Wireless energy harvesting from ambient sources for cognitive networks in rural communities
Bibliographic record
Abstract
Cognitive Radio Networks (CRN) offer a solution to work with low interference in electromagnetically noisy environments such as in the proximity of TV/Radio stations or high voltage power lines in rural communities. However, the extra sensing requirement of CRN nodes results in high energy consumption. This lowers battery life, which is especially problematic in cognitive sensor networks. To counter this problem, in this paper, we propose a wireless energy harvesting (WEH) scheme to harvest RF energy from the ambience. We modify the popular energy efficient MAC protocol LEACH (Low Energy Adaptive Clustering Hierarchy) to incorporate energy harvesting. Our simulation results show RF energy harvesting is feasible only when the transmission power of the source is above 10 kW. This contrasts many papers that suggest to harvest energy from other low power wireless transmitters. Our work also shows that RF energy harvesting CRN can potentially benefit rural communities located within the high power radiation range.
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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 | 0.000 | 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 | 0.001 | 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".