On simple back-off in unreliable radio networks
Name
LIPIcs-OPODIS-2018-27.pdf
Description
Published version
Size
529.42 KB
Format
Adobe PDF
Checksum (MD5)
ac818e4cd3f667169a0825acdf2b0772
Author(s)
Gilbert, S
Lynch, N
Newport, C
Pajak, D
Date Issued
2018
Journal
Leibniz International Proceedings in Informatics, LIPIcs
Citation
Gilbert, S, Lynch, N, Newport, C and Pajak, D. 2018. "On simple back-off in unreliable radio networks." Leibniz International Proceedings in Informatics, LIPIcs, 125.
Version
Final published version
Abstract
© Seth Gilbert, Nancy Lynch, Calvin Newport, and Dominik Pajak. In this paper, we study local and global broadcast in the dual graph model, which describes communication in a radio network with both reliable and unreliable links. Existing work proved that efficient solutions to these problems are impossible in the dual graph model under standard assumptions. In real networks, however, simple back-off strategies tend to perform well for solving these basic communication tasks. We address this apparent paradox by introducing a new set of constraints to the dual graph model that better generalize the slow/fast fading behavior common in real networks. We prove that in the context of these new constraints, simple back-off strategies now provide efficient solutions to local and global broadcast in the dual graph model. We also precisely characterize how this efficiency degrades as the new constraints are reduced down to non-existent, and prove new lower bounds that establish this degradation as near optimal for a large class of natural algorithms. We conclude with an analysis of a more general model where we propose an enhanced back-off algorithm. These results provide theoretical foundations for the practical observation that simple back-off algorithms tend to work well even amid the complicated link dynamics of real radio networks.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.4230/LIPIcs.OPODIS.2018.27
