Advertisement

Mobile Interactions and Computation Offloading in Drop Computing

  • Radu-Ioan Ciobanu
  • Ciprian Dobre
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 22)

Abstract

In recent years, the amount of data consumed by mobile devices has grown exponentially, especially with the advent of the Internet of Things and all its connected devices. For this reason, researchers are looking for methods of alleviating the congestion and strain on the network, generally through various means of offloading, or by bringing the data and computations closer to the devices themselves through edge and fog computing. Thus, in this paper we propose an extension to the Drop Computing paradigm, which introduces the concept of decentralized computing over multilayered networks. We present a novel offloading technique to be employed by Drop Computing nodes for increasing processing speed, reducing deployment costs and lowering mobile device battery consumption, by using the crowd of mobile nodes belonging to humans and the edge devices as opportunities for offloading data and computations. We compare our method with the initial Drop Computing implementation and with the default scenario for mobile applications and show that it is able to improve the overall network performance. We also perform an analysis of human interactions with two monitoring nodes located in an academic environment, to obtain realistic data and to extract behavior patterns regarding human habits and interactions, that aid us in developing an efficient offloading solution.

Notes

Acknowledgements

This work is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 644399 (MONROE) through the open call project “Traffic and Data Offloading in Mobile Networks: TTOff”. The views expressed are solely those of the authors. This research is also supported by University Politehnica of Bucharest, through the “Excellence Research Grants” program, UPB - GEX 2017, identifier UPB- GEX2017, ctr. no. AU 11.17.02/2017.

References

  1. 1.
    Ciobanu, R.-I., Negru, C., Pop, F., Dobre, C., Mavromoustakis, C.X., Mastorakis, G.: Drop computing: Ad-hoc dynamic collaborative computing. Future Gener. Comput. Syst. (2017). http://www.sciencedirect.com/science/article/pii/S0167739X17305678
  2. 2.
    Rebecchi, F., de Amorim, M.D., Conan, V., Passarella, A., Bruno, R., Conti, M.: Data offloading techniques in cellular networks: a survey. IEEE Commun. Surv. Tutor. 17(2), 580–603 (2015)CrossRefGoogle Scholar
  3. 3.
    Aijaz, A., Aghvami, H., Amani, M.: A survey on mobile data offloading: technical and business perspectives. IEEE Wireless Commun. 20(2), 104–112 (2013)CrossRefGoogle Scholar
  4. 4.
    Dimatteo, S., Hui, P., Han, B., Li, V.O.: Cellular traffic offloading through wifi networks. In: 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems, pp. 192–201, October 2011Google Scholar
  5. 5.
    Pitkanen, M., Karkkainen, T., Ott, J.: Opportunistic web access via WLAN hotspots. In: 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 20–30, March 2010Google Scholar
  6. 6.
    Huerta-Canepa, G., Lee, D.: A virtual cloud computing provider for mobile devices. In: Proceedings of the 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond, MCS 2010, pp. 6:1–6:5. ACM, New York (2010).  https://doi.org/10.1145/1810931.1810937
  7. 7.
    Fernando, N., Loke, S.W., Rahayu, W.: Dynamic mobile cloud computing: ad hoc and opportunistic job sharing. In: Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing, UCC 2011, pp. 281–286. IEEE Computer Society, Washington, DC (2011).  https://doi.org/10.1109/UCC.2011.45
  8. 8.
    Miluzzo, E., Cáceres, R., Chen, Y.-F.: Vision: Mclouds - computing on clouds of mobile devices. In: Proceedings of the Third ACM Workshop on Mobile Cloud Computing and Services, MCS 2012, pp. 9–14. ACM, New York (2012). http://doi.acm.org/10.1145/2307849.2307854
  9. 9.
    Verbelen, T., Simoens, P., De Turck, F., Dhoedt, B.: Cloudlets: bringing the cloud to the mobile user. In: Proceedings of the Third ACM Workshop on Mobile Cloud Computing and Services, MCS 2012, pp. 29–36. ACM, New York (2012).  https://doi.org/10.1145/2307849.2307858
  10. 10.
    Alay, Ö., Lutu, A., García, R., Peón-Quirós, M., Mancuso, V., Hirsch, T., Dely, T., Werme, J., Evensen, K., Hansen, A., Alfredsson, S., Karlsson, J., Brunstrom, A., Khatouni, A.S., Mellia, M., Marsan, M.A., Monno, R., Lonsethagen, H.: Measuring and assessing mobile broadband networks with monroe. In: IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–3. IEEE (2016)Google Scholar
  11. 11.
    Alay, Ö., Lutu, A., Peón-Quirós, M., Mancuso, V., Hirsch, T., Evensen, K., Hansen, A., Alfredsson, S., Karlsson, J., Brunstrom, A., Safari Khatouni, A., Mellia, M., Ajmone Marsan, M.: Experience: an open platform for experimentation with commercial mobile broadband networks. In: Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, pp. 70–78. ACM (2017)Google Scholar
  12. 12.
    Ciobanu, R.I., Dobre, C.: Predicting encounters in opportunistic networks. In: Proceedings of the 1st ACM Workshop on High Performance Mobile Opportunistic Systems, HP-MOSys 2012, pp. 9–14. ACM, New York (2012).  https://doi.org/10.1145/2386980.2386983
  13. 13.
    Marin, R.-C., Ciobanu, R.-I., Dobre, C.: Improving opportunistic networks by leveraging device-to-device communication. IEEE Commun. Mag. 55(11), 86–91 (2017)CrossRefGoogle Scholar
  14. 14.
    Huang, J., Qian, F., Gerber, A., Mao, Z.M., Sen, S., Spatscheck, O.: A close examination of performance and power characteristics of 4g lte networks. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, MobiSys 2012, pp. 225–238. ACM, New York (2012).  https://doi.org/10.1145/2307636.2307658
  15. 15.
    Ciobanu, R.-I., Marin, R.-C., Dobre, C.: Mobemu: a framework to support decentralized ad-hoc networking. In: Modeling and Simulation in HPC and Cloud Systems, pp. 87–119. Springer (2018)Google Scholar
  16. 16.
    Boldrini, C., Passarella, A.: HCMM: modelling spatial and temporal properties of human mobility driven by users’ social relationships. Comput. Commun. 33(9), 1056–1074 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Automatic Control and ComputersUniversity Politehnica of BucharestBucharestRomania
  2. 2.National Institute for Research and Development in InformaticsBucharestRomania

Personalised recommendations