Deakin University
Browse

File(s) under permanent embargo

Comparative study of routing protocols for opportunistic networks

conference contribution
posted on 2013-01-01, 00:00 authored by Majeed Alajeely, Asma'a Ahmad, Robin Ram Mohan DossRobin Ram Mohan Doss
Opportunistic networks or OppNets refer to a number of wireless nodes opportunistically communicating with each other in a form of “Store-Carry-Forward”. This occurs when they come into contact with each other without proper network infrastructure. In OppNets there is no end-to-end connection between the source node and the destination node. OppNets grow from a single node (seed) to become large networks by inviting new nodes (helpers) to join the network. Due to these characteristics, OppNets are subject to real routing challenges. In this paper, we have presented an overview of the main available three families of OppNet routing protocols. Further, we have evaluated one protocol from each family (Epidemic, Direct Delivery and PRoPHET) in terms of complexity and scalability. Simulation results show that for small and medium complexity, the three protocols perform better than large complexity. As for scalability, simulation results show that Epidemic and PRoPHET perform better than Direct Delivery in terms of delivery rates and delays, but at a very high cost while Direct Delivery achieved lower delivery rates with a low cost.

History

Event

Sensing Technology. Conference (7th : 2013 : Wellington, New Zealand)

Pagination

209 - 214

Publisher

IEEE

Location

Wellington, New Zealand

Place of publication

Piscataway, N.J.

Start date

2013-12-03

End date

2013-12-05

Language

eng

Publication classification

E1 Full written paper - refereed; E Conference publication

Copyright notice

2013, IEEE

Title of proceedings

ICST 2013 : Proceedings of the 7th International Conference on Sensing Technology

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC