Towards Seamless Service Mobility for Mobile Devices Communicating Within Wireless Grids
Bibliographic record
Abstract
The proliferation of wireless infrastructures and devices is expected to enable new types of value-added applications. Example applications include support of a multi-player game where resources of different devices are shared to collaboratively play a game. Users of such applications require continuous and seamless access to resources while roaming between different access technologies. This calls for a generic wireless service management platform that adequately manages service discovery, distribution, security, billing, and mobility. This paper introduces a Service Roaming Protocol (SRP) as the mechanism to manage service mobility. SRP's main advantage over exiting mobility management algorithm is its context-awareness. The SRP algorithm uses mobile user's geographic location, terminal's capabilities, personal profile, and resources of other grid devices, to provide efficient management of mobile users and services. SRP operation relies on primitives provided by a wireless grid middleware. These primitives include service definition and efficient service distribution and mobility. The wireless grid middleware provides a platform-independent solution to overcome heterogeneity of mobile terminals and provides autonomous service management for mobile grid users. This helps transforming a wireless infrastructure from an infrastructure built for only broadband Internet access into an infrastructure that supports collaborative applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".