Skip to main content
SpringerLink
Log in

A framework involving MEC: imaging satellites mission planning

  • Multi-access Edge Computing Enabled Internet of Things
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Satellite will play an important role in many important industries and exist as a carrier of information transmission in the era of Internet of Things. Massive data can be used in planning and scheduling processes A general data-driven framework-imaging satellite mission planning framework (ISMPF) for solving imaging mission planning problems is proposed. ISMPF mainly includes three parts: task assignment, planning and scheduling and task execution. The framework gives a general solution to the problem of satellite mission planning. The two core parts of the planning and scheduling module are machine learning algorithms and planning and scheduling algorithms, which greatly affect the quality of the results. Machine learning algorithm is mainly used to quickly obtain feasible initial solution. This idea can be used to quickly analyze and model the imaging satellite observation mission planning, imaging satellite measurement and control, data downlink mission planning problems. It has a strong generality and is suitable for most situations of imaging satellites. In order to verify the validity of ISMPF, we designed test examples for measurement and control, data downlink missions. Experimental verification demonstrates the effectiveness of our proposed framework.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Zhang ZQ, Guo JE, Wang P (2012) An AHP-based approach for evaluating mission planning efficiency of imaging satellites. Radio Eng 1:013

    Google Scholar 

  2. Gabrel V (2006) Strengthened 0-1 linear formulation for the daily satellite mission planning. J Comb Optim 11(3):341–346

    Article  MathSciNet  Google Scholar 

  3. Cui K, Xiang J, Zhang Y (2017) Mission planning optimization of video satellite for ground multi-object staring imaging. Adv Space Res 61(6):1476–1489

    Article  Google Scholar 

  4. Zheng Z, Guo J, Gill E (2018) Onboard autonomous mission re-planning for multi-satellite system. Acta Astronaut 145:28–43

    Article  Google Scholar 

  5. Tipaldi M, Glielmo L (2017) A survey on model-based mission planning and execution for autonomous spacecraft. IEEE Syst J PP(99):1–13

    Google Scholar 

  6. Chen XQ, Yu J (2017) Optimal mission planning of geo on-orbit refueling in mixed strategy. Acta Astronaut 133:63–72

    Article  Google Scholar 

  7. Zheng Z, Guo J, Gill E (2017) Swarm satellite mission scheduling & planning using hybrid dynamic mutation genetic algorithm. Acta Astronaut 137:243–253

    Article  Google Scholar 

  8. Song YJ, Ma X, Zhang ZS, Xing LN, Chen YW (2018) A hybrid dynamic population genetic algorithm for multi-satellite and multi-station mission planning system. In: Qiao J et al (eds) Bio-inspired computing: theories and applications. BIC-TA 2018. Communications in computer and information science, vol 951. Springer, Singapore

  9. He Y, He L, Wang Y, Xiao Y, Chen Y, Xing L (2016) Autonomous mission replanning method for imaging satellites considering real-time weather conditions. J Comput Theor Nanosci 13(10):6967–6973

    Article  Google Scholar 

  10. Wang H, Li X, Liu Y, Zhou B (2010) Summary of intelligent algorithms in planning & scheduling of Earth observation satellite. In: IEEE International conference on intelligent computing and intelligent systems, vol 3. IEEE, pp 480–483

  11. Wei J (2013) The mission planning model and improved Ant Colony solving algorithm for networking SAR satellites. In: International conference on management science and engineering, vol 51. IEEE, pp 14–19

  12. Ran CX, Wang HL, Xiong GY, Qiu DS (2010) Research on mission-planning of ocean moving targets imaging reconnaissance based on improved genetic algorithm. J Astronaut 2:025

    Google Scholar 

  13. Kolici V, Herrero X, Xhafa F, Barolli L (2013) Local search and genetic algorithms for satellite scheduling problems. In: Eighth international conference on broadband and wireless computing, communication and applications, vol 28. IEEE, pp 328–335

  14. Pemberton JC, Galiber FI (2000). A constraint-based approach to satellite scheduling. In: DIMACS workshop on constraint programming and large scale discrete optimization. American Mathematical Society, pp 101–114

  15. Xiong J, Leus R, Yang Z, Abbass HA (2016) Evolutionary multi-objective resource allocation and scheduling in the chinese navigation satellite system project. Eur J Oper Res 251(2):662–675

    Article  MathSciNet  Google Scholar 

  16. Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor 19(4):2322–2358

    Article  Google Scholar 

  17. Mao Y, Zhang J, Letaief KB (2017) Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. In: Wireless communications & networking conference. IEEE

  18. Tianze L, Muqing W, Min Z, Wenxing L (2017) An overhead-optimizing task scheduling strategy for ad-hoc based mobile edge computing. IEEE Access 5:5609–5622

    Article  Google Scholar 

  19. Wang F, Xu J, Wang X, Cui S (2017) Joint offloading and computing optimization in wireless powered mobile-edge computing systems. IEEE Trans Wirel Commun PP(99):1

    Google Scholar 

  20. Yang X, Chen Z, Li K, Sun Y, Liu N, Xie W et al (2018) Communication-constrained mobile edge computing systems for wireless virtual reality: scheduling and tradeoff. IEEE Access 6:16665–16677

    Article  Google Scholar 

  21. Taleb T, Dutta S, Ksentini A, Iqbal M, Flinck H (2017) Mobile edge computing potential in making cities smarter. IEEE Commun Mag 55(3):38–43

    Article  Google Scholar 

  22. Jeong S, Simeone O, Kang J (2016) Mobile edge computing via a UAV-mounted cloudlet: optimization of bit allocation and path planning. IEEE Trans Veh Technol 67(3):2049–2063

    Article  Google Scholar 

  23. Zhang K, Mao Y, Leng S, Zhao Q, Li L, Peng X et al (2017) Energy-efficient offloading for mobile edge computing in 5g heterogeneous networks. IEEE Access 4(99):5896–5907

    Google Scholar 

  24. García-Pintado J, Neal JC, Mason DC, Dance SL, Bates PD (2013) Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling. J Hydrol 495(2):252–266

    Article  Google Scholar 

  25. Feng P, Chen H, Peng S, Chen L (2015) A method of distributed multi-satellite mission scheduling based on improved contract net protocol. In: International conference on natural computation. IEEE, pp 1062–1068

  26. Song Y, Huang D, Zhou Z, Chen Y (2018) An emergency task autonomous planning method of agile imaging satellite. EURASIP J Image Video Process 2018(1):29

    Article  Google Scholar 

  27. Song Y, Song B, Zhang Z, Chen Y (2018) The satellite downlink replanning problem: a BP neural network and hybrid algorithm approach for IoT Internet connection. IEEE Access 6:39797–39806. https://doi.org/10.1109/access.2018.2855800

    Article  Google Scholar 

  28. Sun B, Wang W, Xie X, Qin Q (2010) Satellite mission scheduling based on genetic algorithm. Kybernetes 39(8):1255–1261

    Article  Google Scholar 

  29. Brandimarte P (2013) Scheduling satellite launch missions: an milp approach. J Sched 16(1):29–45

    Article  MathSciNet  Google Scholar 

  30. Sun J, Sun J, Barolli A, Biberaj A, Barolli L (2012) Genetic algorithms for satellite scheduling problems. Mob Inf Syst 8(4):351–377

    Google Scholar 

  31. Tangpattanakul P, Jozefowiez N, Lopez P (2015) A multi-objective local search heuristic for scheduling earth observations taken by an agile satellite. Eur J Oper Res 245(2):542–554

    Article  MathSciNet  Google Scholar 

  32. Wang XW, Chen Z, Han C (2016) Scheduling for single agile satellite, redundant targets problem using complex networks theory. Chaos Solitons Fractals 83:125–132

    Article  MathSciNet  Google Scholar 

  33. Jung-Hyun L, Wang SM, Chung D, Hee KK (2012) Multi-satellite control system architecture and mission scheduling optimization. In: Aerospace conference, vol 186. IEEE, pp 1–13

  34. Marinelli F, Nocella S, Rossi F, Smriglio S (2011) A lagrangian heuristic for satellite range scheduling with resource constraints. Comput Oper Res 38(11):1572–1583

    Article  MathSciNet  Google Scholar 

  35. Baek SW, Han SM, Cho KR, Lee DW, Yang JS, Bainum PM et al (2011) Development of a scheduling algorithm and gui for autonomous satellite missions. Acta Astronaut 68(7):1396–1402

    Article  Google Scholar 

  36. Zufferey N, Amstutz P, Giaccari P (2008) Graph colouring approaches for a satellite range scheduling problem. J Sched 11(4):263–277

    Article  MathSciNet  Google Scholar 

  37. Song B, Yao F, Chen Y, Chen Y, Chen Y (2018) A hybrid genetic algorithm for satellite image downlink scheduling problem. Discrete Dyn Nat Soc. https://doi.org/10.1155/2018/1531452

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant numbers: 61473301, 71701203). Special thanks to Chen Wei Yan of Beijing University of Posts and Telecommunications for his guidance on the mobile edge computing part. Thanks to the reviewers for their valuable comments. At the same time, I would like to thank the teachers and students for their help in the paper.

Author information

Authors and Affiliations

  1. College of Systems Engineering, National University of Defense Technology, Changsha, 410073, Hunan, China

    Yan-jie Song, Zhong-shan Zhang, Feng Yao & Ying-wu Chen

  2. Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, 117 Transportation Buld., 104 S. Mathews Ave., MC-238, Urbana, IL, 61801-3080, USA

    Zi-yu Zhou

Authors
  1. Yan-jie Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zi-yu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhong-shan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feng Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ying-wu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Yao.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, Yj., Zhou, Zy., Zhang, Zs. et al. A framework involving MEC: imaging satellites mission planning. Neural Comput & Applic 32, 15329–15340 (2020). https://doi.org/10.1007/s00521-019-04047-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04047-6

Keywords

Search

Navigation