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Record W1989857303 · doi:10.1109/icc.2013.6654762

Online heuristics for monetary-based courier relaying in RFID-Sensor Networks

2013· article· en· W1989857303 on OpenAlexaff
Ashraf E. Al-Fagih, Fadi Al‐Turjman, Hossam S. Hassanein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceNetwork packetComputer networkHeuristicsRelayWirelessTransmission (telecommunications)Telecommunications

Abstract

fetched live from OpenAlex

In integrated RFID and Wireless Sensor Networks (RSNs), the abundance of wirelessly enabled mobile devices facilitates forwarding data packets. This presents a beneficial alternative to offload transmission from relay nodes to access points. However, there is no incentive for such mobile devices to carry the relaying task. Hence, we introduce heuristics for Monetary-based Courier Relaying (MCR) that incorporates price negotiation for relaying from source nodes to access points via mobile couriers in RSN architectures. Our heuristics employ a threshold price for each packet prior to transmission. Whether to forward the packet to a courier or to directly transmit it to access points depends on a criticalness function, in addition to the courier's charge with respect to the packet's threshold price. We compare our MCR model with other dominant mobile Ad hoc delivery schemes; eliciting its efficiency in terms of energy, cost and delivery rate.

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 score0.498
Threshold uncertainty score0.758

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 communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

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