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Record W2738496724 · doi:10.23919/inm.2017.7987348

Ensuring two routes connectivity in mobile ad hoc networks with Random Waypoint mobility

2017· article· en· W2738496724 on OpenAlexaff
Tareq Hayajna, Michel Kaldoch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsWaypointMobility modelComputer scienceComputer networkWireless ad hoc networkNode (physics)Mobile ad hoc networkProbabilistic logicReliability (semiconductor)Network packetMobile computingDelay-tolerant networkingDistributed computingVehicular ad hoc networkOptimized Link State Routing ProtocolWirelessReal-time computingRouting protocolEngineeringTelecommunications

Abstract

fetched live from OpenAlex

To increase mobile ad hoc network reliability, virtually decrease the packets loss to zero, and to support multimedia communications multi-route is required. In order to ensure the availability of two routes, node density must be above a certain value. To the best our knowledge, this paper is the first paper that mathematically determines the required node density to ensure the availability of two routes between any randomly chosen source and destination pair in mobile ad hoc networks with random waypoint mobility model. To this end, a complete probabilistic model is provided. The obtained results reveal that the increase in the node density exponentially increases the probability of having two routes. This exponential increase is limited by a certain threshold, after this threshold the increase is negligible. An interesting conclusion from this paper is that the required node densities to ensure two routes connectivity are the same for both mobile nodes moving according to the generalized random waypoint mobility model and static nodes that are uniformly distributed in the network area. This work can be used by mobile ad hoc network designers to study the network reliability and connectivity.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.501
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.013
GPT teacher head0.247
Teacher spread0.235 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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