Distributed Facility Location for Sensor Network Maintenance
Bibliographic record
Abstract
The continuous growth of wireless sensor networks demands new approaches to efficiently manage and service them. We present an approximation solution to the facility location problem for sensor network maintenance based on static sensors and mobile facilities. The main goal is to increase the network lifetime by recharging or redeploying sensors with the help of mobile multi-purpose maintenance facilities. Our problem is a variant of the facility location problem (FLP). In our case, we need to find a suitable deployment of the facilities where their workload is balanced and the movement of the facilities in their areas is minimized. This should be accomplished keeping the number of sensor communications at minimum. While finding the optimal placement of the maintenance facilities is a NP-hard problem, we show a simple and efficient solution, totally distributed and localized, which starting with a balanced deployment, progresses to a final partition of remarkable quality. Such final partition satisfies the load balancing requirement and minimizes the facility travel times. The experimental analysis of our distributed and localized solution shows that sensor message cost remains low as the size of the network increases. The experiments also show a load distribution similar and sometimes better than centralized deployment solutions.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".