Joint Optimal Placement, Routing, and Flow Assignment in Wireless Sensor Networks for Structural Health Monitoring
Bibliographic record
Abstract
Sensor node placement optimization has a significant role in wireless sensor networks, especially in structural health monitoring. Since sensor node placement affects the routing, optimization should be Jointly done for the node placement and routing. The existing work separately optimizes the node placement and routing (by performing routing after carrying out the node placement). However, this approach does not guarantee the optimality of the overall solution. In this paper, joint optimization of sensor placement, routing, and flow assignment is introduced and solved using mixed integer programming modeling. Finding an optimal solution for this joint problem is too complex. Hence, a near-optimal solution is obtained using genetic algorithms with reduced complexity. In addition, a heuristic algorithm for joint routing and flow assignment with placement is proposed using the effective independence model, which optimizes the information quality and energy consumption for efficient communication. Lastly, results are presented in a nine-floor building to compare the three proposed algorithms with the heuristic algorithm by Li et al. The numerical results show the efficiency of the proposed algorithms and the tradeoff between the efficiency and the complexity.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".