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dc.contributor.authorGhaffari, Mohsen
dc.contributor.authorLynch, Nancy Ann
dc.contributor.authorNewport, Calvin Charles
dc.date.accessioned2014-09-25T20:38:03Z
dc.date.available2014-09-25T20:38:03Z
dc.date.issued2013-07
dc.identifier.isbn9781450320658
dc.identifier.urihttp://hdl.handle.net/1721.1/90369
dc.description.abstractWe study upper and lower bounds for the global and local broadcast problems in the dual graph model combined with different strength adversaries. The dual graph model is a generalization of the standard graph-based radio network model that includes unreliable links controlled by an adversary. It is motivated by the ubiquity of unreliable links in real wireless networks. Existing results in this model [11, 12, 3, 8] assume an offline adaptive adversary - the strongest type of adversary considered in standard randomized analysis. In this paper, we study the two other standard types of adversaries: online adaptive and oblivious. Our goal is to find a model that captures the unpredictable behavior of real networks while still allowing for efficient broadcast solutions. For the online adaptive dual graph model, we prove a lower bound that shows the existence of constant-diameter graphs in which both types of broadcast require Ω(n/ log n) rounds, for network size n. This result is within log-factors of the (near) tight upper bound for the offline adaptive setting. For the oblivious dual graph model, we describe a global broadcast algorithm that solves the problem in O(Dlog n + log[superscript 2] n) rounds for network diameter D, but prove a lower bound of Ω(√n= log n) rounds for local broadcast in this same setting. Finally, under the assumption of geographic constraints on the network graph, we describe a local broadcast algorithm that requires only O(log[superscript 2] n logΔ) rounds in the oblivious model, for maximum degree Δ. In addition to the theoretical interest of these results, we argue that the oblivious model (with geographic constraints) captures enough behavior of real networks to render our efficient algorithms useful for real deployments.en_US
dc.description.sponsorshipFord Motor Company (University Research Program)en_US
dc.description.sponsorshipUnited States. Air Force Office of Scientific Research (AFOSR Contract No. FA9550- 13-1-0042)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (NSF Award No. CCF-1217506)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (NSF Award No. 0939370-CCF)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (NSF Award No. CCF-AF-0937274)en_US
dc.description.sponsorshipUnited States. Air Force Office of Scientific Research (AFOSR Contract No. FA9550-08-1-0159)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (NSF Award No. CCF-072651)en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/2484239.2484259en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleThe cost of radio network broadcast for different models of unreliable linksen_US
dc.typeArticleen_US
dc.identifier.citationGhaffari, Mohsen, Nancy Lynch, and Calvin Newport. “The Cost of Radio Network Broadcast for Different Models of Unreliable Links.” Proceedings of the 2013 ACM Symposium on Principles of Distributed Computing - PODC ’13 (2013), July 22–24, 2013, Montréal, Québec, Canada. ACM New York, NY, USA, p. 345-354.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorGhaffari, Mohsenen_US
dc.contributor.mitauthorLynch, Nancy Annen_US
dc.relation.journalProceedings of the 2013 ACM Symposium on Principles of Distributed Computing - PODC '13en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsGhaffari, Mohsen; Lynch, Nancy; Newport, Calvinen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-3045-265X
dc.identifier.orcidhttps://orcid.org/0000-0003-4213-9898
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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