AoI-Based Sensor Selection for Target Tracking in Asynchronous Sensor Networks
Bibliographic record
Abstract
Due to communication delays and other limitations of wireless sensor networks (WSNs), asynchronous sensor selection is necessary for target tracking. However, not much work has been formulated for this problem. Inspired by the concept of data freshness, this article applies age of information (AoI) to sensor selection for target tracking in the presence of random communication delays. In this regard, we formulate several AoI-based selection designs to measure the value of asynchronous measurements. The first formulation minimizes the time-average AoI to achieve enhanced performance, the second formulation attempts to set an AoI deadline constraint, and the third formulation penalizes the updated delay using an AoI-based penalty function. These AoI-based formulations are applied to target tracking by combining AoI with mutual information (MI) for asynchronous sensor selection. The proposed formulations are then solved by the nondominated sorting genetic algorithm (NSGA-II) and the greedy approach. Finally, the selected sensors are fused by the asynchronous fusion approach. Simulation results validate the effectiveness of the proposed asynchronous sensor selection methods compared with traditional approaches.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".