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Record W1872539111

Refinement of AODV routing algorithm for wireless mesh networks (WMNs)

2011· article· en· W1872539111 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIranian Conference on Electrical Engineering · 2011
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer networkWireless mesh networkDynamic Source RoutingAd hoc On-Demand Distance Vector RoutingOptimized Link State Routing ProtocolWireless Routing ProtocolDistributed computingRouting protocolZone Routing ProtocolAd hoc wireless distribution serviceLink-state routing protocolWireless ad hoc networkRouting (electronic design automation)Wireless networkWirelessTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Wireless Mesh Networks (WMNs) has recently gained popularities due to their fast and economical ways to access to the Internet. Because the WMNs are one of the few commonly implemented types of the ad hoc networks (MANETs), general MANET routing protocols can be used in the WMNs. it also is expected that a protocol that takes the particularities of the WMNs into account will outperform the general protocol. In this paper, we present a reactive routing protocol, called AODV-Mesh, for the WMNs. This protocol is an extension of a common ad hoc routing protocol to be compatible with the specifications of the WMNs. In addition, we discuss the route recovery mechanism of this protocol in two cases; using the local repair mechanism (AODV-Mesh), and not using the local repair mechanism (AODV-Mesh NoLR). Our simulation results show that not using the local repair mechanism in the AODV-Mesh algorithm improves the performance and efficiency.

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.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.222
Teacher spread0.198 · 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