Sensor selection for heterogeneous coverage measures

Simon Shamoun, Tarek Abdelzaher, Amotz Bar-Noy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider sensor selection to optimize multiple conditions. Specifically, we model the sensor network as a graph, in which weighted edges indicate the ability of one node to predict the data of another. Each node is associated with several data types, so there are links for each data type. The objective is to maximize the coverage of all data types. This is applicable to such problems as monitoring air quality in cities and coal mines using several indicators of quality. We first define the maximization criteria, and then how to modify the model and existing algorithms to solve the problem. We demonstrate the importance of the problem and the quality of our methodology on synthetic and realistic scenarios.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 13th International Conference on Distributed Computing in Sensor Systems, DCOSS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages93-98
Number of pages6
ISBN (Electronic)9781538639917
DOIs
StatePublished - Jan 26 2018
Event13th International Conference on Distributed Computing in Sensor Systems, DCOSS 2017 - Ottawa, Canada
Duration: Jun 5 2017Jun 7 2017

Publication series

NameProceedings - 2017 13th International Conference on Distributed Computing in Sensor Systems, DCOSS 2017
Volume2018-January

Other

Other13th International Conference on Distributed Computing in Sensor Systems, DCOSS 2017
CountryCanada
CityOttawa
Period6/5/176/7/17

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Keywords

  • graph models
  • hybrid coverage
  • sensor selection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Instrumentation

Cite this

Shamoun, S., Abdelzaher, T., & Bar-Noy, A. (2018). Sensor selection for heterogeneous coverage measures. In Proceedings - 2017 13th International Conference on Distributed Computing in Sensor Systems, DCOSS 2017 (pp. 93-98). (Proceedings - 2017 13th International Conference on Distributed Computing in Sensor Systems, DCOSS 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCOSS.2017.28