Energy saving on DTN using trajectory inference model

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

doi:10.1145/3167132.3167437

Energy saving on DTN using trajectory inference model

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

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018
Publisher	ACM
Pages	2132-2135
Number of pages	4
ISBN (Electronic)	9781450351911
DOIs	https://doi.org/10.1145/3167132.3167437
Publication status	Published - 9 Apr 2018
Event	33rd Annual ACM Symposium on Applied Computing, SAC 2018 - Pau, France Duration: 9 Apr 2018 → 13 Apr 2018

Conference

Conference	33rd Annual ACM Symposium on Applied Computing, SAC 2018
Country/Territory	France
City	Pau
Period	9/04/18 → 13/04/18

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1145/3167132.3167437

Cite this

@inproceedings{3f7df12cfcbd4eefae8c2ecc1ff0aabc,

title = "Energy saving on DTN using trajectory inference model",

abstract = "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.",

keywords = "Delay or disruption tolerant network, Energy saving, Opportunistic networking, Trajectory inference model",

author = "{De Vit}, {Antnio Rodrigo D.} and C{\'e}sar Marcon and Nunes, {Raul Ceretta} and Thais Webber and Gustavo Sanchez and Rolim, {Carlos Oberdan}",

year = "2018",

month = apr,

day = "9",

doi = "10.1145/3167132.3167437",

language = "English",

pages = "2132--2135",

booktitle = "Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018",

publisher = "ACM",

address = "United States",

note = "33rd Annual ACM Symposium on Applied Computing, SAC 2018 ; Conference date: 09-04-2018 Through 13-04-2018",

}

TY - GEN

T1 - Energy saving on DTN using trajectory inference model

AU - De Vit, Antnio Rodrigo D.

AU - Marcon, César

AU - Nunes, Raul Ceretta

AU - Webber, Thais

AU - Sanchez, Gustavo

AU - Rolim, Carlos Oberdan

PY - 2018/4/9

Y1 - 2018/4/9

N2 - 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.

AB - 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.

KW - Delay or disruption tolerant network

KW - Energy saving

KW - Opportunistic networking

KW - Trajectory inference model

U2 - 10.1145/3167132.3167437

DO - 10.1145/3167132.3167437

M3 - Conference contribution

AN - SCOPUS:85050547051

SP - 2132

EP - 2135

BT - Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018

PB - ACM

T2 - 33rd Annual ACM Symposium on Applied Computing, SAC 2018

Y2 - 9 April 2018 through 13 April 2018

ER -

Energy saving on DTN using trajectory inference model

Abstract

Conference

Keywords

UN SDGs

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