Enhanced Task Offloading in Mobile Edge Computing: A Hybrid Approach Using Deep Q-Learning from Demonstrations and Heuristic Optimization
Bibliographic record
Abstract
The increasing demands of delay-sensitive and resource-intensive applications in the IoT era have made Mobile Edge Computing (MEC) a promising solution for task offloading to nearby edge servers. Effective offloading in MEC requires strategies that balance energy consumption and meet strict deadline constraints to support the limited battery life of IoT devices and ensure low latency. Traditional heuristic algorithms, like DECO, efficiently manage energy and deadlines through predefined rules, while reinforcement learning (RL) methods adapt dynamically in changing environments. This paper proposes a hybrid model, DQfD-DECO, which combines the advantages of DECO and Deep Q-learning from Demonstrations (DQfD). By leveraging demonstration-based learning, our approach enhances initial offloading decisions and refines them through adaptive reinforcement learning. Comparative performance analysis in dynamic MEC scenarios reveals that DQfD-DECO achieves a superior balance of energy efficiency and deadline satisfaction, outperforming traditional rule-based methods in adaptability to fluctuating conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".