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Privacy-Aware Forecasting of Quality of Service in Mobile Edge Computing | IEEE Journals & Magazine | IEEE Xplore

Privacy-Aware Forecasting of Quality of Service in Mobile Edge Computing


Abstract:

We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment – Edge-PMAM (Edge QoS forecasting with Public Model and Atte...Show More

Abstract:

We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment – Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make real-time, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results demonstrate that our approach can effectively improve forecasting performance and protect user privacy.
Published in: IEEE Transactions on Services Computing ( Volume: 16, Issue: 1, 01 Jan.-Feb. 2023)
Page(s): 478 - 492
Date of Publication: 23 December 2021

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