ABSTRACT
Sensing is central to the SenSys and related communities. However, fine-grained spatial sensing remains a challenge despite recent advancements, owing to cost, maintenance, among other factors. Thus, estimating the sensed phenomenon at unmonitored locations and strategically installing sensors is of prime importance. In this work, we introduce Polire - an open-source tool that provides a suite of algorithms for spatial interpolation and near-field passive sensor placements. We replicate two existing papers on these two tasks to show the efficacy of Polire. We believe that Polire is an essential step towards lowering entry barriers towards sensing and scientific reproducibility.
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Digital Library
- Carlos Guestrin, Andreas Krause, and Ajit Paul Singh. 2005. Near-Optimal Sensor Placements in Gaussian Processes. In Proceedings of the 22nd International Conference on Machine Learning (Bonn, Germany) (ICML '05). Association for Computing Machinery, New York, NY, USA, 265--272. Google Scholar
Digital Library
- Katherine A. Klise, Bethany L. Nicholson, and Carl Damon Laird. 2017. Sensor Placement Optimization using Chama. Number SAND2017-11472. Albuquerque, NM: Sandia National Laboratories (10 2017). Google Scholar
Cross Ref
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- David W Wong, Lester Yuan, and Susan A Perlin. 2004. Comparison of spatial interpolation methods for the estimation of air quality data. Journal of Exposure Science & Environmental Epidemiology 14, 5 (Sept. 2004), 404--415. Google Scholar
Cross Ref
Index Terms
A toolkit for spatial interpolation and sensor placement: poster abstract
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