Granting silence to avoid wireless collisions

Jung Il Choi, Mayank Jain, Maria A. Kazandjieva, Philip Levis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

We describe grant-to-send, a novel collision avoidance algorithm for wireless mesh networks. Rather than announce packets it intends to send, a node using grant-to-send announces packets it expects to hear others send. We present evidence that inverting collision avoidance in this way greatly improves wireless mesh performance. Evaluating four protocols from 802.11 meshes and 802.15.4 sensor networks, we find that grant-to-send matches or outperforms CSMA and RTS/CTS in all cases. For example, in a 4-hop UDP flow, grantto- send can achieve 96% of the theoretical maximum throughput while maintaining a 99.9% packet delivery ratio. Grant-tosend is also general enough to replace protocol-specific collision avoidance mechanisms common to sensor network protocols. Grant-to-send is simple. For example, incorporating it into 802.11 requires only 11 lines of driver code and no hardware changes. Furthermore, as it reuses existing 802.11 mechanisms, grant-to-send inter-operates with current networks and can be incrementally deployed. © 2010 IEEE.
Original languageEnglish (US)
Title of host publicationThe 18th IEEE International Conference on Network Protocols
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages82-91
Number of pages10
ISBN (Print)9781424486441
DOIs
StatePublished - Oct 2010
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank Prof. Fouad Tobagi for his invaluable guidanceon this project. We would also like to thank our shepherd,Prof. Maria Papadopouli, and the anonymous reviewers fortheir comments. This work was supported by generous giftsfrom DoCoMo Capital, the National Science Foundation underaward DE-AR -0000018 and grants #0831163 and #0846014,the King Abdullah University of Science and Technology(KAUST), Microsoft Research, scholarships from the SamsungScholarship and a Stanford Terman Fellowship.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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