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Record W4239495461 · doi:10.22215/etd/2018-13291

A DHT-Based Routing Solution for Hierarchical MANETs

2018· dissertation· en· W4239495461 on OpenAlexaff
Ngozi Echegini

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsCarleton University
FundersResearch, Development and Engineering Command
KeywordsFlooding (psychology)Computer networkComputer scienceDefault gatewayRouting (electronic design automation)Distributed computingRouting protocolInterconnectionBackbone networkOverlay networkNode (physics)Hierarchical routingStatic routingEngineeringWorld Wide WebThe Internet

Abstract

fetched live from OpenAlex

This thesis presents an effective routing solution for the backbone of hierarchical Mobile Ad hoc Networks (MANETs). Our solution leverages the storage and retrieval mechanisms of a Distributed Hash Table (DHT) to make routing information available in a decentralized fashion, while supporting different forms of node and network mobility scenarios effectively. We do so by splitting a flat network into clusters, each having a gateway who participates in a DHT overlay. These gateways interconnect the clusters in a backbone network. Two routing approaches for the backbone are explored: flooding, which we use as a base approach, and our solution, which is DHT-based. We compare the performance of our solution against the flooding approach via experimentation in a simulator. Our results show that our DHT-based solution, even in the presence of mobility, achieved above 90% success rates and maintained very low and constant round trip times, which was not the case with the flooding approach. The advantage of our proposed approach increases as the number of clusters increases, demonstrating the superior scalability of our proposed approach. i

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.895
Threshold uncertainty score1.000

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 science0.0020.000
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 head0.020
GPT teacher head0.288
Teacher spread0.268 · 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.

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
Published2018
Admission routes1
Has abstractyes

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