Multi-vessel Computation Offloading in Maritime Mobile Edge Computing Network

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


With the rapid development of maritime communication networks, applications data of ship users has been growing exponentially, more and more resource-intensive tasks (e.g. multimedia applications, high-definition video playback, and online games) appear in daily life. New applications have enormously increased the energy consumption of ship terminals and network bandwidth requirements. To satisfy the requirements of high bandwidth and low latency for great-leap forward development and lighten the workload of mobile networks, the concept of MEC has been proposed and received extensive support from academia and industry, which is considered as one of the key technologies of next generation networks. Illuminated by this idea, this chapter presents computation offloading technology into maritime communication networks. In this chapter, we study the issue of computation offloading for computation-intensive tasks, which focuses on minimizing energy consumption of vessel terminals and time delay of computation-intensive tasks. At first, it determines whether a computation-intensive task should be offloading to a cloud server. Then, it should determine which server executes the computation-intensive task.


  1. 1.
    Taleb, T., Dutta, S., Ksentini, A., Iqbal, M., Flinck, H.: Mobile edge computing potential in making cities smarter. IEEE Commun. Mag. 55(3), 38–43 (2017). MarchCrossRefGoogle Scholar
  2. 2.
    Mebrek, A., Merghem-Boulahia, L., Esseghir, M.: Efficient green solution for a balanced energy consumption and delay in the IoT-Fog-Cloud computing. In: IEEE 16th International Symposium on Network Computing and Applications (NCA). Cambridge, MA, USA, pp. 1–4 (2017)Google Scholar
  3. 3.
    Maddah-Ali, M.A., Tse, D.: Completely stale transmitter channel state information is still very useful. In: 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Allerton, IL, pp. 1188–1195 (2010)Google Scholar
  4. 4.
    Aslam, S., Shahid, A., Lee, K.G.: IMS: Interference minimization scheme for cognitive radio networks using Hungarian algorithm. In: The First International Conference on Future Generation Communication Technologies, London, pp. 17–21 (2012)Google Scholar
  5. 5.
    Dinkelbach, W.: On nonlinear fractional programming. Manag. Sci. 13(7), 492–498 (1967)MathSciNetCrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Electrical Engineering and IntelligentizationDongguan University of TechnologyDongguanChina
  2. 2.Department of Electrical and Computer EngineeringUniversity of WaterlooWaterlooCanada

Personalised recommendations