Mean-Field Artificial Noise Assistance and Uplink Power Control in Covert IoT Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we study a covert Internet of Things (IoT) system. Compared with conventional IoT systems that apply cryptography and information-theoretic secrecy approaches to secure the transmission, our considered IoT system adopts the covertness technique and intends to hide the legitimate transmission from the observant adversaries. In the IoT system, the IoT devices randomly transmit the collected data to their associated IoT gateways (GWs). In the meantime, the adversaries attempt to detect the existence of legitimate transmission based on their received signal power and launch hostile attacks accordingly. To avoid being detected by the adversaries, the IoT system applies uplink power control to achieve covert legitimate transmission. Moreover, to distort the observation of the adversaries so as to mislead their decisions, we propose an artificial noise (AN)-assisted covert communication design, where the AN is transmitted by in-band full-duplex (IBFD) IoT GWs as a jamming operation. We formulate a Stackelberg game to study the interaction between the adversaries and the legitimate entities including the IoT GWs and IoT devices, where the legitimate entities, as the leaders, decide on the powers of legitimate and AN transmissions at the upper level and the adversaries, as the followers, aim to minimize their detection errors at the lower level. Thereafter, considering the large scale of IoT system, we further cast the Stackelberg game into a mean-field Stackelberg game and incorporate the stochastic geometry and statistical channel model to capture the location heterogeneity and channel dynamics among and of the system entities, respectively. In the performance evaluation, we verify the practicability of the mean-field Stackelberg game. Moreover, we demonstrate the effectiveness of AN in improving the transmission covertness.
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.
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 | 0.001 | 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 it