Reliable multiple robot-assisted sensor relocation using multi-objective optimization
Bibliographic record
Abstract
Wireless sensor networks provide a way to monitor a region of interest. Incorporating a robot into the sensor network provides a basis for other types of functionality to be added. One possibility is the replacement of damaged sensors with excess sensors within the wireless sensor network. This scenario has been defined as the “Robot-Assisted ¡Sensor Relocation” (RASR) problem and focused only on minimizing the length of the trajectory taken by the robot. RASR has been recently expanded on as a multi-objective optimization (MOO) problem to examine a more realistic scenario by considering the reliability and placement location of the passive sensors used for replacement; this new problem is termed “Reliable Robot-Assisted Sensor Relocation (RRASR). In this paper, the possibility of multiple robots servicing the sensor network is considered and the RRASR problem formulation is modified accordingly. In addition, load balancing of robots by adding an objective function to the MOO representation is included. We refer to this multi-robot version as Reliable Multiple Robot-Assisted Sensor Relocation. The performance of six state-of-the-art evolutionary MOO algorithms using sensor networks of varying sizes and inflicted damage levels is examined.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".