Optimized node classification and channel pairing scheme for RF energy harvesting based cognitive radio sensor networks
Bibliographic record
Abstract
Spectral-efficiency and energy-efficiency are the key concerns for next-generation wireless networks. RF energy-harvesting is emerged as a prominent technology which self-empowers wireless nodes to achieve energy-efficiency. For spectral-efficiency, the opportunistic spectrum-access technology (by employing cognitive radios) is an optimal solution. Therefore, in this paper, we merge both technologies (cognitive radio & RF energy harvesting) together to achieve network-wide spectral and energy efficiency. A novel two-level residual-energy and channel-quality (capacity and idle-time) aware node-classification scheme is introduced for cluster-based cognitive radio sensor networks to select the best sensor nodes for reporting process. At first level, the nodes are classified as harvesting or transmitting nodes based on their residual energy. Later on, the best node-channel pairs are formed for transmitting nodes using Hungarian algorithm. In the second level of classification, only those nodes are selected for reporting, which can transmit reporting packet in the given duration on the allocated channel. Otherwise, the node is directed to perform energy harvesting task to achieve energy-balancing and avoid unsuccessful reporting. Simulation results demonstrate that the proposed scheme shows better performance gain in terms of successful reporting rate compared to existing node-classification schemes. Furthermore, we compare the proposed node-channel pairing scheme with greedy-pairing and random-pairing schemes and illustrate the performance gain in terms of successful reporting.
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 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 | 0.001 | 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 | 0.000 |
| 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".