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Record W2085351275 · doi:10.1109/glocom.2012.6503993

Performance analysis of cooperative ADHOC MAC for vehicular networks

2012· article· en· W2085351275 on OpenAlexaff
Sailesh Bharati, Weihua Zhuang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRetransmissionComputer networkComputer scienceNetwork packetVehicular ad hoc networkWireless ad hoc networkMultiple Access with Collision Avoidance for WirelessThroughputReliability (semiconductor)Media access controlChannel (broadcasting)WirelessAccess controlRouting protocolOptimized Link State Routing ProtocolTelecommunications

Abstract

fetched live from OpenAlex

The paradigm of vehicular ad-hoc networks (VANETs) emerges as a promising approach to provide road safety, vehicle traffic management, and infotainment applications. Thus, it is important to develop a VANET medium access control (MAC) protocol that provides an efficient and reliable delivery of packets for diverse applications. Cooperative communication, on the other hand, can enhance the reliability of communication links in VANETs, thus mitigating wireless channel impairments due to a poor channel condition. Recently, a cooperative scheme for MAC in VANETs based on time-division multiple access, referred to as Cooperative ADHOC MAC (CAH-MAC), has been proposed [1]. CAH-MAC is an efficient protocol capable of increasing the network throughput by reducing the wastage of time slots. In CAH-MAC, neighboring nodes cooperate by utilizing the unreserved time slots, for retransmission of a packet which failed to reach its target receiver due to a poor channel condition. In this paper, we study the reliability of CAH-MAC in terms of packet transmission delay (PTD) and packet dropping rate (PDR). Through mathematical analysis and computer simulation, we show that CAH-MAC provides reliable communication by decreasing the PTD and PDR as compared with existing approaches.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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.

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

Citations26
Published2012
Admission routes1
Has abstractyes

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