Comparing alternative cluster management approaches for mobile node tracking in a factory Wireless Sensor Network
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) deployed on a shop floor for factory automation are subject to dynamic and uncertain conditions present with heavy machinery. To overcome these obstacles, on one hand, WSNs should be reconfigurable and adaptable to changes in the shop floor and ambient condition; on the other hand, WSNs should be efficient in consuming the limited wireless network resources. To accomplish these goals, we propose two cluster-based architectures (i.e. static or dynamic clusters), to organize the overall shop floor into a set of tracking zones, each composed of a sink node and a set of closest corresponding anchor nodes. To manage the wireless nodes activities and inter and intra cluster communications, an agent-based technique is employed. To compare the architectures, we report on a set of experiments performed in JADE (Java Agent Development Environment). In these experiments, we compare two agent-based approaches (dynamic and static) for managing clusters of wireless sensor nodes in a distributed tracking system. The experimental results corroborate the efficiency of static clusters versus the robustness of the dynamic clusters.
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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 | 0.000 | 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".