An Energy-Efficient and Secure WSN Routing Protocol Using Bayesian Networks and Elitist Genetic Algorithms
Bibliographic record
Abstract
The burgeoning demand for wireless networking and its associated applications has spurred academic endeavors to devise more efficient routing protocols.Wireless sensor networks (WSNs) operating on battery power, grapple with constraints such as quality of services (QoS) issues, energy dissipation, processing overhead, and link failures.Preserving the QoS is paramount for WSNs, as it directly impacts data transmission and overall network performance, rendering them unsuitable for real-time applications.So, this paper introduces a secure energy-efficient optimal routing framework.It is designed using Bayesian network and an Elicit Genetic Algorithm (EGA).The proposed model goal is to mitigate routing issues, enhance the QoS, and optimize energy efficiency.Path selection involves learning information about node/network connectivity and availability, enabling the derivation of disjoint paths without shared communication components.The simulation is done using the network simulator 2 tool to demonstrates the proposed routing protocol effectively facilitates communication by elevating quality of services, reducing energy consumption, and ensuring suitability for real-time applications.The evaluation of network communication effectiveness employs performance parameters such as end-toend delay, throughput, energy dissipation, and packet loss, highlighting the robustness and efficiency of the proposed framework.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".