Energy-Efficient IoT Network Routing Model Based on Multi-Layer Clustering and Deep Learning
Bibliographic record
Abstract
The increasing numbers of sensors or actuators in the Internet of Things (IoT) networks make it essential to develop smart routing solutions whose goals are to extend the network's lifetime and keep reliable communication.In this paper, we present a new hybrid routing model based on multi-layer hierarchical clustering with the hybrid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model to find the most reasonable routing paths in a dynamic IoT environment.The CNN-GRU model utilizes a sequential sliding window of historical node attributes residual energy, hop count, and data rate for predicting the best energy-efficient next-hop node as closely as possible in pipeline.This predictability allows the protocol to rely less on static heuristics, and to adapt its behavior to varying network conditions.Observations from experiments also illustrate that our model achieves superior performance over other baseline protocols in terms of network lifetime of 134 rounds the first node death (FND) following 202 rounds the half-node death (HND), energy consumption of 0.041 J/round, packet delivery ratio of 96.8%, end-to-end delay of 244 ms, and the routing overhead of 28cp/round.These findings demonstrate that the proposed Multi-Layer Clustering with CNN-GRU model can enhance the energy efficiency, reliability, and scalability of IoT networks.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".