Information-Centric Fog Computing for Disaster Relief

  • Jianwen Xu
  • Kaoru Ota
  • Mianxiong DongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)


Natural disasters like earthquakes and typhoons are bringing huge casualties and losses to modern society every year. As the main foundation of the information age, host-centric network infrastructure is easily disrupted during disasters. In this paper, we focus on combining Information-Centric Networking (ICN) and fog computing in solving the problem of emergency networking and fast communication. We come up with the idea from six degrees of separation theory (SDST) in achieving Information-Centric Fog Computing (ICFC) for disaster relief. Our target is to model the relationship of network nodes and design a novel name-based routing strategy using SDST. In the simulation part, we evaluate and compare our work with existing routing methods in ICN. The results show that our strategy can help improve work efficiency in name-based routing under the limitation of post-disaster scenario.


Fog computing Information-centric networking Disaster management Name-based routing Six degrees of separation 


  1. 1.
    Wikipedia: 2018 hokkaido eastern iburi earthquake, September 2018.
  2. 2.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC 2012, pp. 13–16. ACM, New York (2012).
  3. 3.
    Xu, J., Ota, K., Dong, M.: Fast networking for disaster recovery. IEEE Trans. Emerg. Topics Comput. 1 (2018).
  4. 4.
    Hoque, A.K.M.M., Amin, S.O., Alyyan, A., Zhang, B., Zhang, L., Wang, L.: NLSR: named-data link state routing protocol. In: Proceedings of the 3rd ACM SIGCOMM Workshop on Information-Centric Networking, ICN 2013, pp. 15–20. ACM, New York (2013).
  5. 5.
    Garcia-Luna-Aceves, J.: Name-based content routing in information centric networks using distance information. In: Proceedings of the 1st ACM Conference on Information-Centric Networking, ACM-ICN 2014, pp. 7–16. ACM, New York (2014).
  6. 6.
    Hemmati, E., Garcia-Luna-Aceves, J.J.: Making name-based content routing more efficient than link-state routing. CoRR abs/1804.02752 (2018).
  7. 7.
    Li, H., Ota, K., Dong, M.: ECCN: orchestration of edge-centric computing and content-centric networking in the 5G radio access network. IEEE Wirel. Commun. 25(3), 88–93 (2018). Scholar
  8. 8.
    Li, H., Ota, K., Dong, M.: Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Netw. 32(1), 96–101 (2018). Scholar
  9. 9.
    Wu, J., Dong, M., Ota, K., Li, J., Guan, Z.: FCSS: fog computing based content-aware filtering for security services in information centric social networks. IEEE Trans. Emerg. Topics Comput. 1.
  10. 10.
    Li, L., Ota, K., Dong, M.: Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Trans. Indus. Inform. 1 (2018).
  11. 11.
    Gai, K., Choo, K.R., Qiu, M., Zhu, L.: Privacy-preserving content-oriented wireless communication in internet-of-things. IEEE Internet Things J. 5(4), 3059–3067 (2018). Scholar
  12. 12.
    Gai, K., Qiu, M.: Blend arithmetic operations on tensor-based fully homomorphic encryption over real numbers. IEEE Trans. Indus. Inform. 14(8), 3590–3598 (2018). Scholar
  13. 13.
    Gai, K., Qiu, M., Xiong, Z., Liu, M.: Privacy-preserving multi-channel communication in edge-of-things. Future Gen. Comput. Syst. 85, 190–200 (2018).,
  14. 14.
    Erdelj, M., Natalizio, E., Chowdhury, K.R., Akyildiz, I.F.: Help from the sky: leveraging UAVs for disaster management. IEEE Perv. Comput. 16(1), 24–32 (2017). Scholar
  15. 15.
    Han, G., Yang, X., Liu, L., Guizani, M., Zhang, W.: A disaster management-oriented path planning for mobile anchor node-based localization in wireless sensor networks. IEEE Trans. Emerg. Topics Comput. 1 (2018).
  16. 16.
    Li, L., Ota, K., Dong, M., Borjigin, W.: Eyes in the dark: distributed scene understanding for disaster management. IEEE Trans. Parallel Distrib. Syst. 28(12), 3458–3471 (2017). Scholar
  17. 17.
    Ernst, C., Mladenow, A., Strauss, C.: Collaboration and crowdsourcing in emergency management. Int. J. Perv. Comput. Commun. 13(2), 176–193 (2017)Google Scholar
  18. 18.
    Chung, K., Park, R.C.: P2P cloud network services for IoT based disaster situations information. Peer-to-Peer Netw. Appl. 9(3), 566–577 (2016). Scholar
  19. 19.
    Muldoon, S.F., Bridgeford, E.W., Bassett, D.S.: Small-world propensity and weighted brain networks. Sci. Rep. 6, 22057 (2016). Scholar
  20. 20.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature 393, 440 (1998). Scholar
  21. 21.
    Vespignani, A.: Twenty years of network science. Nature 558, 528–529 (2018).,
  22. 22.
    Hemmati, E., Garcia-Luna-Aceves, J.J.: A comparison of name-based content routing protocols. In: 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems, pp. 537–542, October 2015.

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Information and Electronic EngineeringMuroran Institute of TechnologyMuroranJapan

Personalised recommendations