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Record W2100403022 · doi:10.5555/1535571.1535583

DTN based dominating set routing technique for mobile ad hoc networks

2008· article· en· W2100403022 on OpenAlexaff
Hany Samuel, Weihua Zhuang, Bruno R. Preiss

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer networkRoamingComputer scienceWireless ad hoc networkDistributed computingMobile ad hoc networkAdaptive quality of service multi-hop routingQuality of serviceGeographic routingDelay-tolerant networkingOptimized Link State Routing ProtocolNode (physics)Routing protocolWireless networkRouting (electronic design automation)Wireless Routing ProtocolWirelessEngineeringTelecommunications

Abstract

fetched live from OpenAlex

With the wide spread of user mobility and the existence of various types of wireless networks, effective inter-networking is mandatory in order to support user roaming while maintaining the connectivity. In our previous work [1], we propose a system architecture to achieve the connectivity over a heterogeneous wireless network, which employs the delay-tolerant network (DTN) concept to overcome the problem of potential intermittent connections caused by user roaming and ensures quality of service (QoS) in the presence of a long disconnection period. In this paper, we present a new routing technique for mobile ad hoc networks (MANETs) based on the DTN system architecture. We introduce the concept of virtual network topology and redefine the dominating-set based routing for the challenged network environment under consideration. In addition, we propose a time based methodology to predict the probability of future contacts between node pairs to construct the virtual network topology. Simulation results demonstrate the effectiveness of the proposed dominating-set based routing scheme under the DTN system architecture.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.261
Teacher spread0.230 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

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