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Record W206305018

Performance evaluation of hierarchical ad hoc networks.

2005· article· en· W206305018 on OpenAlexaboutno aff
Tai. Yu

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2005
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceWireless ad hoc networkPost hocTelecommunicationsMedicine
DOInot available

Abstract

fetched live from OpenAlex

Ad hoc networking is one of the most challenging areas of wireless communication. Theoretical analysis and experimental results show that QoS (Quality of Service) for each node degrades rapidly while the number of nodes increases in the network. One way to solve performance degradation is to use hierarchical network architectures. In this paper, we investigate performance improvements offered by hierarchical ad hoc networks over flat (non-hierarchical or conventional) ad hoc networks for QoS parameters, namely throughput capacity, delay and power efficiency. We investigated and identified trade-offs among those QoS parameters via computer simulations carried by Network Simulator 2 of University of California (NS-2). In those simulations, we created hierarchical ad hoc networks by clustering the networks using cluster head nodes. Initially network is static (no mobility). Results of static network simulations act as benchmark for the performance parameters. Later mobility scenarios are added into the network to observe how mobility affects the performance. In order to compare two architectures, hierarchical and flat, we systematically changed number of nodes, data packet generation rates, number of clusters, node densities and transmission ranges for the nodes. At the same time, we compared hierarchical ad hoc network architecture with WLAN architecture, which has full infrastructure. Simulation results state that throughput performance is linear with numbers of clusters; and in hierarchical architecture, power efficiency is doubled and delay is significantly lower than flat architecture. Our simulation results conclude that clustering schemes in wireless ad hoc networks can solve the scalability problem that exists in flat architectures.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .Y83. Source: Masters Abstracts International, Volume: 44-01, page: 0499. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.233
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2005
Admission routes1
Has abstractyes

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