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Record W4410727826 · doi:10.14419/sxnnhw10

Comprehensive analysis of manet routing protocols and quality of service metrics

2025· article· en· W4410727826 on OpenAlexaff
Saradha S, S. Kayalvili, Gurunath T. Chavan, M. P. Bobby, Shital Dongre, B. Jegajothi

Bibliographic record

VenueInternational Journal of Basic and Applied Sciences · 2025
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsComputer scienceMobile ad hoc networkQuality of serviceRouting (electronic design automation)Routing protocolComputer network

Abstract

fetched live from OpenAlex

The emergence of wireless technology has presented intriguing possibilities in the realm of communications. Because of this, data may be ‎manipulated using wirelessly connected, portable nodes and possess limitations such as low storage capacity, reliance on autonomous ‎energy sources, and restricted bandwidth. A mobile ad-hoc network (MANET) is a type of wireless network that consists of mobile nodes ‎that do not rely on any immovable structure. In this research, the nodes are observed to be able to move and autonomously arrange ‎themselves freely into a network structure. Various protocols have been devised to improve the routing process and provide a path among ‎any two hosts in a computer system. Providing quality of service (QoS) ensures that MANETs pose significant challenges compared to ‎wireline networks. These challenges primarily arise from node movement, multi-hop communications, conflict for channel access, and the ‎absence of central collaboration. The challenges associated with ensuring such guarantees have meaningfully constrained the practicality ‎and effectiveness of MANETs. There has been a dramatic uptick in studies over the past few decades dedicated to addressing the issue of ‎QoS assurances in MANET protocols. This research paper analyses various routing protocols and QoS metrics in MANETs‎.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.053
GPT teacher head0.356
Teacher spread0.303 · 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 designTheoretical or conceptual
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

Citations2
Published2025
Admission routes1
Has abstractyes

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