Managing Traffic Growth in Solar Powered Wireless Mesh Networks
Bibliographic record
Abstract
Wireless mesh nodes must often be upgraded so that the network can accommodate evolving user demands. When some of the nodes are operated using a sustainable solar energy source, these upgrades must take into account the cost of updating the node energy resource configurations. This is required so that the new network configuration can properly accommodate the updated energy workloads of the re-provisioned network. In this paper we study this problem in the context of sustainable energy mesh node provisioning costs. We derive a mixed integer linear programming (MILP) formulation which is used to optimize the costs of node resource upgrades. Using this result, a lower bound on the network upgrade cost is obtained. The paper then proposes the use of a genetic algorithm based methodology for determining practical cost-effective mesh node resource upgrading. Various examples are given using networks with random, mesh and tree topologies which show the value of the proposed mechanism. In particular we find that the genetic algorithm approach achieves results which are much better than those from an algorithm which uses local optimization. It also performs well compared to our derived lower bound.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".