Impact of User Privacy and Mobility on Edge Offloading

TL;DR

This paper examines how user privacy and mobility patterns influence the effectiveness of edge offloading, highlighting trade-offs between privacy, latency, and performance through simulated scenarios.

Contribution

It provides a comprehensive analysis of privacy and mobility impacts on edge offloading, offering insights into balancing privacy protection with system performance.

Findings

Privacy protection can increase latency in offloading systems.

Mobility patterns significantly affect offloading efficiency.

Trade-offs exist between user privacy and QoE in edge computing.

Abstract

Offloading high-demanding applications to the edge provides better quality of experience (QoE) for users with limited hardware devices. However, to maintain a competitive QoE, infrastructure, and service providers must adapt to users' different mobility patterns, which can be challenging, especially for location-based services (LBS). Another issue that needs to be tackled is the increasing demand for user privacy protection. With less (accurate) information regarding user location, preferences, and usage patterns, forecasting the performance of offloading mechanisms becomes even more challenging. This work discusses the impacts of users' privacy and mobility when offloading to the edge. Different privacy and mobility scenarios are simulated and discussed to shed light on the trade-offs (e.g., privacy protection at the cost of increased latency) among privacy protection, mobility, and offloading performance.