Charging state aware optimal auction design for sensor selection in crowdsourcing based sensor networks

Nianxia Cao, Yanzhi Wang, Swastik Brahma, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

In this paper, crowdsourcing based wireless sensor networks (WSNs) with rechargeable sensors are used for target localization. For rechargeable sensors, the state of charge (SOC) is one of the key factors that decides the sensors' energy cost for the localization task. To conserve limited resources, the fusion center (FC) employs an optimal sensor selection scheme obtained through an auction design approach. The sensors compete to participate in the target localization task by sending bids based on their energy efficiency (analog data) and SOC (quantized data) to the FC. Aiming at maximizing the expected utility, the FC designs an optimal auction mechanism incorporating both analog information about sensors' energy efficiency and quantized information of the SOC and decides on the winning sensor(s) as well as the payment to the winner(s). Simulation experiments show the effective performance of our framework by investigating the effect of the SOC of the sensors on the utility of the FC and the target localization mean square error (MSE).

Original languageEnglish (US)
Title of host publicationFUSION 2016 - 19th International Conference on Information Fusion, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages626-633
Number of pages8
ISBN (Electronic)9780996452748
StatePublished - Aug 1 2016
Event19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany
Duration: Jul 5 2016Jul 8 2016

Publication series

NameFUSION 2016 - 19th International Conference on Information Fusion, Proceedings

Other

Other19th International Conference on Information Fusion, FUSION 2016
Country/TerritoryGermany
CityHeidelberg
Period7/5/167/8/16

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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