ERCD: An energy-efficient clone detection protocol in WSNs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it