A Novel Solution for Achieving Anonymity in Wireless Ad Hoc Routing Protocol
Bibliographic record
Abstract
A mobile ad hoc network consists of mobile nodes that can move freely in an open environment. Communicating nodes in a wireless and mobile ad hoc network usually seek the help of other intermediate nodes to establish communication channels. In such an open environment, malicious intermediate nodes can be a threat to the security and/or anonymity of the exchanged data between the mobile nodes. While data encryption can protect the content exchanged between nodes, routing information may reveal valuable information about end users and their relationships. The main purposes of this paper are to study the possibility of achieving anonymity in ad hoc networks, and propose an anonymous routing protocol, similar to onion routing concept used in wired networks. Our protocol includes a mechanism to establish a trust among mobile nodes while avoiding untrustworthy nodes during the route discovery process. The major objective of our protocol is to allow only trustworthy intermediate nodes to participate in the routing protocol without jeopardizing the anonymity of the communicating nodes. We present our scheme, and report on its performance using an extensive set of simulation set of experiments using ns-2 simulator. Our results indicate clearly that anonymity can be achieved in mobile ad hoc networks, and the additional overhead of our scheme to DSR is reasonably low when compared to a nonsecure DSR ad hoc routing protocol.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".