User Privacy-aware Computation Offloading in Mobile Edge Computing Systems

With the rapid development of mobile communication and Internet of Things (IoT) technologies, smart mobile devices such as portable and sensor devices have been widely used in our daily lives. However, their compact size and limited energy capacity inherently hinder their ability to efficiently handle computation-intensive tasks within acceptable timeframes. To tackle this challenge, computation offloading has emerged as a pivotal solution. Computation offloading can significantly reduce the response time and energy consumption for mobile devices executing such tasks. However, it also bring some challenges, notably the risk of compromising user privacy. In this paper, we prioritize user privacy alongside considerations of service delay and energy consumption, and model the offloading decision problem as a multi-objective optimization problem. We employ an enhanced multi-objective bat algorithm to identify Pareto front solutions, balancing the diverse objectives effectively. Our experimental validation confirms the feasibility and efficacy of the proposed method, offering a promising avenue for addressing the complexities of computation offloading in mobile edge computing systems.

I. Introduction

In recent years, the proliferation of IoT technology alongside advancements in mobile communication has ushered in an era where intelligent mobile devices have become ubiquitous in our daily lives. Consequently, a myriad of sophisticated and computation-heavy applications, such as face recognition and augmented reality have emerged [1]. However, the inherent constraints of mobile and IoT devices, characterized by limited computing and storage resources, pose significant challenges in supporting these computation-intensive tasks. Furthermore, the energy demands associated with these tasks exert additional pressure on these intelligent devices [2].