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Record W2144388829 · doi:10.1145/2068897.2068928

Motion-based routing for opportunistic ad-hoc networks

2011· article· en· W2144388829 on OpenAlexaff
Weihan Wang, Cristiana Amza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer networkLink-state routing protocolDistributed computingRouting protocolGeographic routingWireless ad hoc networkStatic routingDynamic Source RoutingDestination-Sequenced Distance Vector routingRouting (electronic design automation)WirelessTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we introduce novel motion-based routing protocols for opportunistic packet relay in ad-hoc opportunistic networks. Unlike existing ad-hoc routing protocols, which require global knowledge about the mobility patterns of other nodes, thus raising scalability and privacy concerns, our routing protocols store and use only local motion information, such as the current direction of the local node. Such in- formation is easily obtainable by any modern GPS-equipped mobile device. We design and evaluate a series of protocols using different motion information, ranging from simple, easy to capture metrics i.e., speed and direction, to more complex metrics, i.e., the past or expected trajectory of the local node. These metrics allow for increasing degrees of routing accuracy at a correspondingly higher cost in terms of memory and computation. However, all of our routing protocols have constant complexity in the size of the network, and none of them rely on nodes sending or storing information about other for- warding nodes. This makes our schemes ideal for large-scale networks and non-community networks, where membership is very dynamic.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.741

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 science0.0010.000
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 head0.072
GPT teacher head0.243
Teacher spread0.171 · 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
Published2011
Admission routes1
Has abstractyes

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