Effective Location Management of Mobile Actors in Wireless Sensor and Actor Networks
Bibliographic record
Abstract
Abstract—Recent years have witnessed an increasing availability of heterogeneous sensor networks that consist of a large number of resource constrained nodes (sensors) and a small number of powerful resource rich nodes (actors). Such heterogeneous Wireless Sensor Actor Network (WSANs) offer improvement of sensor networks' capacity/coverage, energy conservation and network lifetime. This paper investigates the case where sensors are organized into clusters and mobile actors are used for maintaining an energy efficient topology by periodically manipulating their geographical position. We present an elegant technique that allows actor nodes to find an optimal geographical location with respect to their associated cluster heads such that the overall energy consumed is minimized. The proposed technique includes a weighted cost function based on the residual energy levels of cluster heads that allows the mobile actor to optimally fine-tune its geographical location. We present simulation results that demonstrate a significant increase of network lifetime over the traditional cluster based WSN deployments. Index Terms—wireless sensor actor networks, mobile actors, clustering, energy efficiency, network lifetime. I.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".