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Record W2113689857 · doi:10.1109/iscc.2011.5983906

Towards cellular IP address assignment in wireless heterogeneous sensor networks

2011· article· en· W2113689857 on OpenAlexaff
Mazen G. Khair, Burak Kantarcı, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBlocking (statistics)Computer networkAcknowledgementComputer scienceBase stationScheme (mathematics)IP address managementWirelessCall blockingWireless sensor networkBase (topology)Distributed computingInternet ProtocolTelecommunicationsQuality of serviceThe InternetMathematics

Abstract

fetched live from OpenAlex

In this paper, we have proposed a dynamic IP address assignment architecture for wireless heterogeneous sensor networks. The assignment scheme and the architecture guarantee that communication channels can be assigned only between the registered devices ensuring the security. The dynamic IP address assignment scheme is based on the advertisement of the IP address utilization status at the base stations. Thus, each base station advertises its IP address utilization database when the ratio of the negative acknowledgement messages received from the DNS exceeds a certain threshold. By simulations, we have shown that the proposed assignment scheme introduces significant enhancement in terms of blocking probability when compared to an approach where each base station has its own IP address pool. Furthermore, we have defined three types of blocking, the real blocking, the unjustified acceptance and the unjustified rejections. We have seen that the proposed scheme can lead to lower blocking probability compared to the uniform IP assignment as long as the update threshold is kept below 1.5%.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.947

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 science0.0010.001
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 head0.027
GPT teacher head0.223
Teacher spread0.196 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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