Energy Efficient On-Site Tracking of Mobile Target in Wireless Sensor Networks
Bibliographic record
Abstract
Tracking moving targets is one of the important problems of wireless sensor networks. Many approaches have been proposed to deal with this problem (Yang, H. and Sikdar, B., 2003; Gupta, R. and Das, S.R., 2003; Yu-Chee Tseng et al., 2003; Hegazy, T. and Vachtsevanos, G., 2004). Once the information about a moving target is received, subsequent actions are taken in most cases. Sometimes, a physical presence in the vicinity of the moving target is required to take these actions. We characterize this nature of tracking as the on-site tracking problem. Applying existing approaches to solve the on-site tracking problem normally requires excessive message communication and navigation strategies. We propose an ant-based approach which effectively exploits the mobility dependency property inherent in the system to solve the on-site tracking problem. Our approach is simple and 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 | 0.001 |
| 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".