Abstract
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.
Highlights
In the recent years, with the advent of 5G network, as well as the fast popularization of mobile devices, a myriad of new applications is emerging, such as augmented reality (AR)/virtual reality (VR) [1, 2], online 3D games [3], and connected cars [4]
Previous works require system-level information to design an optimal task offloading strategy, but this is not applicable to infrastructure-free scenarios. We address this second challenge by utilizing the contextual feature vector in the contextual multi-armed bandit (MAB) model to describe userside information and applying the Thompson sampling (TS) algorithm to estimate and learn the performance model based on the contextual information
Extensive simulations are conducted to evaluate the performance of the proposed privacy-aware online task offloading (PAOTO) algorithm under different scenarios
Summary
With the advent of 5G network, as well as the fast popularization of mobile devices, a myriad of new applications is emerging, such as augmented reality (AR)/virtual reality (VR) [1, 2], online 3D games [3], and connected cars [4]. The most related works probably are [10, 15], which studied the optimization of delay and energy consumption cost while considering both location privacy and usage pattern privacy The former scheme formulates this problem as a constrained Markov decision process (CMDP), and the latter one applies a Dyan-Q architecture based on the CMDP to achieve a better privacy-aware offloading policy. In order to minimize system cost (e.g., latency and energy consumption) and protect user’s privacy without requiring any system-level information as a prior knowledge, we propose a device-level and privacy-preserving task offloading scheme for the MEC system This scheme is based on a semiparametric contextual multi-armed bandit (MAB) problem, which can address the trade-offs inherent in the sequential decision problem and overcome the challenges of lacking system-side information.
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