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Record W2135330941 · doi:10.1109/iwcmc.2011.5982655

Versatile medium access control (VMAC) protocol for mobile sensor networks

2011· article· en· W2135330941 on OpenAlexaff
Vincent Ngo, Alagan Anpalagan, Isaac Woungang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceComputer networkWireless sensor networkFrame (networking)ScheduleEnergy consumptionRouting protocolReal-time computingRouting (electronic design automation)

Abstract

fetched live from OpenAlex

In this paper, the problem of mobility handling in wireless sensor network (WSN) is studied with a simple priority backoff technique. To incorporate this technique for stationary and mobile sensor nodes, a novel hybrid MAC protocol called VMAC is designed with a fixed frame length. VMAC combines the advantages of schedule-based MAC for energy savings and contention-based MAC for short transmission delays. To exploit the bandwidth in the network, channel reuse is encouraged and is readily integrated into the protocol. Simulation results using ns2 demonstrate that VMAC with certain frame lengths are suited for selected topologies, but the frame length of one provides sufficient performance. It is also shown that the energy consumption of VMAC is roughly one-third lower compared to pure schedule-based protocol while the average delay is about two-fold less than that of contention-based protocol in one-hop communication scenarios with frame length of one; meaning that VMAC performs very well in short-range communication. The backoff technique is also shown to be fair when nodes contend for medium access and it is even resourceful in speeding up hardware address resolution and routing.

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: Methods · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.970

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 head0.044
GPT teacher head0.298
Teacher spread0.255 · 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
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

Citations3
Published2011
Admission routes1
Has abstractyes

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