K Barrier Prediction Eval

---
name: k-barrier-prediction-eval
description: Evaluates a model's ability to predict the number of k-barriers for intrusion detection in wireless sensor networks deployed over circular regions, based on geometric and deployment parameters. Use when the user wants to benchmark on WSN k-barrier simulation dataset, or asks about evaluating this task. Reports RMSE.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2208.11887
  bibtex_key: singh2022deep
  confidence: high
---

# k-barrier-prediction-eval

> A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks — Abhilash Singh et al. (2022) (arXiv:2208.11887, 2022)

## What this evaluates

Evaluates a model's ability to predict the number of k-barriers for intrusion detection in wireless sensor networks deployed over circular regions, based on geometric and deployment parameters.

## Datasets

- **WSN k-barrier simulation dataset** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `R` — range: [-1, 1]
  - Pearson correlation coefficient between predicted and observed barrier counts. Higher values indicate better alignment with observed values.
- `RMSE` **(primary)** — range: other
  - Root Mean Squared Error: sqrt(mean((y_pred - y_true)^2)). Lower values indicate higher accuracy.
- `bias` — range: other
  - Mean prediction error: mean(y_pred - y_true). Positive values indicate overestimation, negative values indicate underestimation.

## Input / output format

**Input**: Four numerical features: area of the circular region, sensor sensing range, transmission range, and total sensor count.

**Output**: A single continuous float representing the predicted number of k-barriers.

## Scoring recipe

```python
import numpy as np
from scipy.stats import pearsonr
def compute_metrics(y_true, y_pred):
    rmse = np.sqrt(np.mean((y_pred - y_true) ** 2))
    bias = np.mean(y_pred - y_true)
    r, _ = pearsonr(y_true, y_pred)
    return {'R': r, 'RMSE': rmse, 'bias': bias}
```

## Common pitfalls

- The paper evaluates performance on training, validation, test, and combined datasets separately; reporting only combined accuracy masks generalization performance.
- Bias sign convention is explicitly defined: positive bias means overestimation, negative means underestimation, which is opposite to some ML frameworks that define bias as y_true - y_pred.
- Error distribution is noted to be slightly right-skewed, meaning mean bias may not fully capture model performance compared to median error or RMSE.

## Evidence (verbatim from paper)

> We have used R, RMSE, and bias as the performance metrics. A high value of R represents that the predicted values are well in accord with the observed value. A low value of RMSE represents a more accurate model. A positive value of bias shows overestimation, and a negative value of bias shows underestimation.

## Citation

```bibtex
@misc{singh2022deep,
  title={A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks},
  author={Abhilash Singh et al. (2022)},
  year={2022},
  note={arXiv:2208.11887}
}
```

- arXiv: 2208.11887