On the Interaction of Charging-Aware Mobility and Wireless Capacity
Bibliographic record
Abstract
Today's mobile battery-powered communication devices require that users access chargers via wired and, recently, wireless recharging facilities. For a device departing from a location with a given energy “budget”, a plausible strategy is to seek a charger location once the energy is exhausted. We present a model of mobile nodes that captures the paths followed by the nodes with depleted energy seeking, possibly via a detour, to reach a charger. The derived location-dependent mobile node density distribution is used to express the location-dependent congestion of a wireless network whose capacity is used by the mobile nodes. The boundaries, and the relative placement of the charger, create intriguing discontinuities in the probability density function of the nodes across space. We find that chargers are not always “hotspots” in terms of node density, and that the energy budget of the nodes at the beginning of their trip impacts that density and determines the hotspot. Moreover, upon energy depletion, to detour from one's path or not, results in a distinctly different relative impact of the charger placement on the ability of nodes to sustain communication, depending, again on the energy budget the nodes have upon departure from a waypoint.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".