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Record W2149749279 · doi:10.1109/ccece.2005.1557168

An architecture for integrating mobile ad hoc networks with the internet using multiple mobile gateways

2006· article· en· W2149749279 on OpenAlexaff
Mieso K. Denko, Chen Wei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsComputer networkComputer scienceMobile ad hoc networkAd hoc On-Demand Distance Vector RoutingWireless ad hoc networkMobile computingOptimized Link State Routing ProtocolVehicular ad hoc networkDistributed computingMobile IPThe InternetNode (physics)Routing protocolRouting (electronic design automation)TelecommunicationsWirelessEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

Mobile ad hoc networks (MANETs) are typically considered as stand-alone, autonomous networks that support multihop communication without relying on any existing infrastructure. However, the integration of MANETs and infrastructure networks such as the Internet extends the network coverage and increases the application domain of ad hoc networks. In this paper we propose an architecture for integrating MANETs and the Internet using multiple mobile gateways. We used an extended ad hoc on demand distance vector (AODV) routing protocol and mobile IP (MIP) to achieve the integration. The proposed architecture has two main features. First, it provides global communication between MANETs and the Internet using a subset MANET nodes called mobile gateways (MGs) and the MIP foreign agents. The MGs are selected among MANET nodes based on the node stability, load and distance metrics. Second, it allows the MANET nodes to maintain multiple routes to the MGs using hybrid gateway discovery mechanisms. The simulation results of the proposed architecture indicate that the use of multiple mobile gateways and hybrid gateway discovery mechanisms enhance the network performance while providing bi-directional Internet connectivity

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score0.807

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 communication0.0010.000
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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

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