面向天基网络的紧急任务调度算法

李喆元; 何立军; 贾子晔; 李泊慷; 汪彦婷; 闵明慧

doi:10.11959/j.issn.2096-3750.2025.00474

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面向天基网络的紧急任务调度算法

专题：卫星物联网 | 更新时间：2025-04-21

- 面向天基网络的紧急任务调度算法
- Emergency task scheduling algorithm for space-based networks
- 物联网学报 2025年9卷第1期页码：59-70
- 作者机构：
  
  1.西北工业大学软件学院，陕西西安 710072
  2.中国矿业大学信息与控制工程学院，江苏徐州 221116
  3.南京航空航天大学电子信息工程学院，江苏南京 210024
  4.香港城市大学计算学院，香港 999077
- 作者简介：
  
  [ "李喆元（2001‒ ），男，西北工业大学软件学院硕士生，主要研究方向为空天地一体化网络、无线通信。" ]
  [ "何立军（1989‒ ），男，博士，中国矿业大学信息与控制工程学院副教授，主要研究方向为空天地一体化网络、无人机组网、无线通信。" ]
  [ "贾子晔（1990‒ ），女，博士，南京航空航天大学电子信息工程学院副教授，主要研究方向为低空智联网、无人机、空天地一体化网络。" ]
  [ "李泊慷（1998‒ ），男，香港城市大学计算学院硕士生，主要研究方向为大数据分析。" ]
  [ "汪彦婷（1989‒ ），女，博士，西北工业大学软件学院讲师，主要研究方向为边缘计算、群智网络、模型剪枝。" ]
  [ "闵明慧（1991‒ ），女，博士，中国矿业大学信息与控制工程学院讲师，主要研究方向为无线通信、网络安全、隐私保护、强化学习。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62201463);江苏省自然科学基金资助项目(BK20220883)
- DOI：10.11959/j.issn.2096-3750.2025.00474
  中图分类号： TN929.5;
- 收稿日期：2024-12-29，
  
  修回日期：2025-02-12，
  
  纸质出版日期：2025-03-30
- 稿件说明：
移动端阅览
李喆元,何立军,贾子晔等.面向天基网络的紧急任务调度算法[J].物联网学报,2025,09(01):59-70.

LI Zheyuan,HE Lijun,JIA Ziye,et al.Emergency task scheduling algorithm for space-based networks[J].Chinese Journal on Internet of Things,2025,09(01):59-70.
李喆元,何立军,贾子晔等.面向天基网络的紧急任务调度算法[J].物联网学报,2025,09(01):59-70. DOI： 10.11959/j.issn.2096-3750.2025.00474.

LI Zheyuan,HE Lijun,JIA Ziye,et al.Emergency task scheduling algorithm for space-based networks[J].Chinese Journal on Internet of Things,2025,09(01):59-70. DOI： 10.11959/j.issn.2096-3750.2025.00474.

摘要

天基网络作为空天地一体化网络重要组成部分，具备覆盖范围广、吞吐量高以及抗灾能力强等优势，被广泛应用于应急通信等领域。在应急救援场景下，灾区短时间生成海量、高时间敏感性业务数据，亟须通过天基网络快速回传，以减少损失。然而，天基网络的网络资源受限且数据传输遵循时间窗等约束，导致数据传输时延高，大大降低了天基网络的应急能力。为此，提出了一种面向天基网络的紧急任务调度机制，实现了应急场景下紧急任务与网络资源精准匹配以最小化紧急任务最大传输时延。首先，将紧急任务调度问题建模为整数规划问题；其次，提出了一种新的高效编码方式，压缩解空间，为高效求解提供保障；最后，采用全局搜索和局部搜索相结合的思路提出了一种高效调度策略，即在遗传算法的架构中融入鲸鱼优化算法进行局部搜索，降低紧急任务最大传输时延。仿真结果表明，所提算法有良好的收敛性，并能有效降低紧急任务最大传输时延，提升天基网络的应急能力。

Abstract

As an essential component of the space-air-ground integrated network

the space-based network has advantages such as wide coverage

high throughput

and strong disaster resilience

and is widely applied in emergency communication and other fields. In the scenario of emergency rescue

a large amount of high-time-sensitive data is generated within a short period in the disaster-stricken area. It is urgently necessary to be quickly relayed back via the space-based network to reduce losses. However

the network resources of the space-based network are limited

and data transmission needs to follow constraints such as time windows

resulting in high transmission delay and significantly reducing the emergency response capacity of the space-based network. Therefore

an emergency task scheduling mechanism for space-based networks was proposed to achieve precise matching of emergency tasks with the limited resources of the space-based network to minimize the transmission delay of emergency tasks. Firstly

the emergency task scheduling problem was modeled as an integer programming problem to minimize the maximum transmission delay of emergency tasks. Secondly

a new efficient encoding method was proposed to compress the solution space and provide a guarantee for the efficient solution of the problem. Furthermore

a high-efficiency scheduling strategy was proposed by combining global search and local search

that is

integrating the whale optimization algorithm into the framework of the genetic algorithm for local search

which reduces the maximum transmission delay of emergency tasks. Simulation results show that the proposed algorithm has good convergence and can effectively reduce the maximum transmission delay of emergency tasks

enhancing the emergency data transmission performance of the space-based network.

关键词

Keywords

references

周笛 , 盛敏 , 郝琪 , 等 . 巨型星座系统的网络运维与资源管控技术 [J ] . 天地一体化信息网络 , 2020 , 1 ( 1 ): 26 - 35 .

ZHOU D , SHENG M , HAO Q , et al . Network operation, maintenance and resource management in mega constellation system [J ] . Space-Integrated-Ground Information Networks , 2020 , 1 ( 1 ): 26 - 35 .

FENG H L , CUI Z Q , HAN C Z , et al . Bidirectional green promotion of 6G and AI: architecture, solutions, and platform [J ] . IEEE Network , 2021 , 35 ( 6 ): 57 - 63 .

MENG X , ZHANG N , JIAN M N , et al . Channel modeling and estimation for reconfigurable-intelligent-surface-based 6G SAGIN IoT [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 11 ): 9273 - 9282 .

QI F , MANG G , ZHANG S W , et al . A multi-layer architecture for space-air-ground network and IoT services [C ] // Proceedings of the 2021 International Wireless Communications and Mobile Computing (IWCMC) . Piscataway : IEEE Press , 2021 : 1809 - 1813 .

沈学民 , 承楠 , 周海波 , 等 . 空天地一体化网络技术: 探索与展望 [J ] . 物联网学报 , 2020 , 4 ( 3 ): 1 - 19 .

SHEN X M , CHENG N , ZHOU H B , et al . Space-air-ground integrated networks: review and prospect [J ] . Chinese Journal on Internet of Things , 2020 , 4 ( 3 ): 1 - 19 .

CUI H X , ZHANG J , GENG Y H , et al . Space-air-ground integrated network (SAGIN) for 6G: requirements, architecture and challenges [J ] . China Communications , 2022 , 19 ( 2 ): 90 - 108 .

HASSAN N U L , HUANG C W , YUEN C , et al . Dense small satellite networks for modern terrestrial communication systems: benefits, infrastructure, and technologies [J ] . IEEE Wireless Communications , 2020 , 27 ( 5 ): 96 - 103 .

ZHOU D , SHENG M , LI J D , et al . Aerospace integrated networks innovation for empowering 6G: a survey and future challenges [J ] . IEEE Communications Surveys & Tutorials , 2023 , 25 ( 2 ): 975 - 1019 .

ABDELSADEK M Y , KARABULUT-KURT G , YANIKOMEROGLU H , et al . Broadband connectivity for handheld devices via LEO satellites: is distributed massive MIMO the answer? [J ] . IEEE Open Journal of the Communications Society , 2023 , 4 : 713 - 726 .

JI S J , ZHOU D , SHENG M , et al . Dynamic space-ground integrated mobility management strategy for mega LEO satellite constellations [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 9 ): 11043 - 11060 .

MAO S , LIU L , HOU X W , et al . Multi-domain resource management for space-air-ground integrated sensing, communication, and computation networks [J ] . IEEE Journal on Selected Areas in Communications , 2024 , 42 ( 12 ): 3380 - 3394 .

XIAO Y L , ZHANG T , SUN M . Geographical addressing strategy for space-ground integrated network [J ] . International Journal of Satellite Communications and Networking , 2021 , 39 ( 2 ): 178 - 192 .

ROJANASOONTHON S . Parallel machine scheduling with time windows [D ] . Austin, USA : University of Texas at Austin , 2004 .

LV Z Q , AN N , FAN C B , et al . Research on satellite laser ranging observation task scheduling method [J ] . Measurement Science and Technology , 2024 , 35 ( 4 ): 1 - 9 .

FEI H X , ZHANG X , LONG J , et al . Towards multi-satellite collaborative computing via task scheduling based on genetic algorithm [J ] . Aerospace , 2023 , 10 ( 2 ): 95 .

HE L J , LIANG B , LI J D , et al . Joint observation and transmission scheduling in agile satellite networks [J ] . IEEE Transactions on Mobile Computing , 2022 , 21 ( 12 ): 4381 - 4396 .

XIONG M H , XIONG W , LIU Z . A co-evolutionary algorithm with elite archive strategy for generating diverse high-quality satellite range schedules [J ] . Complex & Intelligent Systems , 2023 , 9 ( 5 ): 5157 - 5172 .

LIU Y , ZHANG S Y , HU H Y . A simulated annealing algorithm with tabu list for the multi-satellite downlink schedule problem considering waiting time [J ] . Aerospace , 2022 , 9 ( 5 ): 235 .

HU Y F , GONG W B . An on-orbit task-offloading strategy based on satellite edge computing [J ] . Sensors , 2023 , 23 ( 9 ): 4271 .

ABABNEH J . A hybrid approach based on grey wolf and whale optimization algorithms for solving cloud task scheduling problem [J ] . Mathematical Problems in Engineering , 2021 , 2021 : 3517145 .

LIU H S , CHEN Q Y , PAN N , et al . Three-dimensional mountain complex terrain and heterogeneous multi-UAV cooperative combat mission planning [J ] . IEEE Access , 2020 , 8 : 197407 - 197419 .

ZHANG J W , XING L N , PENG G S , et al . A large-scale multiobjective satellite data transmission scheduling algorithm based on SVM+NSGA-Ⅱ [J ] . Swarm and Evolutionary Computation , 2019 , 50 : 100560 .

GAO X Z , ZHANG H B , YU T H , et al . Autonomous mission planning for multi-agile earth observation satellites using whale optimization algorithm [C ] // Proceedings of the 2020 Chinese Automation Congress (CAC) . Piscataway : IEEE Press , 2020 : 4102 - 4107 .

MAO H Z , SCHWARZKOPF M , VENKATAKRISHNAN S B , et al . Learning scheduling algorithms for data processing clusters [C ] // Proceedings of the ACM Special Interest Group on Data Communication . New York : ACM , 2019 : 270 - 288 .

HUANG Y X , MU Z C , WU S F , et al . Revising the observation satellite scheduling problem based on deep reinforcement learning [J ] . Remote Sensing , 2021 , 13 ( 12 ): 2377 .

WEN Z J , LIU Y , ZHANG S Y , et al . Scheduling observation tasks for large-scale satellite constellation [J ] . Journal of Physics: Conference Series , 2024 , 2746 ( 1 ): 012040 .

LI G H , LI X F , LI J , et al . PTMB: an online satellite task scheduling framework based on pre-trained Markov decision process for multi-task scenario [J ] . Knowledge-Based Systems , 2024 , 284 : 111339 .

QIAO Z M , YANG W B , LI F , et al . Satellite communication resource scheduling using a dynamic weight-based soft actor critic reinforcement learning [J ] . IEEE Access , 2024 , 12 : 111653 - 111662 .

TANG Q Q , XIE R C , FANG Z R , et al . Joint service deployment and task scheduling for satellite edge computing: a two-timescale hierarchical approach [J ] . IEEE Journal on Selected Areas in Communications , 2024 , 42 ( 5 ): 1063 - 1079 .

PENG G S , SONG G P , XING L N , et al . An exact algorithm for agile earth observation satellite scheduling with time-dependent profits [J ] . Computers & Operations Research , 2020 , 120 : 104946 .

NEMATPOUR M , IZADKHAH H , MAHAN F . Enhanced genetic algorithm with some heuristic principles for task graph scheduling [J ] . The Journal of Supercomputing , 2023 , 79 ( 2 ): 1784 - 1813 .

SREENU K , SREELATHA M . W-Scheduler: whale optimization for task scheduling in cloud computing [J ] . Cluster Computing , 2019 , 22 ( 1 ): 1087 - 1098 .

MIRJALILI S , LEWIS A . The whale optimization algorithm [J ] . Advances in Engineering Software , 2016 , 95 : 51 - 67 .

XU L Z , YU C H , WU B , et al . A hybrid genetic algorithm for ground station scheduling problems [J ] . Applied Sciences , 2024 , 14 ( 12 ): 5045 .

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