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Record W2113041149 · doi:10.1109/ntms.2011.5720618

Mobility Assisted Routing in Mobile Ad Hoc Networks

2011· article· en· W2113041149 on OpenAlexaff
Kazi Atiqur Rahman, Kemal Tepe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer networkComputer scienceDSRFLOWPacket lossNetwork packetSource routingRouting protocolEnd-to-end delayDynamic Source RoutingWireless Routing ProtocolOptimized Link State Routing ProtocolWireless ad hoc networkMobile ad hoc networkDistributed computingWirelessTelecommunications

Abstract

fetched live from OpenAlex

This paper presents an algorithm to include mobility in a routing protocol to reduce packet losses in mobile ad hoc networks (MANETs). The algorithm is applicable to any on-demand routing protocol. If the degree of mobility of any node in any route increases, the route life time decreases. That causes frequent link failures, and results more packet loss and low throughput. Packet loss requires packet retransmissions, which further overload the network and can cause additional latency and packet loss. The proposed algorithm estimates the number of packets that can traverse through the route before it breaks be-cause of mobility. This algorithm increases network throughput and packet delivery ratio. The algorithm is implemented in dynamic source routing (DSR) protocol, and simulated in Network Simulator-2. The simulation results show that the packet delivery ratio of DSR with the algorithm can improve up to forty six percent over DSR in mobile ad hoc networks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.243
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2011
Admission routes1
Has abstractyes

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