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Record W2096077520 · doi:10.1109/pccc.2004.1395026

Position-based routing with a power-aware weighted forwarding function in MANETs

2005· article· en· W2096077520 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 International Conference on Performance, Computing, and Communications, 2004 · 2005
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceComputer networkRouting protocolEnergy consumptionWireless Routing ProtocolLink-state routing protocolVirtual routing and forwardingDestination-Sequenced Distance Vector routingHop (telecommunications)Routing (electronic design automation)Static routingGeographic routingDistributed computingEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Power-aware routing is crucial for battery-finite mobile nodes in MANETs. Energy consumption optimization consists of not only energy saving, but also energy-balanced usage of nodes. In this paper, we present a new position-based routing protocol with a power-aware weighted forwarding function, named PAWF. With this forwarding function, the energy consumption and energy residual values of mobile nodes are asymmetrically combined with forwarding achievement and used to make decisions of hop by hop routing. PAWF routing requires only the local information of strict neighbors and the destination. With the power-awareness, the over-usage of mobile nodes is avoided and the lifetime of the network is increased due to energy consumption balance. Based on simulation results, PAWF is also shown to be able to provide global energy-sufficient near-optimal paths by making locally optimal choices, which offers the possibility to implement further QoS routing in MANETS.

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: Empirical · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.926

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 communication0.0000.001
Open science0.0020.000
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 head0.022
GPT teacher head0.272
Teacher spread0.250 · 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