Secure Route Detection with Multi Level Trust Evaluation Model Using Replicated Auditor Node for Extended Packet Delivery Rate in WSN
Bibliographic record
Abstract
Wireless sensor networks (WSNs) are designed to monitor their surroundings, an application that has been leveraged significantly by advances in the Internet of Things (IoT).However, the susceptibility of wireless systems to errors and malicious attacks remains a challenge.Identity deception, particularly, has emerged as a significant issue, exacerbated by the redundancy of multi-hop routing, leading to potentially damaging attacks on routing protocols.Routing, the method employed in WSNs to disseminate data to base stations, has recently seen the incorporation of trust mechanisms to enhance security and foster cooperation among nodes.Routing decisions are made based on the anticipated trustworthiness of individual nodes.Considering the vulnerability of WSNs to various attacks, secure routing is of paramount importance.In this research, we propose a Multi-Level Trust Evaluation Model using Replicated Auditor Node (MLTEM-RAN) for secure route detection.This model aims at maximizing the packet delivery rate.It takes into account information about each relay node along the path, including the trust value and the current condition of each node.The trust value of a node is defined as the attack probability of the node, which is based on historical behaviors.The node's status, on the other hand, is a composite measure that considers both the node's remaining energy and its distance to the sink node.When compared with existing models, the proposed MLTEM-RAN model demonstrates superior performance in terms of packet delivery rate.This study thus represents a significant step forward in the development of secure and efficient routing strategies for WSNs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".