EDIR: Efficient Distributed Image Retrieval of Novel Objects in Mobile Networks

Noor Felemban, Fidan Mehmeti, Thomas F.La Porta, Heesung Kwon

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

1 Scopus citations

Abstract

Crowdsourcing data collection from a network of mobile devices is useful in various applications. Mobile devices store a large amount of visual data that aid in different situations. Trained CNNs can be deployed on mobile devices to be used in searching for objects of interest. Querying for novel objects, for which models have not been trained, presents unique challenges. When novel objects are queried, new models must be trained and distributed to all edge devices, which can be cumbersome. In this paper we propose EDIR, an efficient method and a system that enables answering these queries while taking into account the bandwidth limitations in wireless networks, and the limited energy and computational power on mobile devices. Results show that EDIR reduces the amount of data transfer by 45%compared to other approaches while achieving a good F1 score.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages392-400
Number of pages9
ISBN (Electronic)9781665449359
DOIs
StatePublished - 2021
Event18th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021 - Virtual, Online, United States
Duration: Oct 4 2021Oct 7 2021

Publication series

NameProceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021

Conference

Conference18th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
Country/TerritoryUnited States
CityVirtual, Online
Period10/4/2110/7/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture

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