Advertisement

A Review of Mobility Prediction Models Applied in Cloud/Fog Environments

  • David H. S. LimaEmail author
  • Andre L. L. Aquino
  • Marilia Curado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11339)

Abstract

Cloud and Fog Computing are two emerging technologies that have being used in various fields of application. On one hand, Cloud Computing has the problem of big latency, being especially problematic when the application requires a rapid response in the edge network. On the other hand, Fog Computing distributes the computational data processing tasks to the edge network to reduce the latency, but it still faces challenges especially when dealing with support for mobile users. This work aims to present a review of the works in Cloud/Fog Computing that use mobility prediction techniques in their favor in order to deal with users mobility problem. Additionally we present the potential of applying the techniques in Cloud/Fog environments.

Keywords

Cloud Computing Fog computing Mobility prediction 

Notes

Acknowledgements

The work presented in this paper was partially carried out in the scope of the MobiWise project: From mobile sensing to mobility advising (P2020 SAICTPAC/0011/2015), cofinanced by COMPETE 2020, Portugal 2020 – Operational Program for Competitiveness and Internationalization (POCI), European Union’s ERDF (European Regional Development Fund) and the Portuguese Foundation for Science and Technology (FCT).

References

  1. 1.
    Abbasi, O.R., Alesheikh, A.A., Sharif, M.: Ranking the city: the role of location-based social media check-ins in collective human mobility prediction. ISPRS Int. J. Geo-Inf. 6(5), 136 (2017)CrossRefGoogle Scholar
  2. 2.
    Agarwal, R., Gauthier, V., Becker, M., Toukabrigunes, T., Afifi, H.: Large scale model for information dissemination with device to device communication using call details records. Comput. Commun. 59, 1–11 (2015)CrossRefGoogle Scholar
  3. 3.
    Ahmed, E., Akhunzada, A., Whaiduzzaman, M., Gani, A., Ab Hamid, S.H., Buyya, R.: Network-centric performance analysis of runtime application migration in mobile cloud computing. Simul. Model. Pract. Theory 50, 42–56 (2015)CrossRefGoogle Scholar
  4. 4.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)CrossRefGoogle Scholar
  5. 5.
    Arshad, R., Elsawy, H., Sorour, S., Al-Naffouri, T.Y., Alouini, M.S.: Handover management in 5G and beyond: a topology aware skipping approach. IEEE Access 4, 9073–9081 (2016)CrossRefGoogle Scholar
  6. 6.
    Bao, W., Yuan, D., Yang, Z., Wang, S., Li, W., Zhou, B.B., Zomaya, A.Y.: Follow me fog: toward seamless handover timing schemes in a fog computing environment. IEEE Commun. Mag. 55(11), 72–78 (2017)CrossRefGoogle Scholar
  7. 7.
    Beiro, M.G., Panisson, A., Tizzoni, M., Cattuto, C.: Predicting human mobility through the assimilation of social media traces into mobility models. EPJ Data Sci. 5, 30 (2016)CrossRefGoogle Scholar
  8. 8.
    Bhattacharya, A., De, P.: A survey of adaptation techniques in computation offloading. J. Netw. Comput. Appl. 78, 97–115 (2017)CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Chen, Y.S., Tsai, Y.T.: A mobility management using follow-me cloud-cloudlet in fog-computing-based RANs for smart cities. Sensors 18(2), 489 (2018)CrossRefGoogle Scholar
  11. 11.
    Comito, C., Falcone, D., Talia, D.: Mining human mobility patterns from social geo-tagged data. Pervasive Mob. Comput. 33, 91–107 (2016)CrossRefGoogle Scholar
  12. 12.
    Drissi, M., Oumsis, M.: Performance Evaluation of Multi-criteria Vertical Handover for Heterogeneous Wireless Networks. In: Intelligent Systems and Computer Vision, pp. 1–5 (2015)Google Scholar
  13. 13.
    Farris, I., Taleb, T., Bagaa, M., Flick, H.: Optimizing service replication for mobile delay-sensitive applications in 5G edge network. In: IEEE International Conference on Communications, pp. 1–6 (2017)Google Scholar
  14. 14.
    Flores, H., et al.: Social-aware device-to-device communication: a contribution for edge and fog computing? In: ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pp. 1466–1471. ACM (2016)Google Scholar
  15. 15.
    Gallotti, R., Bazzani, A., Rambaldi, S., Barthelemy, M.: A Stochastic Model of Randomly Accelerated Walkers for Human Mobility. Nat. Commun. 7, 1–7 (2016)Google Scholar
  16. 16.
    Gani, A., Nayeem, G.M., Shiraz, M., Sookhak, M., Whaiduzzaman, M., Khan, S.: A review on interworking and mobility techniques for seamless connectivity in mobile cloud computing. J. Netw. Comput. Appl. 43, 84–102 (2014)CrossRefGoogle Scholar
  17. 17.
    Gao, T., Chen, M., Gu, H., Yin, C.: Reinforcement learning based resource allocation in cache-enabled small cell networks with mobile users. In: IEEE/CIC International Conference on Communications in China, pp. 1–6 (2017)Google Scholar
  18. 18.
    Hess, A., Hummel, K.A., Gansterer, W.N., Haring, G.: Data-driven human mobility modeling: a survey and engineering guidance for mobile networking. ACM Comput. Surv. 48(3), 38:1–38:39 (2015)CrossRefGoogle Scholar
  19. 19.
    Jahromi, K.K., Zignani, M., Gaito, S., Rossi, G.P.: Simulating human mobility patterns in urban areas. Simul. Model. Pract. Theory 62, 137–156 (2016)CrossRefGoogle Scholar
  20. 20.
    Karamshuk, D., Boldrini, C., Conti, M., Passarella, A.: Human mobility models for opportunistic networks. IEEE Commun. Mag. 49(12), 157–165 (2011)CrossRefGoogle Scholar
  21. 21.
    Karimzadeh, M., et al.: Mobility and bandwidth prediction as a service in virtualized LTE systems. In: IEEE International Conference on Cloud Networking, pp. 132–138 (2015)Google Scholar
  22. 22.
    Lee, K., Shin, I.: User mobility model based computation offloading decision for mobile cloud. J. Comput. Sci. Eng. 9(3), 155–162 (2015)CrossRefGoogle Scholar
  23. 23.
    Li, B., Liu, Z., Pei, Y., Wu, H.: mobility prediction based opportunistic computational offloading for mobile device cloud. In: IEEE International Conference on Computational Science and Engineering, pp. 786–792 (2014)Google Scholar
  24. 24.
    Li, W., Zhao, Y., Lu, S., Chen, D.: Mechanisms and challenges on mobility-augmented service provisioning for mobile cloud computing. IEEE Commun. Mag. 53(3), 89–97 (2015)CrossRefGoogle Scholar
  25. 25.
    Lind, P.G., Moreira, A.: Human mobility patterns at the smallest scales. Commun. Comput. Phys. 18(2), 417–428 (2015)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Mazimpaka, J.D., Timpf, S.: How they move reveals what is happening: understanding the dynamics of big events from human mobility pattern. ISPRS Int. J. Geo-Inf. 6(1), 15 (2017)CrossRefGoogle Scholar
  27. 27.
    Mustafa, A.M., Abubakr, O.M., Ahmadien, O., Ahmedin, A., Mokhtar, B.: Mobility prediction for efficient resources management in vehicular cloud computing. In: IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp. 53–59 (2017)Google Scholar
  28. 28.
    Ojima, T., Fujii, T.: Resource management for mobile edge computing using user mobility prediction. In: International Conference on Information Networking, pp. 718–720 (2018)Google Scholar
  29. 29.
    Pirozmand, P., Wu, G., Jedari, B., Xia, F.: Human mobility in opportunistic networks: characteristics, models and prediction methods. J. Netw. Comput. Appl. 42(SI), 45–58 (2014)CrossRefGoogle Scholar
  30. 30.
    Plachy, J., Becvar, Z., Strinati, E.C.: Dynamic resource allocation exploiting mobility prediction in mobile edge computing. In: IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications, pp. 1–6 (2016)Google Scholar
  31. 31.
    Rao, W., Zhao, K., Zhang, Y., Hui, P., Tarkoma, S.: Towards maximizing timely content delivery in delay tolerant networks. IEEE Trans. Mob. Comput. 14(4), 755–769 (2015)CrossRefGoogle Scholar
  32. 32.
    Shi, L., Fu, X., Li, J.: Mobility prediction-based service scheduling optimization algorithm in cloudlets. In: Sun, X., Chao, H.-C., You, X., Bertino, E. (eds.) ICCCS 2017. LNCS, vol. 10603, pp. 619–630. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68542-7_53CrossRefGoogle Scholar
  33. 33.
    Shiraz, M., Sookhak, M., Gani, A., Shah, S.A.A.: A study on the critical analysis of computational offloading frameworks for mobile cloud computing. J. Netw. Comput. Appl. 47, 47–60 (2015)CrossRefGoogle Scholar
  34. 34.
    Simini, F., González, M.C., Maritan, A., Barabási, A.L.: A universal model for mobility and migration patterns. Nature 484(7392), 96–100 (2012)CrossRefGoogle Scholar
  35. 35.
    Terroso-Saenz, F., Valdes-Vela, M., Gonzalez-Vidal, A., Skarmeta, A.F.: Human mobility modelling based on dense transit areas detection with opportunistic sensing. Mob. Inf. Syst. 2016, 1–15 (2016)CrossRefGoogle Scholar
  36. 36.
    Yang, X., Zhao, Z., Lu, S.: Exploring spatial-temporal patterns of urban human mobility hotspots. Sustainability 8(7), 1–18 (2016)CrossRefGoogle Scholar
  37. 37.
    Zhang, F., Zhu, X., Guo, W., Ye, X., Hu, T., Huang, L.: Analyzing urban human mobility patterns through a thematic model at a finer scale. ISPRS Int. J. Geo-Inf. 5(6), 78–95 (2016)CrossRefGoogle Scholar
  38. 38.
    Zhang, H., Qiu, Y., Chu, X., Long, K., Leung, V.C.M.: Fog radio access networks: mobility management, interference mitigation, and resource optimization. IEEE Wirel. Commun. 24(6), 120–127 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • David H. S. Lima
    • 1
    • 3
    Email author
  • Andre L. L. Aquino
    • 2
  • Marilia Curado
    • 1
  1. 1.Centre for Informatics and Systems (CISUC)DEI/FCTUC - University of CoimbraCoimbraPortugal
  2. 2.LaCCAN/CPMAT – Computer InstituteFederal University of Alagoas (UFAL)MaceióBrazil
  3. 3.Federal Institute of Alagoas (IFAL)Rio LargoBrazil

Personalised recommendations