A low energy security-aware routing protocol based on grey clustering analysis in WSNs
Bibliographic record
Abstract
Abstract Wireless sensor networks (WSNs) comprise an extensive array of spatially dispersed sensor nodes, interconnected through a wireless medium to monitor and record physical information from the environment. In WSNs, multi-hop routing is employed for data transmission among nodes, rendering these networks susceptible to a diverse range of attacks. In order to mitigate these threats, proficient trust management schemes must be employed to ascertain node reliability and segregate malevolent nodes from the rest of the network. In recent years, trust-based routing protocols and multipath routing have become important ways to improve the security and performance of WSNs. This paper mainly introduces grey theory based on multipath and proposes a low energy security-aware routing protocol for WSNs based on grey clustering analysis (SPBCDS). Firstly, during the route establishment phase, the protocol employs a grey clustering analysis algorithm to classify the security status of each candidate cluster by conducting security analysis of the cluster area. Secondly, to ensure data privacy and enhance protocol fault tolerance, a dynamic slicing technique is introduced to select the set of candidate cluster heads, taking into account both the security status of the candidate clusters and the distance between cluster heads. Subsequently, the packets are sliced proportionally based on the security status level of the candidate clusters and directed to the corresponding candidate cluster head nodes. Experimental results demonstrate that this algorithm effectively reduces network energy consumption, extends the network lifecycle, and enhances overall network reliability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".