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Record W2022455894 · doi:10.1109/icsens.2013.6688566

Region based three dimensional real-timel routing protocol for wireless sensor networks

2013· article· en· W2022455894 on OpenAlexaff
Sarab F. Al Rubeaai, Brajendra K. Singh, Kemal Tepe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer networkComputer scienceRouting protocolPacket forwardingNetwork packetGeographic routingWireless sensor networkVirtual routing and forwardingWireless Routing ProtocolDynamic Source RoutingZone Routing ProtocolDistributed computingNode (physics)Engineering

Abstract

fetched live from OpenAlex

This paper presents a novel 3D real-time geographical routing protocol (3DRTGP) for wireless sensor networks (WSNs). It provides a unique adaptive packet forwarding region (PFR) selection algorithm based on network density. 3DRTGP controls the number of forwarding nodes to reduce collision in the network, which in turn, enables it to meet real-time application requirements, i.e., packet delivery within a specified deadline. In order to make 3DRTGP viable, we provide a solution for void node problem (VNP) in 3D deployment. The performance evaluation through simulation shows significant improvements in end-to-end delay and missratio over similar 3D geographical routing protocols.

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 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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.526
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
Bibliometrics0.0000.000
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 head0.257
Teacher spread0.232 · 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 teacher head, not a consensus.

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

Citations3
Published2013
Admission routes1
Has abstractyes

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