Entropy-Aware Task Offloading in Mobile Edge Computing
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach
Mobile edge computing (MobEC) builds an Information Technology (IT) service environment to enable cloud-computing capabilities at the edge of mobile networks. To tackle the restrictions in the battery power and computati…
Cloud ComputingEdge-computingQ-Learningreinforcement-learning+1Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks
Computational offloading has become an enabling component for edge intelligence in mobile and smart devices. Existing offloading schemes mainly focus on mobile devices and servers, while ignoring the potential network co…
SchedulingDistributed Task Offloading and Resource Allocation for Latency Minimization in Mobile Edge Computing Networks
The growth in artificial intelligence (AI) technology has attracted substantial interests in latency-aware task offloading of mobile edge computing (MEC)-namely, minimizing service latency. Additionally, the use of MEC s…
Combinatorial OptimizationEdge-computingPrivacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning
Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the mining process in mobile blockchain requires high computational and storage…
Deep Reinforcement LearningEdge-computingreinforcement-learningReinforcement Learning (RL)Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach
We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wire…
Deep Reinforcement LearningEdge-computingreinforcement-learningReinforcement Learning+1