Air pollution data exhibit characteristics like long range correlations and multi-fractal scaling that can be exploited to implement an energy efficient, adaptive spatial sampling technique for pollution sensor nodes. In this work, we present a) results from de-trended fluctuation analysis to prove the presence of non-linear dynamics in real pollution datasets gathered from trials carried out in Cyprus, b) a novel Multi-scale Nearest Neighbors based Adaptive Spatial Sampling (MNNASS) technique that determines the predictability and in turn the directional influences between data from different sensor nodes, and c) performance analysis of the algorithm in terms of energy savings and measurement accuracy.
['Manik Gupta', 'Eliane L. Bodanese', 'Lamling Venus Shum', 'Stephen Hailes']
Poster abstract: exploiting nonlinear data similarities-a multi-scale nearest-neighbor approach for adaptive sampling in wireless pollution sensor networks