Energy Provisioning in Green Mesh Networks Using Positional Awareness
Bibliographic record
Abstract
Untethered vehicular roadside infrastructure will eventually be deployed using various energy sustainable designs. In the solar-powered case, cost-effective nodes must be provisioned with a combination of a solar panel and a battery, which is sufficient to prevent future node outage. During the network design phase, a bandwidth usage profile (BUP) is assumed, and historical solar insolation traces are used to determine the minimum-cost provisioning that is required for each network node. In practical systems, however, there are usually restrictions in the way that the nodes can be positioned, and this results in a time-varying and node-dependent attenuation of the available solar energy. Unfortunately, conventional resource provisioning methods cannot take this into account; therefore, the deployed system may be unnecessarily expensive. In this paper, the resource provisioning problem is considered from this point of view. We first review conventional resource provisioning mechanisms and give an example that shows the value of introducing positional solar insolation awareness. A provisioning algorithm is then introduced that takes known positional variations into consideration when performing the energy provisioning. A variety of results are then presented, which show that reductions in total network provisioning cost can be obtained using the proposed methodology, compared with conventional algorithms. The proposed algorithm also performs very well compared with a linear programming formulation, which gives lower bounds on the node resource provisioning cost assignments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".