Dynamic Task Allocation in Healthcare Edge Computing Leveraging Multi-Objective Deep Q-Learning
Bibliographic record
Abstract
The rapid proliferation of smart medical sensors and wearable healthcare devices has significantly increased the demand for low-latency computing services. The data generated by these monitoring devices must be processed efficiently by edge computing systems. However, the dynamic and unpredictable nature of incoming computational tasks requires real-time, robust, and priority-sensitive task allocation mechanisms. This study introduces an on-line task allocation algorithm for edge computing in healthcare systems, leveraging Deep Q-Learning to address these challenges. A multi-objective optimization framework is formulated, enabling the task allocator to enhance system efficiency without requiring prior knowledge of future tasks. The algorithm maximizes the number of allocated tasks while prioritizing critical ones and minimizing counterproductive operation times. A deep Q-Network is implemented to estimate Q-values for each state-action pair, empowering the task allocator to select actions with the highest Q-value consistently. The performance of the proposed algorithm is rigorously evaluated in a simulated environment. The results show that the algorithm effectively learns an optimal policy for the task allocation problem, producing an average improvement of 74.6% in the overall performance of the system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".