Optimizing Energy Efficiency in Wireless Sensor Networks via Cluster-Based Routing and a Hybrid Optimization Approach
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) are increasingly deployed to survey various environmental conditions, finding applications across domains such as agriculture, healthcare, and environmental monitoring.The sensors within these networks are tasked with collecting data and transmitting it to a central sink node through wireless means.Given that the sensor nodes operate on battery power and are often situated in remote locations where maintenance poses logistical challenges, energy conservation emerges as a critical issue.This study introduces an Energy-based Multiobjective Hybrid Optimization Algorithm (E-MHOA), designed to optimize cluster-based routing protocols to enhance energy efficiency in WSNs.The proposed E-MHOA integrates the Cuckoo Search Algorithm (CSA) with the Whale Optimization Algorithm (WOA) to judiciously select Cluster Heads based on their residual energy levels.The primary focus of the E-MHOA is to facilitate improved energy efficiency and data delivery within the context of agricultural monitoring applications.An array of performance metrics, including energy efficiency, End-to-End Delay (EED), packet drop, and network throughput, were employed to evaluate the efficacy of the E-MHOA.Comparative analyses were conducted against existing methodologies, such as MWCSGA, PAwCOR, and CEELBRP.The results of the simulation show that the E-MHOA approach performs noticeably better than the MWCSGA in terms of energy efficiency, achieving a notable efficiency rate of 98.09% in networks comprising 100 nodes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".