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Record W2098441621 · doi:10.1109/itng.2009.255

An Online Multipath Routing Algorithm for Maximizing Lifetime in Wireless Sensor Networks

2009· article· en· W2098441621 on OpenAlex
Mahmood R. Minhas, Sathish Gopalakrishnan, Victor C. M. Leung

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMultipath routingComputer scienceDynamic Source RoutingComputer networkStatic routingMultipath propagationDestination-Sequenced Distance Vector routingLink-state routing protocolEqual-cost multi-path routingPolicy-based routingRouting (electronic design automation)AlgorithmDistributed computingRouting protocolChannel (broadcasting)

Abstract

fetched live from OpenAlex

We address the maximum lifetime routing problem in wireless sensor networks, and present an online multipath routing algorithm. The proposed algorithm strives to maximize the network lifetime metric by distributing the source-to-sink traffic for a given routing request along a set of paths. Fuzzy membership function is used for designing the edge weight function. Simulation results obtained under a variety of network scenarios show that the proposed multipath scheme is able to achieve better lifetime results than those obtained by its predecessor single-path fuzzy routing scheme as well as by another well-known online routing scheme, namely the Online Maximum Lifetime heuristic.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.462
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.019
GPT teacher head0.258
Teacher spread0.239 · 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

Quick stats

Citations23
Published2009
Admission routes1
Has abstractyes

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