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Record W2168638178 · doi:10.1109/aina.2006.83

An efficient coverage-based flooding scheme for geocasting in mobile ad hoc networks

2006· article· en· W2168638178 on OpenAlexaff
L. Hughes, Atekeh Maghsoudlou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeocastFlooding (psychology)Computer networkComputer scienceMobile ad hoc networkNetwork packetMulticastWireless ad hoc networkOptimized Link State Routing ProtocolRouting protocolWirelessTelecommunications

Abstract

fetched live from OpenAlex

Geocasting is a variation of traditional multicasting in which the goal is to deliver a packet to all nodes within a geographical area often referred to as a geocast region. Several protocols have been designed for geocasting in mobile ad hoc networks (MANETs) which focus on how to route packets to the geocast region efficiently, while packet delivery within the geocast region is usually achieved by flooding. Given the known limitations of simple flooding on a network, it is beneficial to look for solutions that improve the packet delivery mechanism within a geocast region. In this paper, a protocol is proposed that extends existing geocast protocols by supporting a novel packet delivery mechanism through the use of efficient flooding. The solution employs coverage information and partitioning the geocast region into grids. Using coverage information, only nodes that can reach the new portion of the region, which has not been covered by a transmission, will rebroadcast the packet. The paper shows that this solution is superior to simple flooding since it reduces the number of redundant retransmissions within the geocast region considerably.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.671
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.239
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2006
Admission routes1
Has abstractyes

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