TY - GEN
T1 - EDIR
T2 - 18th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
AU - Felemban, Noor
AU - Mehmeti, Fidan
AU - Porta, Thomas F.La
AU - Kwon, Heesung
N1 - Funding Information:
This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1109/MASS52906.2021.00056
DO - 10.1109/MASS52906.2021.00056
M3 - Conference contribution
AN - SCOPUS:85123934145
T3 - Proceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
SP - 392
EP - 400
BT - Proceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 October 2021 through 7 October 2021
ER -