Scalable Dynamic Routing Protocol for Cognitive Radio Sensor Networks
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".