Scalable Dynamic Routing Protocol for Cognitive Radio Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
Wireless sensor networks (WSNs) have been increasingly considered an attractive solution for a plethora of applications. The low cost of sensor nodes provides a mean to deploy large sensor arrays in a variety of applications, such as civilian and environmental monitoring. Most of the WSNs operate in unlicensed spectrum bands, which have become overcrowded. As the number of the nodes that join the network increases, the need for energy-efficient, resource-constrained, and spectrum-efficient protocol also increases. Incorporating cognitive radio capability in sensor networks yields a promising networking paradigm, also known as cognitive radio sensor networks. In this paper, a cognitive networking with opportunistic routing protocol for WSNs is introduced. The objective of the proposed protocol is to improve the network performance after increasing network scalability. The performance of the proposed protocol is evaluated through simulations. An accurate channel model is built to evaluate the signal strength in different areas of a complex indoor environment. Then, a discrete event simulator is applied to examine the performance of the proposed protocol in comparison with two other routing protocols. Simulation results show that when comparing with other common routing protocols, the proposed protocol performs better with respect to throughput, packet delay, and total energy consumption.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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