Resource Management for Cognitive IoT Systems With RF Energy Harvesting in Smart Cities
Bibliographic record
Abstract
The number of Internet of Things (IoT) nodes is increasing in modern cities which demands spectrum and energy efficiency. Fifth-generation (5G) networks are considered as a key paradigm for the realization of future IoT applications. Particularly, cognitive radio and non-orthogonal multiple access are candidate technologies for 5G networks that can improve spectral efficiency and accommodate a large number of IoT devices. Furthermore, radio frequency (RF) energy harvesting can increase the energy efficiency of IoT networks. In this paper, we propose a resource management scheme for cognitive IoT network with RF energy harvesting in 5G networks. The objective is to maximize the throughput while assuring quality-of-service requirements in terms of data rate and minimum residual energy constraint on each IoT node. We use mixed integer linear programming and greedy approaches to solve the optimization problem. We then present the simulation results of the proposed scheme to exhibit the significant positive impact on the performance of the IoT network.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".