ERCD: An energy-efficient clone detection protocol in WSNs
Bibliographic record
Abstract
Wireless sensor networks (WSNs) play an increasing role in a wide variety of applications ranging from hostile environment monitoring to telemedicine services. The hardware and cost constraints of sensor nodes, however, make sensors prone to clone attacks and pose great challenges in the design and deployment of an energy-efficient WSN. In this paper, we propose a location-aware clone detection protocol, which guarantees successful clone attack detection and has little negative impact on the network lifetime. Specifically, we utilize the location information of sensors and randomly select witness nodes located in a ring area to verify the privacy of sensors and to detect clone attacks. The ring structure facilitates energy efficient data forwarding along the path towards the witnesses and the sink, and the traffic load is distributed across the network, which improves the network lifetime significantly. Theoretical analysis and simulation results demonstrate that the proposed protocol can approach 100% clone detection probability with trustful witnesses. We further extend the work by studying the clone detection performance with untrustful witnesses and show that the clone detection probability still approaches 98% when 10% of witnesses are compromised. Moreover, our proposed protocol can significantly improve the network lifetime, compared with the existing approach.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".