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Record W2040187817 · doi:10.1145/1582379.1582423

Performance analysis of BitTorrent enabled ad hoc network routing

2009· article· en· W2040187817 on OpenAlexaff
Padmini Vellore, Paul Gillard, R. Venkatesan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer networkComputer scienceUnicastDynamic Source RoutingAd hoc On-Demand Distance Vector RoutingDistributed computingWireless Routing ProtocolOptimized Link State Routing ProtocolRouting protocolWireless ad hoc networkZone Routing ProtocolThroughputNetwork packetMulticastDestination-Sequenced Distance Vector routingWirelessTelecommunications

Abstract

fetched live from OpenAlex

The analytical model of BitTorrent Enabled Ad hoc Network (BEAN) routing protocol (unicast extension of Multi-cast BEAN - MBEAN routing protocol) is discussed for a random network. The source node forms multiple routes to destination differentiating each route by it's neighbor and sends packets through the best route. When a link or route fails, the source node chooses one of the existing alternate routes. This paper compares the throughput obtained analytically with that obtained in simulation for both AODV (Ad hoc On Demand Distance Vector) and BEAN routing protocols. Further simulations are run for realistic cases and the two protocols are compared for packet delivery ratio (PDR) and system throughput. The results show that, in spite of expending more time and control packets in finding a new route, BEAN performs as good as and in many cases, better than AODV, for a network with high density, in terms of throughput and PDR, thereby making it a potential unicast routing protocol for multicast applications.

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)0.000
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 score0.721
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.225
Teacher spread0.215 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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