Physical Layer Security-assisted Partial Computation Offloading in Mobile Edge Computing
Bibliographic record
Abstract
With mobile edge computing (MEC), resource-constrained Internet of Things (IoT) users can offload computation tasks to edge servers in proximity for processing, which can reduce the service latency and energy consumption. Due to the broadcast nature of wireless communications, sensitive information can be leaked during computation offloading, in presence of adversaries or eavesdroppers. This work aims to improve the latency, energy efficiency and security in computation offloading in dynamic MEC environments. Specifically, partial offloading strategy is considered and a physical layer security-assisted scheme is developed to achieve multiple objectives, including maximizing the number of completed tasks before their respective deadlines and minimizing energy consumption, while providing enhanced security in a long run. A Markov decision process (MDP) with discrete-continuous action spaces is formulated and a deep reinforcement learning (DRL) method named hybrid-action deep deterministic policy gradient (HDDPG) is proposed to optimize the offloading ratio, friendly jammer selection, and computing power allocation. Simulation results demonstrate that the proposed HDDPG outperform the state-of-art deep DRL baselines in terms of the number of completed tasks before deadlines and energy costs while satisfying the security requirements.
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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 | 0.000 |
| 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 | 0.000 | 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".