Performance Evaluation of an Anonymous Routing Protocol using Mobile Agents for Wireless Ad hoc Networks
Bibliographic record
Abstract
While much recent research focuses only on providing routing services for ad hoc networks, very little work has been done towards achieving anonymity for wireless ad hoc and sensor networks. However, malicious nodes in a wireless and mobile ad hoc environment can jeopardize the security of the network if the issues of secure data exchange are not properly handled. Encryption cannot fully protect the data communicated between nodes, as routing information may expose the identities of the communicating nodes and put their relationships at risk. In this paper, we propose an efficient anonymous routing protocol that uses a mobile agent paradigm for wireless and mobile ad hoc networks. In our protocol, only trustworthy nodes are allowed to participate in communications and the misbehavior of malicious nodes is thus prevented effectively. We describe our protocol and provide its performance evaluation based on simulation experiments implemented in an ns-2 simulator. Compared to the SDAR protocol, our experimental results demonstrate that our scheme not only achieves the necessary anonymity in mobile ad hoc networks, but also provides more security with reasonably little additional overhead.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".