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Record W2170503384 · doi:10.1145/570758.570767

Energy-aware routing in MANETs

2002· article· en· W2170503384 on OpenAlexaff
Ahmed Safwat, Hossam S. Hassanein, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer networkComputer scienceWireless Routing ProtocolOptimized Link State Routing ProtocolAd hoc wireless distribution serviceDynamic Source RoutingRouting protocolLink-state routing protocolWireless ad hoc networkAdaptive quality of service multi-hop routingZone Routing ProtocolDistributed computingWirelessRouting (electronic design automation)Telecommunications

Abstract

fetched live from OpenAlex

Wireless mobile ad hoc stations have limited battery capacity. Hence, ad hoc routing protocols ought to be energy conservative. However, The simulation studies carried out for table-driven and on-demand ad hoc routing protocols fall short of examining essential power-based performance metrics, such as average node and network lifetime, energy-based protocol fairness, average dissipated energy per protocol, and standard deviation of the energy dissipated by each individual node. In this paper, we present a thorough energy-based performance study of power-aware routing protocols for wireless mobile ad hoc networks. Our energy consumption model is based on a detailed implementation of the IEEE 802.11 physical layer convergence protocol (PLCP) and medium access control (MAC) sublayers. To our best knowledge, this is the first such detailed performance study. Moreover, we propose some novel enhancements to routing in wireless ad hoc networks that enables the admission of flows without jeopardizing the limited energy of the wireless stations.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.206
Teacher spread0.190 · 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
GenreEmpirical

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

Citations34
Published2002
Admission routes1
Has abstractyes

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