Effects of Correlated Shadowing Modeling on Performance Evaluation of Wireless Sensor Networks
Bibliographic record
Abstract
Wireless links are typically modeled in isolation as independent and parallel links. However, in reality, there is often a correlation in defects and impairments between collocated links. The spatial correlation of shadowing among proximate wireless links has been frequently observed. Although different types of correlated shadowing models have been proposed for wireless channels, these models are not present in popular network simulators. Ignoring such correlations results in the diversity of adjacent links being over- estimated. Our hypothesis is that this could lead to incorrect results when evaluating the performance of wireless systems and network protocols. In this paper, the ns-3 simulation environment has been enhanced with a prototype correlated shadowing model developed in our previously published work. We study the end-to-end packet delivery ratio and delay in wireless sensor networks using both correlated and independent propagation models. Simulation results show that correlated and independent shadowing models produce significant differences in network performance. Moreover, we compare the complexity of different modeling options. It is shown that this correlated propagation model in ns-3 can improve simulation realism without inducing any considerable degradation of scalability.
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".