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Record W2140714688 · doi:10.1109/ccece.2007.130

Designing Secure Peer-to-Peer Voice Applications in Ad Hoc Wireless Networks

2007· article· en· W2140714688 on OpenAlexaff
Issam Al-Dalati, Ashraf Matrawy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceComputer networkWireless ad hoc networkVoice over IPWireless networkPeer-to-peerNetwork architectureQuality of serviceMobile ad hoc networkWirelessTelecommunicationsNetwork packetThe InternetWorld Wide Web

Abstract

fetched live from OpenAlex

Peer-to-peer (P2P) networking is the exchange of services between nodes at the edge of the network. The main objective of P2P networking is to share un-utilized network resources, i.e. memory, processing power and network bandwidth. P2P services were recently expanded to include voice over IP (VoIP) applications. Voice P2P applications can access peers behind network address translators (NATs), can achieve better quality of service, and may also save cost. These P2P voice applications can be deployed on wireless networks. The advantages of the integration of P2P technology in an ad hoc environment include increasing data rates, enhancing network coverage, and reducing connection establishment time. However, this integration introduces some mobility and security issues that should be taken into consideration when designing these applications to run in ad hoc networks. This paper provides a discussion of the major issues that arise when a P2P voice application is to be designed in an ad hoc wireless network. We briefly survey the state of the art in this new area, provide a discussion of the above advantages and issues, and conclude the paper with a discussion on how to secure the P2P super-nodes in a wireless environment, which are crucial to this architecture.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2007
Admission routes1
Has abstractyes

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