Entropy-Aware Task Offloading in Mobile Edge Computing
This addresses privacy concerns for mobile users in edge computing, but it is incremental as it builds on existing offloading schemes by adding privacy considerations.
The paper tackles privacy issues in mobile edge computing task offloading, focusing on usage pattern and location privacy, and proposes a method using a Deep Recurrent Q-Network to solve the Markov Decision Process, with effectiveness demonstrated through numerical simulations.
Mobile Edge Computing (MEC) technology has been introduced to enable could computing at the edge of the network in order to help resource limited mobile devices with time sensitive data processing tasks. In this paradigm, mobile devices can offload their computationally heavy tasks to more efficient nearby MEC servers via wireless communication. Consequently, the main focus of researches on the subject has been on development of efficient offloading schemes, leaving the privacy of mobile user out. While the Blockchain technology is used as the trust mechanism for secured sharing of the data, the privacy issues induced from wireless communication, namely, usage pattern and location privacy are the centerpiece of this work. The effects of these privacy concerns on the task offloading Markov Decision Process (MDP) is addressed and the MDP is solved using a Deep Recurrent Q-Netwrok (DRQN). The Numerical simulations are presented to show the effectiveness of the proposed method.