Deep Reinforcement Learning Cloud-Edge-Terminal Computation Resource Allocation Mechanism for IoT

Shu, Xinjian; Wu, Lei; Qin, Xiaoyang; Yang, Runhua; Wu, Yifan; Wang, Dequan; Liao, Boxian

doi:10.1007/978-981-15-8462-6_177

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Mobile edge computing and energy harvesting framework of centralized training with decentralized execution by adopting MD-hybrid-AC method [120]. Asynchronous advantage actor-critic method for mobile edge computing because computation offloading cannot have good performance in many situations, but the optimal algorithm can be chosen to use on IoTside [196].…”

Section: Classification Of Publications By Mathematical Approach Of Application Methodologymentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

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Section: Classification Of Publications By Mathematical Approach Of Application Methodologymentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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“…Another commonly used algorithm is the gradient-based algorithm. Shu et al [24] proposed a computation offloading decision model based on heuristic algorithms and the Asynchronous Advantage Actor-Critic (A3C) algorithm. In their study, the A3C algorithm is not directly employed to make decisions.…”

Section: Iot Networkmentioning

confidence: 99%

A comprehensive survey on computation offloading using reinforcement learning

Liang,

Qi,

Yang

et al. 2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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“…Cloud-edge-terminal architecture is originally designed for the collaboration of computing resources on different sides (Tian et al, 2019;Shu, et al, 2020). Although pavement inspection task does not need that much real-time computing resources, adopting distributed-architecture-based system design will be more flexible and adaptive for the future developments of 5G and internet of things technologies in civil airports.…”

Section: Airport Pavement Inspection Robotic System Overviewmentioning

confidence: 99%

A cloud-edge-terminal-based robotic system for airport runway inspection

Gui

Zhong

Wang

et al. 2021

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Purpose This paper aims to present a novel robotic system for airport pavement inspection tasks. Design/methodology/approach The cloud-edge-terminal-based distributed architecture is designed for the proposed robotic system. Then, the following three major parts are designed and deployed, respectively: Terminal: the wheeled-robot-based data collection system. Edge: remote monitoring and data analysis system. Cloud: shared database center of the inspection data and knowledge. Findings Validation and application results show that the proposed system satisfies the demands of automated airport pavement inspection tasks and saves the cost of manpower and time. Originality/value The proposed system provides a novel solution for the full process of airport pavement inspection. Compared with the traditional manual method, the robotic system can guarantee complete coverage and provide high-precision pavement inspection results with less time and labor costs.

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Deep Reinforcement Learning Cloud-Edge-Terminal Computation Resource Allocation Mechanism for IoT

Cited by 5 publications

References 7 publications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

A comprehensive survey on computation offloading using reinforcement learning

A cloud-edge-terminal-based robotic system for airport runway inspection

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