Cooperative Resource Allocation for Computation-Intensive IIoT Applications in Aerial Computing

Liu, Jialei; Li, Guosheng; Huang, Quanzhen; Bilal, Muhammad; Xu, Xiaolong; Song, Houbing

doi:10.1109/jiot.2022.3222340

Cited by 11 publications

(3 citation statements)

References 44 publications

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“…Ma et al [28] designed a novel task-offloading framework that vehicles' computation jobs could execute natively; otherwise, they were offloaded to UAVs and ECDs. Liu et al [29] established the processor resources and energy consumption model for the task-offloading approach. Dinh et al [30] proposed a communication method that utilizes the flexible deployment of UAVs and their cooperative transmissions to improve acccessing in the networking.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

Li,

Qu,

Liu

et al. 2024

Electronics

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With the increasing dissemination of the Internet of Things and 5G, mobile edge computing has become a novel scheme to assist terminal devices in executing computation tasks. To elevate the coverage and computation capability of edge computing, a collaborative computation offloading and resource management architecture was proposed in space–air–ground integrated networking (SAGIN). In this manuscript, we established a novel model considering the computation offloading cost constraints of the communication, computing and cache model in the SAGIN. To be specific, the joint optimization problem of collaborative computation offloading and resource management was modeled as a mixed integer nonlinear programming problem. To address this issue, this paper proposed a computation offloading and resource allocation strategy based on deep reinforcement learning (DRL). Differing from traditional methods, DRL does not need a well-established formulation or previous information, and it is capable of revising the strategy adaptively according to the environment. The simulation results demonstrate the proposed approach can achieve the optimal reward values in the case of different terminal device numbers. Furthermore, this manuscript provided the analysis with variant parameters of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

Li,

Qu,

Liu

et al. 2024

Electronics

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“…With the support of intelligent network technology based on AI, edge computing, and the Internet of Things, IoV technology deeply integrates cars with the network. It enables intelligent interconnection among vehicles and between vehicles and road networks [3][4][5]. This brings users a more intelligent, convenient, and secure travel experience.…”

Section: Introductionmentioning

confidence: 99%

“…When the element of the jth row of the ith column is 1, it means that task i is the predecessor task of task j. For example, g 5 2 = 1 indicates that task 5 is the successor task of task 2 and task 2 is the predecessor task of task 5. g 5 2 = 0 indicates that there is no dependency between task 5 and task 2. Action Space: The action of the agent is to select the task to be offloaded to the ES under the current BS or transferred to the ES under another BS.…”

mentioning

confidence: 99%

Dependency-aware Online Task Offloading based on Deep Reinforcement Learning for IoV

Liu,

Wang,

Zhao

et al. 2024

Preprint

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The integration of artificial intelligence with vehicle wireless communicationtechnology willing meet the intelligent communication needs of the Internetof Vehicles (IoV), supporting more sophisticated vehicle applications. However,making real-time dependency-aware task offloading decisions is difficult due tothe high mobility of vehicles and the dynamic nature of the network environment.This leads to additional application computation time and energy consumption,increasing the risk of offloading failures for computationally intensive andlatency-sensitive applications. In this paper, an offloading strategy for vehicleapplications that jointly considers latency and energy consumption in the basestation cooperative computing model is proposed. Firstly, we establish a collaborativeoffloading model involving multiple vehicles, multiple base stations,and multiple edge servers. Transferring vehicular applications to the applicationqueue of edge servers and prioritizing them based on their completion deadlines.Secondly, each vehicular application is modeled as a directed acyclic graph(DAG) task with data dependency relationships. Subsequently, we propose atask offloading method based on task dependency awareness in deep reinforcementlearning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle applicationcompletion time and reducing edge server energy consumption. Finally,simulation results show that compared with the heuristic method, our proposedDAG-DQN method reduces task completion time by 16%, reduces system energyconsumption by 19%, and improves decision-making efficiency by 70%.

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Ets-ddpg: an energy-efficient and QoS-guaranteed edge task scheduling approach based on deep reinforcement learning

Zhao,

Xia,

Sun

et al. 2024

Wireless Netw

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Cooperative Resource Allocation for Computation-Intensive IIoT Applications in Aerial Computing

Abstract: County (UMBC)ScholarWorks@UMBC digital repository on the Maryland Shared Open Access (MD-SOAR) platform.

Cited by 11 publications

References 44 publications

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

Dependency-aware Online Task Offloading based on Deep Reinforcement Learning for IoV

Ets-ddpg: an energy-efficient and QoS-guaranteed edge task scheduling approach based on deep reinforcement learning

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