MétaCan
Menu
Back to cohort

Vertical Mobility Management Architectures in Wireless Networks: A Comprehensive Survey and Future Directions

2010· article· en· W2146839357 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Communications Surveys & Tutorials · 2010
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceMobility managementHandoverComputer networkWireless networkContext (archaeology)Vertical handoverWirelessContext managementTelecommunicationsHeterogeneous networkUbiquitous computingHuman–computer interaction

Abstract

fetched live from OpenAlex

Mobile users and applications are putting pressure on wireless network operators to improve the seamless handover of devices and services. Strong business competition for subscribers, along with the ever increasing availability of wireless networks will give nomadic and mobile users the opportunity, and systems the power, to make better handover decisions. In this paper, we present a comprehensive review of the literature on mobility management architectures for seamless handover of mobile users in heterogeneous networks. We describe the design rationale for selected architectures, with an in-depth analysis of their main goals, assumptions, and requirements. We also provide directions for further work in this field by highlighting the mandatory requirements and the features of future architectures. We then present a new architecture called Context-Aware Mobility Management System (CAMMS). CAMMS is a new cross-layer, context-aware and interactive approach to seamless handover of users and services. With that proposal, we identified the essential functional entities that must be part of future architectures.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.270
Teacher spread0.249 · 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