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Record W2107812461 · doi:10.1002/dac.843

An energy‐efficient multipath routing protocol for wireless sensor networks

2006· article· en· W2107812461 on OpenAlexafffund
Ye Ming Lu, Vincent W. S. Wong

Bibliographic record

VenueInternational Journal of Communication Systems · 2006
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceComputer networkMultipath routingWireless Routing ProtocolDynamic Source RoutingRouting protocolLink-state routing protocolDistributed computingZone Routing ProtocolStatic routingGeographic routingRouting (electronic design automation)

Abstract

fetched live from OpenAlex

Abstract The energy consumption is a key design criterion for the routing protocols in wireless sensor networks. Some of the conventional single path routing schemes may not be optimal to maximize the network lifetime and connectivity. In this paper, we propose a distributed, scalable and localized multipath search protocol to discover multiple node‐disjoint paths between the sink and source nodes. We also propose a load balancing algorithm to distribute the traffic over the multiple paths discovered. We compare our proposed scheme with the directed diffusion, directed transmission,N‐to‐1 multipath routing, and the energy‐aware routing protocols. Simulation results show that our proposed scheme has a higher node energy efficiency, lower average delay and control overhead than those protocols. Copyright © 2006 John Wiley & Sons, Ltd.

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)0.000
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.303
Teacher spread0.284 · 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
GenreMethods

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

Citations191
Published2006
Admission routes2
Has abstractyes

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