WSN Coverage Hole Detection Animation
Find wireless sensor network blind spots with sensing circles, nearest-sensor cells, and Voronoi-style gap candidates.
WSN coverage hole detection
Inspect a sensor field the way a deployment tool would: draw sensing disks, partition the field by nearest sensor, test Voronoi-style candidate points, then patch the largest blind spot.
Coverage field
Start with deployed sensors and their sensing circles. Red cells show sampled points that no active sensor covers.
--
--
--
--
Read the monitored field, active nodes, failures, and sensing radius.
Each colored cell belongs to the closest active sensor.
Voronoi-style vertices and boundary points reveal likely blind spots.
Sampled cells estimate how much area is covered and where holes remain.
Add, move, or reactivate sensors where the model finds the largest gap.
The map estimates holes by counting active sensing disks at each sampled point.
If a Voronoi candidate is farther than the sensing radius, the nearby cells contain a blind spot.
A bounded field can have a corner hole even when interior Voronoi vertices look safe.
Move or add sensors near the worst uncovered point, then rerun the scan and failure test.
Technical support
Use these notes to connect the animation with WSN coverage theory, units, and deployment limits.
Technical accuracy notes
The animation treats detection as perfect inside radius R and absent outside R. Real sensors may be directional, noisy, blocked, or terrain-limited.
Coverage percent is computed on a finite grid of points. A finer scan is better, but still not a proof of continuous coverage.
Delaunay triangle circumcenters approximate the interior Voronoi vertices. A candidate farther than R from every active sensor indicates an interior gap risk.
Rectangular boundaries introduce corner and edge candidates that are not captured by interior Voronoi vertices alone.
Coverage holes always mean degree 0. Target k coverage asks whether each sample has at least k active sensors, which is a redundancy requirement.
The random-placement estimate ignores boundaries and exact placement. Use it as a rough benchmark, not as deployment proof.
Formulas and units
Waiting for model update...
Detection algorithm
| Stage | What the animation does | Why it matters |
|---|---|---|
| 1. Active sensor set | Remove failed nodes and draw each remaining sensing disk. | A dead node contributes no coverage even if it still appears in the deployment plan. |
| 2. Nearest-sensor cells | Color sample points by the nearest active sensor. | Nearest cells make the responsibility region for each sensor visible. |
| 3. Delaunay and Voronoi candidates | Use Delaunay triangles and circumcenters to mark interior Voronoi-style vertices. | Large empty regions usually appear at points far from three neighboring sensors. |
| 4. Boundary scan | Check corners and edge samples in addition to interior candidates. | Deployment borders are common hole locations because sensing circles extend outside the monitored area. |
| 5. Repair | Add a sensor or pull the nearest sensor toward the largest positive gap. | After a patch, the whole scan must be rerun because nearby cells and candidate points change. |
Remediation choices
Best when the hole is large, the budget allows more nodes, and connectivity can reach the new position.
Best when one nearby sensor has redundant overlap and can be shifted without opening a new hole.
Useful in a model, but real sensors may need more power, better calibration, or a different modality.
Best for duty-cycled WSNs where spare nodes already exist near the gap.
Use k=2 or k=3 for safety, noisy sensing, or deployments where one failed node cannot be allowed to create a blind spot.
Run a real survey after installation; geometry cannot capture obstacles, soil, enclosure, or environmental drift.
Practice prompts
Choose the boundary preset. Patch the largest hole once. Did the remaining risk move to a corner or the interior?
Use the node failure preset. Clear failures, then increase failed sensors one at a time and watch when k=1 coverage breaks.
Set target k to 2. Compare the sampled target coverage with the hole area. Why can holes be zero while k=2 still fails?