Energy-Efficient Edge Intelligence in IoT Environment Using Cross-Layer Bio-Inspired Optimization with Deep Learning Framework
Bibliographic record
Abstract
The rapid rise of Internet of Things (IoT) applications has increased the demand for energy-efficient or computationally sustainable Wireless Sensor Networks (WSNs).This paper proposes a hybrid optimization Bio-Inspired Deep Learning and Edge-Cloud (BIO-DLEC) framework with the Whale Optimization Algorithm (WOA) and the Grey Wolf Optimizer (GWO) for energy-aware clustering and routing to address these challenges.The hybrid framework incorporates the exploration capability of WOA to diversify candidate solutions, and GWO exploits them, thus achieving a balance process.During the clustering stages, optimal cluster heads (CHs) are selected based on a multi-objective fitness function that ensures overall optimality from the use of residual energies, intra-cluster compactness, and load balancing.In the routing stage, energy-efficient routing paths are established by minimizing communication cost, hop count, and latency within a multi-hop topology.The experimental setup uses a hybrid NS-3 and iFogSim2 simulation environment.The BIO-DLEC improved overall network performance achieving a 25.5% longer lifetime, 21.8% less energy dissipation, 17.3% lower end-to-end latency, and a packet delivery ratio (PDR) higher than 95%.Overall, the results indicate the benefits of BIO-DLEC frameworks improved throughput reliability and enhanced sustainability for next-generation IoT-enabled WSNs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".