Reconfigurable Intelligent Surface-Assisted Secure Mobile Edge Computing Networks
Bibliographic record
Abstract
Mobile edge computing (MEC) has been recognized as a viable technology to satisfy low-delay computation requirements for resource-constrained Internet of things (IoT) devices. Nevertheless, the broadcast feature of wireless electromagnetic communications may lead to the security threats to IoT devices. In order to enhance the task offloading security, this paper proposes a reconfigurable intelligent surface (RIS)-assisted secure MEC network framework. Furthermore, we investigate the max-min computation efficiency problem under the secure computation rate requirements, by jointly optimizing the local computing frequencies and transmission power of IoT devices, time-slot assignment, and phase beamforming of the RIS. To solve the formulated non-convex problem, we further develop an iterative algorithm, in which the Dinkelbach-type method and block coordinate descent (BCD) technique are utilized to tackle the fractional objective function and coupled optimization variables, respectively. In particular, the successive convex approximation (SCA) and penalty function-based methods are exploited to solve the transmit power control and reflecting beamforming optimization subproblems, respectively, and the closed-form expression for local computing frequencies optimization subproblem is derived. Numerical results quantify the performance gain achieved by the proposed RIS-assisted secure MEC networks, when compared to existing benchmark methods.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".