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Record W2001313361 · doi:10.1002/wcm.118

Medium access control protocols for wireless mobile ad hoc networks: issues and approaches

2003· article· en· W2001313361 on OpenAlexaff
Teerawat Issariyakul, Ekram Hossain, Dong In Kim

Bibliographic record

VenueWireless Communications and Mobile Computing · 2003
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsSimon Fraser UniversityUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceComputer networkWireless ad hoc networkAd hoc wireless distribution serviceAccess controlMobile ad hoc networkMultiple Access with Collision Avoidance for WirelessThroughputNode (physics)Optimized Link State Routing ProtocolVehicular ad hoc networkMedia access controlWirelessWireless networkRouting protocolDistributed computingRouting (electronic design automation)TelecommunicationsNetwork packet

Abstract

fetched live from OpenAlex

Abstract In this article, a comprehensive survey of the medium access control (MAC) approaches for wireless mobile ad hoc networks is presented. The complexity in MAC design for wireless ad hoc networks arises due to node mobility, radio link vulnerability and the lack of central coordination. A series of studies on MAC design has been conducted in the literature to improve medium access performance in different aspects as identified by the different performance metrics. Tradeoffs among the different performance metrics (such as between throughput and fairness) dictate the design of a suitable MAC protocol. We compare the different proposed MAC approaches, identify their problems and discuss the possible remedies. The interactions among the MAC and the higher layer protocols such as routing and transport layer protocols are discussed and some interesting research issues are also identified. Copyright © 2003 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)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.006
Science and technology studies0.0020.007
Scholarly communication0.0080.012
Open science0.0050.004
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0020.002

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 head0.053
GPT teacher head0.322
Teacher spread0.270 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations28
Published2003
Admission routes1
Has abstractyes

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