On the performance of overlaid wireless energy harvesting cognitive industrial sensor networks under jamming attacks
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
Two or more wireless sensor networks coexist in the same space while low energy consuming devices mobiles in a secondary network harvest ambient RF energy from transmissions by nearby active transmitters in the primary network. The channels are allocated to the primary network, while the overlaid secondary network can access the idle channel allocated to perform data transmission opportunistically and operate properly. In this paper, with the jammer implanted, we propose a novel solution in which we execute a deception strategy to exhaust the energy of the jammers. As a result, the energy constraint jammers will be challenging to achieve jamming attacks when the secondary transmitters (STs) transmit information. We formulate the problem first to tackle the issue; that is, we regard throughput optimization issues for ST under jamming attacks as a Markov decision process (MDP). Then, since the focus is mainly on the throughput of the secondary network, a learning algorithm is adopted to maximize it. Through the learning process, the STs can adapt to the dynamics of the primary network while executing proper actions to benefit the overall throughput online. Simulations validate the efficiency and the convergence of the algorithm we proposed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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 it