Optimal Wireless Sensor Network Ant-Lifetime Routing Algorithm Using Multi-Phase Pheromone
Bibliographic record
Abstract
The research introduces a Pheromone-based Ant Trusted Routing Algorithm (PATRA), aimed at improving routing efficiency and security in Wireless Sensor Networks (WSN).The approach will combine Ant-Colony Optimization (ACO) with reputation-based mechanisms to ensure trusted data delivery through the selection of more trustworthy and energy-efficient nodes.Packet Delivery Ratio (PDR), Energy Consumption, Packet Loss Rate, and the number of received packets are considered for the performance metrics that are observed through extensive simulations with a range of environments, including the possibility of malicious nodes.The results indicate that PATRA consistently outperforms conventional approaches like Quality of Service -Particle Swarm Optimization (QOS-PSO), Ant Colony Optimization Routing Control (ACORC), and Trust-Aware Node Activity Routing Protocol (TANARP) by maintaining a high PDR, reduced energy consumption, and lower packet loss rates with a maximization of received packets.These further demonstrate that PATRA possesses robustness regarding the impact of malicious nodes and network lifetime.The simulation experiments also confirm that the proposed approach outperforms the previous approaches by a large margin in security, efficiency, and reliability, and is thus a promising approach to be employed for secure and energy-efficient 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| 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".