A master-sink based model for on-site tracking of multiple mobile targets in wireless sensor networks
Bibliographic record
Abstract
The problem of on-site tracking of a mobile target in wireless sensor networks was introduced recently by B.S. Malhotra et al. (2004). The on-site tracking requires the physical presence of the sink in the area of interest. This paper generalizes the on-site tracking problem for multiple mobile targets and then proposes an energy efficient solution to solve it. The solution is an extension of the ant based method-proposed by B.S. Malhotra et al. for on-site tracking of a single target. The introduction of multiple tracking units (sinks) requires coordination among themselves for efficient tracking. This coordination among the sinks in our approach is achieved through a master sink. We conducted a simulation study and compared our approach with TTDD (H. Luo et al., 2002) and SPC (B.S. Malhotra et al., 2004) for the on-site tracking of multiple targets. Our approach is simple and the simulation results show that it is energy efficient.
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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 | 0.001 | 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".