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Record W2153360118 · doi:10.4236/wsn.2009.15049

An Energy-Efficient MAC Protocol for Ad Hoc Networks

2009· article· en· W2153360118 on OpenAlexaff
Yongsheng Shi, T. Aaron Gulliver

Bibliographic record

VenueWireless Sensor Network · 2009
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceComputer networkMobile ad hoc networkNetwork allocation vectorIEEE 802.11sMultiple Access with Collision Avoidance for WirelessThroughputWireless ad hoc networkOptimized Link State Routing ProtocolIEEE 802.1XIEEE 802.11Inter-Access Point ProtocolWireless networkDistributed computingWirelessRouting protocolTelecommunicationsWi-FiNetwork packetWireless mesh network

Abstract

fetched live from OpenAlex

A mobile ad hoc network (MANET) is a collection of nodes equipped with wireless communications and a networking capability without central network control. Nodes in a MANET are free to move and organize themselves in an arbitrary fashion. Energy-efficient design is a significant challenge due to the characteristics of MANETs such as distributed control, constantly changing network topology, and mobile users with limited power supply. The IEEE 802.11 MAC protocol includes a power saving mechanism, but it has many limitations. A new energy-efficient MAC protocol (EE-MAC) is proposed in this paper. It is shown that EE-MAC performs better than IEEE 802.11 power saving mode and exceeds IEEE 802.11 with respect to balancing network throughput and energy savings.

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)0.003
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.004

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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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

Citations14
Published2009
Admission routes1
Has abstractyes

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