Privacy-Aware Online Task Offloading for Mobile-Edge Computing
| Autor: | Yinlong Liu, Dali Zhu, Ting Li, Haitao Liu, Jiyan Sun, Liru Geng |
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| Rok vydání: | 2021 |
| Předmět: |
Technology
Optimization problem Article Subject Computer Networks and Communications Computer science 050801 communication & media studies TK5101-6720 02 engineering and technology Task (project management) 0508 media and communications Server 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Mobile edge computing business.industry 05 social sciences 020206 networking & telecommunications Energy consumption Telecommunication business Thompson sampling Mobile device 5G Information Systems Computer network |
| Zdroj: | Wireless Communications and Mobile Computing, Vol 2021 (2021) |
| ISSN: | 1530-8677 1530-8669 |
| DOI: | 10.1155/2021/6622947 |
| Popis: | Mobile edge computing (MEC) has been envisaged as one of the most promising technologies in the fifth generation (5G) mobile networks. It allows mobile devices to offload their computation-demanding and latency-critical tasks to the resource-rich MEC servers. Accordingly, MEC can significantly improve the latency performance and reduce energy consumption for mobile devices. Nonetheless, privacy leakage may occur during the task offloading process. Most existing works ignored these issues or just investigated the system-level solution for MEC. Privacy-aware and user-level task offloading optimization problems receive much less attention. In order to tackle these challenges, a privacy-preserving and device-managed task offloading scheme is proposed in this paper for MEC. This scheme can achieve near-optimal latency and energy performance while protecting the location privacy and usage pattern privacy of users. Firstly, we formulate the joint optimization problem of task offloading and privacy preservation as a semiparametric contextual multi-armed bandit (MAB) problem, which has a relaxed reward model. Then, we propose a privacy-aware online task offloading (PAOTO) algorithm based on the transformed Thompson sampling (TS) architecture, through which we can (1) receive the best possible delay and energy consumption performance, (2) achieve the goal of preserving privacy, and (3) obtain an online device-managed task offloading policy without requiring any system-level information. Simulation results demonstrate that the proposed scheme outperforms the existing methods in terms of minimizing the system cost and preserving the privacy of users. |
| Databáze: | OpenAIRE |
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