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

• Published in: IEEE internet of things journal Vol. 6; no. 3; pp. 4547 - 4555
• Main Authors: He, Xiaofan, Jin, Richeng, Dai, Huaiyu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Computation offloading; Deep learning; Edge computing; Internet of Things; Internet-of-Things (IoT); Machine learning; Mobile computing; mobile-edge computing (MEC); Numerical models; post-decision state (PDS); Privacy; Servers; Task analysis; Wireless communication
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2018.2878718

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 <inline-formula> <tex-math notation="LaTeX">{Q} </tex-math></inline-formula>-network. Theoretic analysis and numerical results are provided to corroborate the correctness and the effectiveness of the proposed algorithm.