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Record W2781398886 · doi:10.1504/ijssc.2017.10010065

A* search based next hop selection for routing in opportunistic networks

2017· article· en· W2781398886 on OpenAlexaff
Sahil Gupta, Isaac Woungang, Satbir Jain, Satya Jyoti Borah, Pragya Kuchhal, Sanjay Kumar Dhurandher

Bibliographic record

VenueInternational Journal of Space-Based and Situated Computing · 2017
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer networkComputer scienceGeographic routingRouting protocolHop (telecommunications)Node (physics)Routing (electronic design automation)Distributed computingDynamic Source RoutingBenchmark (surveying)Wireless Routing ProtocolGeographyEngineering

Abstract

fetched live from OpenAlex

Opportunistic network (oppnet) is one of the challenging fields of wireless network. It is an intermittently connected mobile network where nodes are mobile and no end-to-end path exists. Connectivity among nodes is established when they are within the transmission range of each other. If a node has message to communicate and no intermediate node is available, then the message is stored in the node's buffer till an appropriate communication opportunity arises which is known as store-carry-forward paradigm. This paradigm has given Oppnets a new direction in the field of research. In any network, efficient and effective routing is very crucial. In this paper, a novel routing technique has been proposed for Oppnets named as A* based opportunistic routing (A*OR) which uses the A* searching technique to select the best forwarders towards the destination. The proposed protocol is compared with prophet, PRoWait and EDR as a benchmark protocols and is found to perform 19%, 11% and 32% better than prophet, PRoWait and EDR, respectively in terms of delivery probability.

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: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.956

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 communication0.0010.000
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 head0.047
GPT teacher head0.303
Teacher spread0.256 · 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
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
Published2017
Admission routes1
Has abstractyes

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