<title>Collaborative sensor management for decentralized asynchronous sensor networks</title>
Bibliographic record
Abstract
In this paper, we consider the problem of sensor resource management in decentralized tracking systems with asynchronous communication and sensor selection. Due to the availability of cheap sensors, it is possible to deploy a large number of sensors and use them to monitor a large surveillance region. Even though a large number of sensors are available, due to frequency, power and other physical limitations, only a maximum of certain number of sensors can be used by any fusion center at any one time. The problem is then to select the sensor subsets that should be used at each sampling time in order to optimize the tracking performance under the given constraints. In recent papers, we proposed algorithms to handle the above problem in centralized, distributed and decentralized architectures. However, in the paper for sensor subset selection for decentralized architecture, we assumed that all the fusion centers change their sensors at the same time, and their sensor change time interval is fixed and known. However, in general case, fusion centers may change their sensors at different time, and their sensor change intervals may not be fixed. In this case, the sensor management become more difficult. We have to decide when to change the subsets, and how to incorporate the changes made in the neighboring fusion centers in selecting the future sensor subsets. We propose an efficient algorithm to handle the above problem in real time. Simulation results illustrating the performance of the proposed algorithm are also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".