Edge Intelligence for Plug-in Electrical Vehicle Charging Service

Zhang, Yanru; Hong, Feng; Wang, Yan; Liu, Zhi; Zhou, Yingjie; Chang, Zheng

doi:10.1109/mnet.011.2000587

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Secondly, MCC also has some security issues, as the data and applications stored on mobile devices may contain sensitive information, and there is a risk of data leakage when such information is transferred to the cloud. In order to address this problem, a mobile edge computing (MEC) network has emerged [8][9][10]. The core idea of the MEC network is to migrate computational resources in the upper network to the edge of the lower network by configuring high-speed computational servers for the network edge devices, such as base stations (BSs), in order to provide computationally intensive users with nearby computational support.…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Approach for Computation Offloading in MEC Dynamic Networks

Fan,

Cai

2023

Preprint

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In this paper, we study the dynamic time-slot scheme server selection problem in mobile edge computing (MEC) system, which takes into account the dynamic access of users at edge servers and introduces the dynamically changing factors affecting the workload of edge servers, such as offloading policies, offloading ratios, user’s transmitting power, and reserved capacity of the servers. However, the above factors make it tricky to achieve long-term optimization in the edge server selection process. To cope with the above challenges, we model the server selection problem faced as a Markov Decision Process (MDP) and propose a Deep Reinforcement Learning (DRL) based algorithm to solve it. Selection strategy learning is performed based on the observed server selection performance in previous phases. Simulation results show that the DRL-based algorithm proposed in this paper has the lowest average latency compared to benchmark algorithms.

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Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Approach for Computation Offloading in MEC Dynamic Networks

Fan,

Cai

2023

Preprint

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Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey

Duan

Wang

Ren

et al. 2023

IEEE Commun. Surv. Tutorials

115

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A deep reinforcement approach for computation offloading in MEC dynamic networks

Fan,

Cai

2024

EURASIP J. Adv. Signal Process.

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In this study, we investigate the challenges associated with dynamic time slot server selection in mobile edge computing (MEC) systems. This study considers the fluctuating nature of user access at edge servers and the various factors that influence server workload, including offloading policies, offloading ratios, users’ transmission power, and the servers’ reserved capacity. To streamline the process of selecting edge servers with an eye on long-term optimization, we cast the problem as a Markov Decision Process (MDP) and propose a Deep Reinforcement Learning (DRL)-based algorithm as a solution. Our approach involves learning the selection strategy by analyzing the performance of server selections in previous iterations. Simulation outcomes show that our DRL-based algorithm surpasses benchmarks, delivering minimal average latency.

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Edge Intelligence for Plug-in Electrical Vehicle Charging Service

Cited by 3 publications

References 15 publications

A Deep Reinforcement Approach for Computation Offloading in MEC Dynamic Networks

A Deep Reinforcement Approach for Computation Offloading in MEC Dynamic Networks

Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey

A deep reinforcement approach for computation offloading in MEC dynamic networks

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