Optimization of Watchdog Selection in Wireless Sensor Networks
Bibliographic record
Abstract
Application areas of wireless sensor networks (WSNs) are rapidly expanding these days. This also intensifies security concerns in extensively deployed WSNs. A watchdog system is one of the security enhancement methods. In such a system, a number of sensor nodes are selected as watchdogs that monitor their single hop neighbors. Thus, sensing operations lose resources to combat distrust. This letter develops models that optimize the selection of watchdogs in WSNs. It focuses on two major facts: 1) overlapping and 2) coverage. Overlapping occurs when a sensor node is monitored by multiple watchdogs. It causes additional consumption of resources. It is inevitable due to the propagation characteristics of wireless signals. The full coverage occurs when each sensor node in a WSN is either monitored by at least one watchdog or working as a watchdog. This letter presents three optimization models for watchdog selection in WSNs. It also evaluates the models through case studies for realistic WSN topologies. The presented models provide a better understanding of resource efficient watchdog deployment strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".