Joint node selection, flow routing, and cell coverage optimisation for network sum‐rate maximisation in wireless sensor networks
Bibliographic record
Abstract
In this study, the problems of joint node selection, flow routing, and cell coverage optimisation in energy‐constrained wireless sensor networks (WSNs) are considered. Due to the energy constraints on network nodes, maximising network sum‐rate under target network lifetime, flow routing, cell coverage, and minimum rate constraints is of paramount importance in WSNs. To this end, a mixed‐integer non‐linear programming problem is formulated, where the aim is to optimally select which network nodes to act as sensors or relays while ensuring connectivity to the fusion centre optimised network flows, and full network coverage. The formulated problem happens to be NP‐hard (i.e. computationally prohibitive). In turn, a solution procedure based on the branch and bound with the reformulation‐linearisation technique (BB‐RLT) is devised to provide a ‐optimal solution to the formulated problem. Simulation results are presented to validate the efficacy of the devised BB‐RLT solution procedure. This work provides significant theoretical results on network sum‐rate maximisation for WSNs under a variety of practical constraints.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".