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Record W2107318399 · doi:10.1109/mcom.2005.1404604

Performance evaluation of multihop ad hoc WLANs

2005· article· en· W2107318399 on OpenAlexafffund
Farshad Eshghi, A.K. Elhakeem, Yousef R. Shayan

Bibliographic record

VenueIEEE Communications Magazine · 2005
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsConcordia University
FundersConcordia University
KeywordsComputer scienceComputer networkWireless ad hoc networkIEEE 802.11Mobile ad hoc networkNetwork packetVehicular ad hoc networkWirelessDistributed computingAd hoc wireless distribution serviceWireless networkOptimized Link State Routing ProtocolRouting protocolTelecommunications

Abstract

fetched live from OpenAlex

Ongoing technological advances in portable devices, coupled with the need for continuous connectivity while mobile, have made ad hoc networks a compelling research and development topic, particularly in a challenging multimedia multihop scenario. The ability of IEEE 802.11's ad hoc mode of operation, as a dominating wireless local area network (WLAN) protocol, to serve multihop networks requires thorough investigation. In this article, through considering crucial real-life physical phenomena and avoiding as many confining assumptions as possible, system performance measures such as delay and packet failure rate are evaluated. As a result, the importance of adequate selection of the system parameters toward performance improvement is underscored. Moreover, the simulation results imply that by complementing through priority provisions, coordination, route reservation, clustering, and optimum channel coding considerations, the IEEE 802.11 medium access control (MAC) protocol can survive in a multihop scenario. The custom simulation environment developed features modularity, comprising traffic generator, mobility, wireless channel, and IEEE 802.11 protocol modules, and is capable of accommodating many more of the physical phenomena involved.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.529

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 communication0.0000.001
Open science0.0030.000
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 head0.052
GPT teacher head0.312
Teacher spread0.260 · 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 designOther design
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

Citations21
Published2005
Admission routes2
Has abstractyes

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