Deep Reinforcement Learning‐Driven Cooperative Routing for Energy Efficiency in Wireless Multimedia Sensor Networks
Bibliographic record
Abstract
ABSTRACT The rapidly evolving landscape of cooperative routing protocols in network systems necessitates innovative approaches to address their inherent challenges, such as energy depletion, scalability limitations, QoS maintenance difficulties, and dynamic topology changes. This research introduces a deep reinforcement learning (DRL) framework designed to enhance the efficiency and effectiveness of cooperative routing protocols. A relay‐based routing mechanism is developed using the proposed enhanced deep reinforcement learning (EnDRL) technique. In this approach, the DRL framework leverages the adaptive kookaburra optimization (AdKo) algorithm to select the optimal action, significantly improving routing efficiency. The AdKo algorithm is formulated to select the optimal action by incorporating adaptive concepts into the traditional kookaburra optimization algorithm. This adaptation involves dynamically adjusting weights to improve the convergence rate and mitigate the risk of local optimal trapping, thereby enhancing the overall performance of the optimization process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 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".