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Record W1894885767 · doi:10.1109/icccn.2001.956319

Load-sensitive routing for mobile ad hoc networks

2002· article· en· W1894885767 on OpenAlexaff
Kui Wu, Janelle Harms

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer networkComputer scienceDynamic Source RoutingWireless Routing ProtocolZone Routing ProtocolLink-state routing protocolRouting protocolDistributed computingStatic routingDestination-Sequenced Distance Vector routingOptimized Link State Routing ProtocolInterior gateway protocolDSRFLOWRouting (electronic design automation)

Abstract

fetched live from OpenAlex

A mobile ad hoc network (MANET) is a collection of wireless mobile computers forming a temporary network with no existing wired infrastructure. Due to the dynamic nature of network topology and the resource constraints, routing in MANETs is a challenging task. Distributing the routing tasks fairly has eminent advantages, such as reducing the possibility of power depletion and queuing delay in the hosts with heavy duties. However, most current routing protocols for MANETs do not take load balancing into account. In this paper, we propose a load-sensitive on-demand routing approach, which utilizes the network load information as the main route selection criterion. We perform a simulation study on the proposed routing protocol. Compared with the dynamic source routing (DSR), our protocol shows better performance in terms of packet delivery ratio and average end-to-end delay. Further, with low mobility, these benefits are gained without an increase in the control overhead.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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

Citations84
Published2002
Admission routes1
Has abstractyes

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