Reliable Intelligent Reflecting Surface-Assisted Mobile Edge Computing Systems: A Physical Layer Security and Encryption Design
Bibliographic record
Abstract
Mobile edge computing (MEC) has emerged as a promising technology to extend the functionality of end-users' wireless devices while prolonging their battery life by offloading computationally intensive tasks to remote edge servers. However, the inherent broadcast nature of wireless transmission during offloading introduces notable security challenges. To address this issue, we propose leveraging intelligent reflecting surface (IRS) technology to enhance physical layer security (PLS). Nevertheless, attaining high PLS for all users in dense networks with multiple malicious terminals is challenging. In this paper, we investigate the physical layer encryption (PLE) to complement the PLS in enabling secure wireless transmission. Since such encryption and decryption processes require computation resources, we aim to optimize the encryption decision, offloading decision, as well as wireless and computing resource allocations. Our objective is to minimize the maximum weighted energy consumption while satisfying practical constraints, including limited computing and wireless resources, fulfilling minimum user rate requirements, and complying with IRS conditions. To tackle the non-convex objective and constraints, we explore the utilization of bisection search and successive convex approximation (SCA) methods. Our numerical results confirm the efficiency of the proposed design in terms of energy consumption and network capacity within a secure MEC network.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".