NIS02-2: A Secure Routing Protocol for Heterogeneous Sensor Networks
Bibliographic record
Abstract
Sensor networks are envisioned to have important applications in military and homeland security. For sensor networks deployed in such hostile environments, security is critical to ensure privacy, integrity, authenticity, and availability of communications. Routing is a fundamental operation in sensor networks. Past researches on sensor network routing focused on efficiency and effectiveness of data dissemination. Few of them considered security during the design phase of the routing protocols. Furthermore, previous researches on sensor networks mainly considered homogeneous sensor networks, i.e., all sensor nodes are the same. Research has shown that homogeneous ad hoc networks have poor performance. We adopt a heterogeneous sensor network (HSN) model for better performance and security. In this paper, we present an efficient secure routing protocol for HSN which takes advantage of the powerful high-end sensors. The security analysis demonstrates that the secure routing protocol can defend typical routing attacks. The simulation shows that the secure routing protocol has better performance than a popular routing protocol - directed diffusion.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".