Energy saving on DTN using trajectory inference model

Antônio Rodrigo D. De Vit, César Marcon, Raul Ceretta Nunes, Thais Webber, Gustavo Sanchez, Carlos Oberdan Rolim

Research output: Chapter in Book/Published conference outputConference publication


Delay or Disruption Tolerant Networks (DTN) are characterized by long delays and intermittent connectivity, requiring efficient energy consumption for increasing the mobile nodes lifetime. The movements of nodes modify the network topology, changing the number of connection opportunities between nodes. This paper proposes a new technique for energy saving on DTN by using a trajectory inference model for mobile nodes powered by machine learning techniques. The objective of this work is to reduce the energy consumption of DTN using a mobility prediction method. Experimental results indicate more than 47% of energy saving on data communication applying the trajectory inference model.

Original languageEnglish
Title of host publicationProceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018
Number of pages4
ISBN (Electronic)9781450351911
Publication statusPublished - 9 Apr 2018
Event33rd Annual ACM Symposium on Applied Computing, SAC 2018 - Pau, France
Duration: 9 Apr 201813 Apr 2018

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Conference33rd Annual ACM Symposium on Applied Computing, SAC 2018

Bibliographical note

Publisher Copyright:
© 2018 Authors.


  • Delay or disruption tolerant network
  • Energy saving
  • Opportunistic networking
  • Trajectory inference model


Dive into the research topics of 'Energy saving on DTN using trajectory inference model'. Together they form a unique fingerprint.

Cite this