2018 journal article

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

IEEE INTERNET OF THINGS JOURNAL, 6(3), 4547–4555.

By: X. He*, R. Jin n & H. Dai n

author keywords: Deep learning; Internet-of-Things (IoT); mobile-edge computing (MEC); post-decision state (PDS); privacy
topics (OpenAlex): IoT and Edge/Fog Computing; Privacy-Preserving Technologies in Data; Mobile Crowdsensing and Crowdsourcing
TL;DR: In this paper, a new privacy vulnerability caused by the wireless offloading feature of MEC-enabled IoT is identified and an effective privacy-aware offloading scheme is developed based on a newly proposed deep post-decision state (PDS)-learning algorithm. (via Semantic Scholar)
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Source: Web Of Science
Added: July 15, 2019

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.