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Deep PDS-Learning for Privacy-Aware Offloading in MEC-Enabled IoT | IEEE Journals & Magazine | IEEE Xplore

Deep PDS-Learning for Privacy-Aware Offloading in MEC-Enabled IoT


Abstract:

The rapid uptake of Internet-of-Things (IoT) devices imposes an unprecedented pressure for data communication and processing on the backbone network and the central cloud...Show More

Abstract:

The rapid uptake of Internet-of-Things (IoT) devices imposes an unprecedented pressure for data communication and processing on the backbone network and the central cloud infrastructure. To overcome this issue, the recently advocated mobile-edge computing (MEC)-enabled IoT is promising. Meanwhile, driven by the growing social awareness of privacy, significant research efforts have been devoted to relevant issues in IoT; however, most of them mainly focus on the conventional cloud-based IoT. In this paper, a new privacy vulnerability caused by the wireless offloading feature of MEC-enabled IoT is identified. To address this vulnerability, an effective privacy-aware offloading scheme is developed based on a newly proposed deep post-decision state (PDS)-learning algorithm. By exploiting extra prior information, the proposed deep PDS-learning algorithm allows the IoT devices to learn a good privacy-aware offloading strategy much faster than the conventional deep Q-network. Theoretic analysis and numerical results are provided to corroborate the correctness and the effectiveness of the proposed algorithm.
Published in: IEEE Internet of Things Journal ( Volume: 6, Issue: 3, June 2019)
Page(s): 4547 - 4555
Date of Publication: 30 October 2018

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