Message trust‐based secure multipath routing protocol for opportunistic networks
Bibliographic record
Abstract
Summary Opportunistic networks (OppNets) are composed of wireless nodes opportunistically communicating with each other. These networks are designed to operate in a challenging environment characterized by high delay, intermittent connectivity, and no guarantee of fixed path between the sender and the destination nodes. One of the most vital issues in designing and maintaining practical networks over a time period is the security of the messages flowing in OppNets. This paper proposes a new method called message trust‐based secure multipath routing protocol (MT‐SMRP) for opportunistic networks. Various routing protocols such as ProPHet, Epidemic, and HiBOp, to name a few, have been proposed for OppNets, but none of these have applied a secure multipath routing technique. The proposed MT‐SMRP scheme relays the message to the destination through the disjoint paths, each applying a soft‐encryption technique to prevent message fabrication attacks. Simulations are conducted using the Haggle Infocom'06 real mobility data traces, showing that when time‐to‐live is varied, (1) the proposed MT‐SMRP scheme outperforms D‐MUST by 18.10%, 7.55%, 3.275%, respectively, in terms of delivery probability, messages dropped, and average latency; (2) it also outperforms SHBPR by 21.30%, 7.44%, and 4.85%, respectively, in terms of delivery probability, messages dropped, and average latency. Under similar performance metrics, the performance of MT‐SMRP is also shown to be better than that of D‐MUST and SHBPR when the buffer size (respondents. the message generation interval) is varied.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".