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Record W2076647216 · doi:10.1002/wcm.948

Security mechanism for voice over multipath mobile<i>ad hoc</i>networks

2010· article· en· W2076647216 on OpenAlexaff
Binod Vaidya, Mieso K. Denko, Joel J. P. C. Rodrigues

Bibliographic record

VenueWireless Communications and Mobile Computing · 2010
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Guelph
FundersInstituto de Telecomunicações
KeywordsComputer scienceComputer networkMobile ad hoc networkVoice over IPWireless networkMultipath propagationWireless ad hoc networkWirelessCorrectnessComputer securityTelecommunicationsNetwork packetThe Internet

Abstract

fetched live from OpenAlex

Abstract In the next generation of wireless communication systems, there will be a need for the rapid deployment of independent mobile users for video and voice communication over wireless network. It can be seen that since the demand for Voice over IP (VoIP) over wireless network is growing, the use of VoIP over mobile ad hoc network (MANET) is expected to grow as well. However, security is a critical issue in MANETs where mobile nodes communicate with each other over relatively unreliable wireless links without any preexisting infrastructure. In this paper, we present a framework for secure voice transmission over multipath MANET. An efficient traffic allocation approach for multipath MANET is proposed in order to deliver real‐time traffic over it. The proposed security mechanism was evaluated through two analyses. The correctness of the proposed mechanism was proved using Gong, Needham, and Yahalom (GNY) logic while the security analysis shows that the proposed scheme is resistant to various attacks. For the performance evaluation of the proposed framework, simulations were conducted and the results of the simulation experiments show that it is efficient and robust in terms of various performance metrics. Copyright © 2010 John Wiley &amp; Sons, Ltd.

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 categoriesMeta-epidemiology (narrow)
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 score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.003
Research integrity0.0000.001
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 head0.270
Teacher spread0.258 · 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.

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

Citations8
Published2010
Admission routes1
Has abstractyes

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