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Record W3092706418 · doi:10.3991/ijim.v14i17.16639

MROM Scheme to Improve Handoff Performance in Mobile Networks

2020· article· en· W3092706418 on OpenAlexfundno aff
Ahmed A. Mousa, Aisha Hassan Abdalla Hashim, Huda Adibah Mohd Ramli

Bibliographic record

VenueInternational Journal of Interactive Mobile Technologies (iJIM) · 2020
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsnot available
FundersMinistère des Ressources Naturelles et de la Faune
KeywordsMultihomingHandoverComputer networkComputer scienceRouterMobile IPNetwork packetRouting protocolRouting (electronic design automation)ThroughputMobility managementDistributed computingThe InternetInternet ProtocolWirelessTelecommunications

Abstract

fetched live from OpenAlex

Mobile Router (MR) mobility supported by Network Mobility Basic Support Protocol (NEMO BS) is a Mobile IPv6 (MIPv6) extension that supports Host Mobility. Proposed Multihoming and Route Optimization for MANEMO (MROM) scheme is designed to provide Route Optimization (RO) and Multihomed in NEMO architectures. This paper proposes two novel schemes; MANEMO routing scheme and Multihoming-based scheme. These are to provide support for next generation networks. The proposed MROM scheme differs from other schemes for NEMO environment because it considers the requirements of more application flows parameters as packet lost delivery, handoff delay as well as throughput). Another difference is that not only the network infrastructure can begin the functionality of flow routing, but also an Edge Mobile Router (EMR) can do this flow for routing. Moreover, it utilizes the state of the art and presently active access network to perform the separation of each flow in mobile network. Thus, proposed MROM exhibits multihoming features and improves handoff performance by initiating flow-based fast registration process in NEMO environment. A handoff method is proposed with enhanced functionalities of the Local Mobility Anchors (LMA), Mobile Routers (MRs) and signaling messages with a view to achieve continuous connectivity through handoff in NEMO. Both analytical and simulation approaches are used. Analytical evaluation is carried out to analyze packet delivery lost and handoff delay of our proposed scheme. It was also shown that cost of signaling messages and packet delivery are contributing to total handoff cost. At the simulation part, network simulator 3 (NS 3) has been used as the tool to get performance metrics that have been considered like packet delivery ratio, handoff delay, and packet loss. Our proposed scheme (MROM) has been benchmarking to the standard NEMO BS Protocol and P-NEMO. In this paper, we discuss proposed MROM for next generation networks, providing detailed analysis with a numerical model, proposed MROM, by maximizing the handoff performance, has been justified to have better mobility support than the ordinary NEMO BS Protocol and PNEMO.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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