DPRL: Task Offloading Strategy Based on Differential Privacy and Reinforcement Learning in Edge Computing

نویسندگان

چکیده

Mobile edge computing has been widely used in various IoT devices due to its excellent power and good interaction speed. Task offloading is the core of mobile computing. However, most existing task strategies only focus on improving unilateral performance MEC, such as security, delay, overhead. Therefore, delay overhead we propose a strategy based differential privacy reinforcement learning. This optimizes required for process while protecting user privacy. Specifically, before offloading, interfere with user’s location information avoid malicious servers from stealing Then, basis ensuring combined resource environment MEC network, learning select appropriate offloading. Simulation results show that our scheme improves many aspects, especially security consumption. Compared typical protection scheme, improved by 7%, consumption reduced 9% compared strategy.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2022

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2022.3175194