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Record W2067235323 · doi:10.7125/apan.32.10

Experiences with SeaMo: A Vertical Handoff Implementation for Heterogeneous Wireless Networks

2011· article· en· W2067235323 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the Asia-Pacific Advanced Network · 2011
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsnot available
FundersSoutheastern Ontario Academic Medical Organization
KeywordsHandoverComputer scienceComputer networkWireless networkVertical handoverWirelessHeterogeneous networkTelecommunications

Abstract

fetched live from OpenAlex

SeaMo, a vertical handoff (VHO) implementation based on our earlier paper [1] is tested with various mobility scenarios on a mobile IP testbed using MIPv6 and HIP protocols for mobility management. SeaMo considers various parameters like RSSI, link quality metric, end-to-end available bandwidth, battery power, network usage costs etc., in making the VHO decision. We present our experiences using SeaMo in mobility scenarios involving 3G and WLAN. We present a set of results which demonstrate the performance of SeaMo. We explain the need for considering a wide range of parameters from RSSI to network usage cost. We also highlight the impact of using the available battery power in decision making. In addition, we attempt to measure typical values of parameters such as network association delay, and handoff decision delay that impact the QoS.

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.

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 score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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