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Record W2145885111 · doi:10.1109/mcom.2002.1018018

Position-based routing in ad hoc networks

2002· article· en· W2145885111 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

VenueIEEE Communications Magazine · 2002
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceComputer networkLink-state routing protocolDistributed computingRouting protocolStatic routingDestination-Sequenced Distance Vector routingDynamic Source RoutingRobustness (evolution)Wireless ad hoc networkScalabilityInterior gateway protocolRouting (electronic design automation)WirelessTelecommunications

Abstract

fetched live from OpenAlex

The availability of small, inexpensive low-power GPS receivers and techniques for finding relative coordinates based on signal strengths, and the need for the design of power-efficient and scalable networks provided justification for applying position-based routing methods in ad hoc networks. A number of such algorithms were developed previously. This tutorial will concentrate on schemes that are loop-free, localized, and follow a single-path strategy, which are desirable characteristics for scalable routing protocols. Routing protocols have two modes: greedy mode (when the forwarding node is able to advance the message toward the destination) and recovery mode (applied until return to greedy mode is possible). We discuss them separately. Methods also differ in metrics used (hop count, power, cost, congestion, etc.), and in past traffic memorization at nodes (memoryless or memorizing past traffic). Salient properties to be emphasized in this review are guaranteed delivery, scalability, and robustness.

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 categoriesnone
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 score0.927
Threshold uncertainty score0.755

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 science0.0030.000
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 head0.030
GPT teacher head0.260
Teacher spread0.230 · 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