基于“通算存”资源协同的无人机链路异常监测方案研究

王清; 夏晓东; 荆臻; 代燕杰; 孙建德; 冯传奋

doi:10.11959/j.issn.1000-0801.2025010

您当前的位置：

首页 >

文章列表页 >

基于“通算存”资源协同的无人机链路异常监测方案研究

工程与应用 | 更新时间：2025-03-11

- 基于“通算存”资源协同的无人机链路异常监测方案研究
- Research on link anomaly monitoring scheme of unmanned aerial vehicle based on “communication-calculation-caching” resource collaboration
- 电信科学 2025年41卷第2期页码：168-181
- 作者机构：
  
  1.国网山东省电力公司营销服务中心（计量中心），山东济南 250013
  2.山东师范大学，山东济南 250014
- 作者简介：
  
  [ "王清（1984- ），男，博士，国网山东省电力公司营销服务中心（计量中心）高级工程师，主要研究方向为电能计量、通信与信息系统。" ]
  [ "夏晓东（1979- ），男，国网山东省电力公司营销服务中心（计量中心）高级工程师，主要研究方向为电能计量。" ]
  [ "荆臻（1981- ），女，国网山东省电力公司营销服务中心（计量中心）高级工程师，主要研究方向为电能计量。" ]
  [ "代燕杰（1978- ），女，国网山东省电力公司营销服务中心（计量中心）正高级工程师，主要研究方向为电能计量。" ]
  [ "孙建德（1978- ），男，博士，山东师范大学教授，主要研究方向为多媒体信息处理、信息检索、信息安全、立体显示及人机交互。" ]
  [ "冯传奋（1980- ），男，博士，山东师范大学正高级工程师，主要研究方向为移动通信网络规划设计、云-边-端协同等。" ]
- 基金信息：
  
  国网山东省电力公司科技项目(2023A-018)
- DOI：10.11959/j.issn.1000-0801.2025010
  中图分类号： TN929
- 收稿：2024-07-06，
  
  修回：2024-12-19，
  
  纸质出版：2025-02-20
- 稿件说明：
移动端阅览
王清,夏晓东,荆臻等.基于“通算存”资源协同的无人机链路异常监测方案研究[J].电信科学,2025,41(02):168-181.

WANG Qing,XIA Xiaodong,JING Zhen,et al.Research on link anomaly monitoring scheme of unmanned aerial vehicle based on “communication-calculation-caching” resource collaboration[J].Telecommunications Science,2025,41(02):168-181.
王清,夏晓东,荆臻等.基于“通算存”资源协同的无人机链路异常监测方案研究[J].电信科学,2025,41(02):168-181. DOI： 10.11959/j.issn.1000-0801.2025010.

WANG Qing,XIA Xiaodong,JING Zhen,et al.Research on link anomaly monitoring scheme of unmanned aerial vehicle based on “communication-calculation-caching” resource collaboration[J].Telecommunications Science,2025,41(02):168-181. DOI： 10.11959/j.issn.1000-0801.2025010.

摘要

近年来无人机（unmanned aerial vehicle，UAV）以其低成本、高灵活的特点在网络链路监测领域得到了广泛应用，但其高移动性、电量受限特性使监测任务实时、高效处理面临严峻的挑战。以智慧电力中通过无人机进行链路异常监测应用为例，针对不同监测场景需求，基于云、边、端节点的通信资源、计算资源、存储资源，综合考虑任务卸载与迁移联合优化，构建了基于“通算存”资源协同的任务处理模型，提出了基于“通算存”资源协同效益最优和时延最优两种链路异常监测方案。仿真实验揭示了基于“通算存”资源协同任务处理的内在机理。结果表明，与传统基于“通算”资源协同方案相比，所提链路异常监测方案具有更高的任务处理效益或更低的任务处理时延。

Abstract

In recent years

unmanned aerial vehicles (UAVs) have been widely used in the field of network link monitoring due to their low cost and high flexibility. However

the characteristics of high mobility and limited battery capacity of UAV impose serious challenges on real-time and high benefit processing of monitoring tasks. The scenario of link anomaly monitoring through drones in smart power was taken as an example. Considering the joint optimization of task offloading and migration

a task processing model based on “communication-calculation-caching” resource collaboration was constructed in order to meet the needs of different monitoring scenarios

which took into account the communication resources

computing resources

and storage resources of cloud

edge and end nodes. Two link anomaly monitoring schemes based on “communication-calculation-caching” resource collaboration with optimal benefit and optimal delay were proposed. The inherent mechanism of collaborative task processing based on “communication-calculation-caching” resources was revealed through simulation experiments. The results indicate that the proposed link anomaly monitoring scheme has higher task processing benefit or lower task processing delay compared to traditional link anomaly monitoring scheme based on “communication-calculation” resource collaboration.

关键词

Keywords

references

LI H X , SHOU G C , HU Y H , et al . Mobile edge computing: progress and challenges [C ] // Proceedings of the 2016 4th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) . Piscataway : IEEE Press , 2016 : 83 - 84 .

WEI X J , WANG S G , ZHOU A , et al . MVR: an architecture for computation offloading in mobile edge computing [C ] // Proceedings of the 2017 IEEE International Conference on Edge Computing (EDGE) . Piscataway : IEEE Press , 2017 : 232 - 235 .

ABBAS N , ZHANG Y , TAHERKORDI A , et al . Mobile edge computing: a survey [J ] . IEEE Internet of Things Journal , 2018 , 5 ( 1 ): 450 - 465 .

ZHAO P T , TIAN H , FAN S S , et al . Information prediction and dynamic programming-based RAN slicing for mobile edge computing [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 4 ): 614 - 617 .

HAO Y X , CHEN M , HU L , et al . Energy efficient task caching and offloading for mobile edge computing [J ] . IEEE Access , 2018 ( 6 ): 11365 - 11373 .

SONG Z Y , MA R J , XIE Y . A collaborative task offloading strategy for mobile edge computing in Internet of vehicles [C ] // Proceedings of the 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) . Piscataway : IEEE Press , 2021 : 1379 - 1384 .

陈沛锐 . 基于深度学习的边缘计算资源协同方案研究 [D ] . 重庆 : 重庆邮电大学 , 2020 .

CHEN P R . Research on collaborative scheme of edge computing resources based on deep learning [D ] . Chongqing : Chongqing University of Posts and Telecommunications , 2020 .

KIM T , KIM Y , ADU E , et al . On offloading decision for mobile edge computing systems considering access reservation protocol [J ] . IEEE Access , 2023 ( 11 ): 40457 - 40468 .

ZHANG X R , PENG M G , YAN S , et al . Joint communication and computation resource allocation in fog-based vehicular networks [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 15 ): 13195 - 13208 .

BAI J J , LIU X , ZHU X R , et al . Joint optimization of task offloading and resource allocation based on edge-terminal collaboration [C ] // Proceedings of the 2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) . Piscataway : IEEE Press , 2023 : 1 - 6 .

XU C , TANG Z X , YU H B , et al . Digital twin-driven collaborative scheduling for heterogeneous task and edge-end resource via multi-agent deep reinforcement learning [J ] . IEEE Journal on Selected Areas in Communications , 2023 , 41 ( 10 ): 3056 - 3069 .

LIN Y , KE F , CHEN M L , et al . Joint communication, sensing and computing for V2I networks [C ] // Proceedings of the 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall) . Piscataway : IEEE Press , 2023 : 1 - 5 .

王心一 , 陈志江 , 雷磊 , 等 . 多无人机网络边缘智能计算卸载算法 [J ] . 数据采集与处理 , 2023 , 38 ( 6 ): 1286 - 1298 .

WANG X Y , CHEN Z J , LEI L , et al . Computation offloading algorithm for multi-UAV network based on edge intelligence [J ] . Journal of Data Acquisition and Processing , 2023 , 38 ( 6 ): 1286 - 1298 .

WANG D Y , LIU Z L , WANG X X , et al . Mobility-aware task offloading and migration schemes in fog computing networks [J ] . IEEE Access , 2019 , 7 : 43356 - 43368 .

WANG D Y , TIAN X Q , CUI H R , et al . Reinforcement learning-based joint task offloading and migration schemes optimization in mobility-aware MEC network [J ] . China Communications , 2020 , 17 ( 8 ): 31 - 44 .

FENG C F , SUN J D . Mobility-aware task offloading scheme based on optimal system benefit in multi-access edge computing [M ] // Lecture Notes in Electrical Engineering . Singapore : Springer Nature Singapore , 2022 : 1025 - 1033 .

LI X J , TANG J , XU Y , et al . Mobility-aware multi-task migration and offloading scheme for Internet of vehicles [J ] . Chinese Journal of Electronics , 2023 , 32 ( 6 ): 1192 - 1202 .

CHEN M H , LIANG B , DONG M . Joint offloading and resource allocation for computation and communication in mobile cloud with computing access point [C ] // Proceedings of the IEEE INFOCOM 2017-IEEE Conference on Computer Communications . Piscataway : IEEE Press , 2017 : 1 - 9 .

浏览量

293

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于区块链的频谱共享算法

低空3.5 GHz双载波网络覆盖解决方案研究

MEC业务连续性技术研究与应用

基于MEC的车联网协作组网关键技术

基于人工智能的基站巡检应用实践