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IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Online ISSN : 1745-1337
Print ISSN : 0916-8508
Regular Section
Optimal Probabilistic Epidemic Forwarding for Energy Constrained Delay Tolerant Networks
Li SU, Yong LI, Depeng JIN, Lieguang ZENG
Author information
  • Li SU

    State Key Laboratory on Microwave and Digital Communications, Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University

  • Yong LI

    State Key Laboratory on Microwave and Digital Communications, Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University

  • Depeng JIN

    State Key Laboratory on Microwave and Digital Communications, Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University

  • Lieguang ZENG

    State Key Laboratory on Microwave and Digital Communications, Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University

Corresponding author

ORCID
Keywords:delay tolerant networks,energy constraint,probabilistic epidemic forwarding
JOURNALRESTRICTED ACCESS

2010 Volume E93.AIssue 8Pages 1573-1577

DOIhttps://doi.org/10.1587/transfun.E93.A.1573
Details
  • Published: August 01, 2010Received: February 10, 2010Available on J-STAGE: August 01, 2010Accepted: -Advance online publication: -Revised: -
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Abstract
In delay tolerant networks, energy efficient forwarding algorithms are significant to enhance the performance of message transmission probability. In this paper, we focus on the problem of optimal probabilistic epidemic forwarding with energy constraint. By introducing a continuous time model, we obtain the optimal static and dynamic policies for multi-messages forwarding. Extensive numerical results show that the optimal dynamic policy achieves higher transmission probability than the optimal static policy while the number of messages decreases the average transmission probability.
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© 2010 The Institute of Electronics, Information and Communication Engineers
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