Defending Against Randomly Located Eavesdroppers by Establishing a Protecting Region
Bibliographic record
Abstract
The security problem in wireless sensor networks faces severe challenges, due to the openness of the sensor network channel and the mobility and diversity of the terminals. When facing randomly located eavesdroppers, the situation is much more complex. This paper studies the security performance of a wireless sensor network where randomly located passive and active eavesdroppers are both considered. Compared to a passive eavesdropper, an active eavesdropper can perform both eavesdropping and malicious jamming simultaneously in a wireless sensor network. Based on beamforming and artificial noise (AN), we propose a practical way to defend against the eavesdropper by establishing a protecting region. An appropriate metric, the hybrid outage probability, which takes both the transmission outage probability and the secrecy outage probability into consideration, is utilized to evaluate the security performance. In addition, the concept of safe transmission range is defined to evaluate the security performance. Simulation results are provided to depict the insecure region and verify the harm of the active eavesdropper to the transmission in the wireless sensor network.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".