An Effective Topology Optimization Algorithm for Wireless Network with Parking Surveillance Sensors
Bibliographic record
Abstract
Magnetron-based wireless sensor networks (WSNs) have been widely applied for the parking space surveillance. In this paper, on the basis of genetic algorithm (GA), we will propose a solution for the network topology optimization based on a 2-tier tree-type WSN, which is an effective way of improving the overall performance of the WSN concerning parking surveillance. The proposed algorithm aims to assess the optimal placement and the quantity of the network access points (APs) when given the locations of the sensor nodes. The problem is formulated as an integer programming (IP) problem and it can be solved efficiently by using genetic algorithm (GA). Tested by a real WSN in a parking lot with about 200 parking spaces, the results illustrate that a placement of APs generated by the proposed algorithm is more effective, compared with one produced by an experienced engineer in a parking surveillance industry.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".