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Record W2936076319 · doi:10.5815/ijwmt.2019.02.01

Convergence of MANET in Communication among Smart Devices in IoT

2019· article· en· W2936076319 on OpenAlexaff
Tanweer Alam, Baha Rababah

Bibliographic record

VenueInternational Journal of Wireless and Microwave Technologies · 2019
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMobile ad hoc networkComputer scienceInternet of ThingsInformation exchangeComputer networkConvergence (economics)Bridge (graph theory)Mobile deviceSmart deviceComputer securityTelecommunicationsWorld Wide WebHuman–computer interactionNetwork packet

Abstract

fetched live from OpenAlex

In the next generation network, the physical things will enable to exchange the information among them. Internet of Things (IoT) is an emerging technology that provides facility to connect physical things with the digital world and able to exchange the information. Mobile ad-hoc networks (MANET) is consistently selfdesigning, framework less system of smart devices associated with each other remotely. Every smart device is enabled to change their locations using the mobility feature of MANET. These devices are also able to act as a bridge to exchange information between devices. MANET in the IoT becomes more attractive with its important approach in the communication among smart objects because MANET has a special feature that can create a network by own self or can connect with another huge network. In this research, the authors propose a solution that describes the convergence of MANET in the IoT. The results found in this paper have been tested and implemented using different seniors.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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 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

Citations60
Published2019
Admission routes1
Has abstractyes

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