Skip to main content

An Emergency Resource Scheduling Model Based on Edge Computing

  • Conference paper
  • First Online:
Artificial Intelligence for Communications and Networks (AICON 2019)

Abstract

In recent years, more and more researches are focusing on the field of disaster emergency management, especially in emergency resource dispatching. In a disaster scenario, a control center needs to obtain a large amount of real-time information at the scene to make decisions timely and accurately. Traditional Cloud Computing has many shortcomings in terms of delay and bandwidth, and Edge Computing (EC) will be more suitable for this scenario. We apply EC to the emergency rescue to cope with the problem of latency needs. First, we propose an edge-based emergency rescue architecture that consists of three layers: Cloud Layer, Edge Layer, and Resource Layer. Based on this, we give a resource scheduling model that requires the collaboration of Cloud and Edge Layers. The Cloud Layer gives a partition for these tasks, and all sub-tasks are assigning to the edge servers to get a locally optimal solution. Finally, these solutions from different edge servers are summarized to the Cloud Layer to get a globally optimal solution. We compare our algorithm with CS-GA and VRP. The simulation results show that RSE has good performance in scheduling time and cost.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhang, T.: Research on emergency resource scheduling in disaster emergency management. Hefei University of Technology (2015)

    Google Scholar 

  2. Chai, X.R.: Research on disaster emergency rescue material dispatching system. University of Science and Technology of China (2009)

    Google Scholar 

  3. Cai, J.M.: Research on time-varying and reliability related issues of earthquake disaster emergency logistics. Central South University (2012)

    Google Scholar 

  4. Zhao, T.: Research on optimization of emergency disaster material distribution system for sudden natural disasters in China. Dalian Maritime University (2011)

    Google Scholar 

  5. Xue, H., Cai, B., Jiang, C., Yuan, Y.: Research on the robust decision of emergency material distribution for chain retail supply chain. In: Chinese Control and Decision Conference (CCDC), Shenyang, pp. 1922–1927 (2018)

    Google Scholar 

  6. Gu, Y.: Study on scheduling model of emergency materials distribution after natural disaster. In: 2nd International Workshop on Intelligent Systems and Applications, Wuhan, pp. 1–4 (2010)

    Google Scholar 

  7. Sun, Q., Kong, F., Zhang, L., Dang, X.: Study on emergency distribution route decision making. In: International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), Jilin, pp. 338–341 (2011)

    Google Scholar 

  8. Satria, D., Park, D., Jo, M.: Recovery for overloaded mobile edge computing. Future Gener. Comput. Syst. 70, 138–147 (2017)

    Article  Google Scholar 

  9. Zhao, T., Zhou, S., Guo, X.: A cooperative scheduling scheme of l cloud and Internet cloud for delay-aware mobile cloud computing. In: Proceedings of IEEE GLOBECOM Workshops, pp. 1–6 (2015)

    Google Scholar 

  10. Flores, H., Hui, P., Tarkoma, S., Li, Y., Srirama, S., Buyya, R.: Mobile code offloading: from concept to practice and beyond. IEEE Commun. Mag. 53, 80–88 (2015)

    Article  Google Scholar 

  11. Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24, 2795–2808 (2016)

    Article  Google Scholar 

  12. You, C., Huang, K., Chae, H., Kim, B.H.: Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans. Wirel. Commun. 16, 1397–1411 (2017)

    Article  Google Scholar 

  13. Sardellitti, S., Scutari, G., Barbarossa, S.: Joint optimization of radio and computational resources for multicell mobile-edge computing. IEEE Trans. Signal Inf. Process. Over Netw. 1, 89–103 (2015)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  15. Lin, S., Cheng, H.F., Li, W., Huang, Z., Hui, P., Peylo, C.: Ubii: physical world interaction through augmented reality. IEEE Trans. Mob. Comput. 16, 872–885 (2017)

    Article  Google Scholar 

  16. Hu, Y., Patel, M., Sabella, D., et al.: Mobile edge computing: a key technology towards 5G. ETSI White Paper (2015)

    Google Scholar 

  17. Zhang, K., et al.: Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access J. 4, 5896–5907 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mengyu Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhong, S., He, T., Li, M., Rui, L., Xia, G., Zhu, Y. (2019). An Emergency Resource Scheduling Model Based on Edge Computing. In: Han, S., Ye, L., Meng, W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-22971-9_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-22971-9_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22970-2

  • Online ISBN: 978-3-030-22971-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics