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Record W2604831683 · doi:10.24138/jcomss.v2i1.303

Mobile IP Address Efficiency

2017· article· en· W2604831683 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

VenueJournal of Communications Software and Systems · 2017
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceIPv4Mobile IPComputer networkNetwork address translationNAT traversalIP address managementNetwork addressIPv6IPv6 addressAddress spaceMobile computingHost (biology)Protocol (science)Computer securityNode (physics)ARP spoofingWireless networkInternet ProtocolTelecommunicationsWirelessThe InternetWorld Wide Web

Abstract

fetched live from OpenAlex

In future wireless networks, Mobile IP will be widely deployed as a general mobility protocol. Currently, in theprotocol each mobile node (MN) should have one public home address to identify itself when it is away from home. Unlike the stationary host, the MN cannot simply use private addresses when NAT (Network Address Translation) is enabled. How to assign public addresses among mobile nodes is important to save the already limited IPv4 addresses. Even though Mobile IPv6 can provide a large address space, when communicating with IPv4 based hosts, the MN still needs to use one public IPv4 address. Protocol translation can map between IPv6 and IPv4 addresses;however, it is a NAT-based approach and breaks end-to-endcommunications. From a new perspective, we propose anaddress-sharing mechanism that allows a large number of MNs to share only one IPv4 public address while avoiding most of the drawbacks of NAT.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.440

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 studies0.0010.000
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 head0.031
GPT teacher head0.285
Teacher spread0.255 · 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