@inproceedings{0b920b13eba5470faaf75e07d14fedf7,
title = "EDIR: Efficient Distributed Image Retrieval of Novel Objects in Mobile Networks",
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.",
keywords = "Classification, Deep learning, Mobile networks, Novelty detection",
author = "Noor Felemban and Fidan Mehmeti and Porta, \{Thomas F.La\} and Heesung Kwon",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021 ; Conference date: 04-10-2021 Through 07-10-2021",
year = "2021",
doi = "10.1109/MASS52906.2021.00056",
language = "English",
series = "Proceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "392--400",
booktitle = "Proceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021",
}