skip to main content
10.1145/3384419.3430407acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
short-paper

A toolkit for spatial interpolation and sensor placement: poster abstract

Published:16 November 2020Publication History

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.

References

  1. Nipun Batra, Jack Kelly, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, and Mani Srivastava. 2014. NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring. In Fifth International Conference on Future Energy Systems. ACM Press, Cambridge, UK, 265--276. arXiv:1404.3878 Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  3. 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 ScholarGoogle ScholarCross RefCross Ref
  4. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. 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 ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. A toolkit for spatial interpolation and sensor placement: poster abstract

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
      November 2020
      852 pages
      ISBN:9781450375900
      DOI:10.1145/3384419

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 November 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper

      Acceptance Rates

      Overall Acceptance Rate174of867submissions,20%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader