A Flexible Transformer Q-Network for Energy-Aware WSN Scheduling
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) are finding more and more applications in various fields including environmental monitoring, healthcare, industrial automation as well as smart cities, because of the capability to sense, process, and transmit information in real time. WSNs have a number of challenges, which are related to its disadvantages, such as low energy capacity of sensor nodes, inconsistent delays in communication, and changing workloads. Task scheduling is also important in solving these predicaments as it maintains efficient sensing, computation, and communication task allocation without depleting network energy resources. Nonetheless, traditional scheduling strategies usually have difficulties with adapting to the dynamic environment, which results in the situation of premature loss of energy, unequal workload distribution, and shortened network lifetime. A Coupled Flexible Q-value Normalized Transformer Neural Network (CoupFQ-NTNN) is proposed to address the constraints and mitigate the drawbacks through energy-conscious task scheduling. This architecture combines reinforcement learning with Transformer architecture, with normalized Q-values that stabilize the decision-making process and a mechanism of attention that learns and memorizes the intricate relationships between tasks and nodes. Moreover, Cleaner Fish Optimization (CFO) is also used to optimize the scheduling decisions and reach a balance between global exploration and local exploitation. The proposed CoupFQNTNN framework has better performance, node energy consumption of 0.82 J, task completion rate of 99.8, latency of 12 ms, throughput of 98.7% and accuracy of 99.9%, and it is better in terms of performance compared to the existing task scheduling methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".