s method of Exponential Double Sampling (EDS) coupled with a change detection mechanism based on exponentially weighted moving averages (EWMA). The main advantages of EDSAS are that it does not require heavy computation, incurs low memory and communication overhead and the prediction model can be implemented with ease on resource constrained sensor nodes. EDSAS has been evaluated by using real urban road traffic Carbon Monoxide (CO) pollution datasets and has been compared and shown to give better results for performance metrics like sampling fraction and miss ratio. We have also undertaken analysis of the pollution data based on the information received and shown that EDSAS scores over other published technique called e-Sense in capturing the underlying characteristics of the real data.