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DNN Retraining Method Reducing Accuracy Degradation in Packet-Lossy Environments
Dongwhee Kim, Yujin Lim, Syngha Han, Jungrae Kim
http://doi.org/10.5626/JOK.2023.50.3.285
Limited resources on mobile devices have necessitated a collaboration with cloud servers, called “Collaborative Intelligence”, to process growing Deep Neural Network (DNN) model sizes. Collaborative intelligence takes a long time to send a lot of feature data from clients to servers. One can reduce the transfer time using User Datagram Protocol (UDP), but a dropped packet during UDP transfer reduces inference accuracy. This paper proposed a DNN retraining method to develop a robust DNN model. The server-side layers are retrained to avoid lossy features by modeling continuous feature losses resulting from a packet drop. Our results showed that it can reduce accuracy reduction from packet losses, provide high accuracy reliability against changes in the communication environment, and reduce the storage overheads of mobile devices.
Service Migration Based on Reinforcement Learning in Vehicular Edge Computing
http://doi.org/10.5626/JOK.2021.48.2.243
As edge computing can provide low latency and real-time services, it is emerging as a promising technology that can lead the Internet of things(IoT). However, user mobility and limited coverage of edge computing result in service interruption and reduce Quality of Service(QoS). Thus, service migration is considered an important issue to guarantee seamless service. In this paper, a migration decision algorithm is proposed using Q-learning, as a reinforcement learning method in the vehicular edge computing environment. The proposed algorithm decides whether or not to migrate and where to migrate in order to meet delay constraint and minimize system cost. In the performance evaluation, we compared propsed algorithm with other algorithms in terms of deciding whether or not to migrate and where to migrate, and our proposed algorithm shows better performance compared to the other algorithms.
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