WSN Target Tracking Demo

WSN Target Tracking Demo

Explore detection coverage, prediction wake zones, handoff, target loss, and recovery in a wireless sensor network

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Interactive wireless sensor network target tracking workbench with moving target, sensor detection ranges, prediction wake zone, handoff and recovery states, energy estimates, accuracy metrics, and mobile-safe reference material.
Animation WSN Tracking Energy-Aware

WSN Target Tracking Demo

Watch a moving target cross a wireless sensor network. Only nearby and predicted-next sensors wake up, so learners can compare detection accuracy, handoffs, recovery search, and energy savings.

100% Tracking accuracy
63% Energy saved
Handoff ready Tracker state
TryLoad Patrol loop, set Sensor nodes to 12, Detection radius to 80 m, and press Play.
ObserveActive detections, Prediction error, Handoff state, Tracking accuracy, and Energy index evolve as the target crosses 12 sensing zones.
ExplainOnly sensors near the predicted position wake, so an 80 m detection radius trades redundant observations for energy while handoff preserves track continuity.
Technical boundariesThe tracker uses ideal positions, circular detection, and a simple predictor; measurement noise distributions, clutter, identity swaps, communication delay, clock skew, terrain, packet loss, and multi-target association are omitted.
Colour keywsn identitycurrent / primaryreference / datasuccesscautionerror / failure

Sensors detect locally

A node can report the target only when distance to the target is within its detection radius.

Prediction saves energy

The tracker wakes nodes near the predicted next position instead of keeping the whole network on.

Handoff keeps continuity

When the target leaves one node and enters another, responsibility transfers to a new detector.

Recovery costs energy

If prediction fails, the network expands a search ring until a sensor detects the target again.

1

Sensor field

Place nodes and show coverage.

2

Detection range

Only nearby sensors can see the target.

3

Target motion

Follow the trajectory and trail.

4

Wake prediction

Wake sensors near the next position.

5

Handoff

Transfer tracking between nodes.

6

Recovery

Expand search after loss.

Sensor field Start by checking node placement and coverage before the target moves.

Sensor field

Start by checking node placement and coverage before the target moves.

Stage 1 of 6
Wireless sensor network target tracking animation A sensor field with detection ranges, predicted target position, wake zone, active sensors, handoff links, search ring, and tracking metrics. Patrol tracking field Prediction wakes only the likely next sensors.
What changed Nodes form the sensing field.

Coverage depends on node count, placement, and sensing radius.

Diagnosis Ready to track

The default network has overlapping coverage for a continuous track.

Result --

Use Play or Step to advance the target.

Controls

Choose a tracking scenario, then adjust coverage, speed, and prediction lead time.

Scenario
Network tuning

Calculation Trace

This is a teaching model for detection and energy-aware wake-up. A production tracker may use RSSI, time difference, Bayesian filters, or multi-target data association.

detected_i = distance(target, sensor_i) <= radius accuracy = detected_frames / total_frames Detection is modeled as an ideal circular range so learners can see the geometry clearly.
predicted_pos = target_pos + velocity * lead_time awake_i = distance(predicted_pos, sensor_i) <= wake_radius Prediction wakes a small neighborhood before the target arrives.
energy_saved = 1 - average_awake_nodes / total_nodes recovery_radius grows while target is lost The energy number compares predictive wake-up to an always-on network.

Reference Material

Use these cards to connect the visual model to real wireless sensor network tracking design.

Quick Reference
Sensor node A battery-powered device that can sense locally and send a tracking report to the network.
Detection radius The idealized distance within which this demo treats a target as detectable by a node.
Wake zone A predicted neighborhood of nodes that should stay awake for the next target position.
Handoff A change in tracking responsibility from one detecting node to another.
Lost frame A time step where no sensor detects the target. Search must expand or the track is broken.
Recovery search An expanding ring search around the last predicted position after the target is lost.
Tracking Design Checklist
Check coverage gaps Sparse placement or small sensing radius creates locations where the target cannot be detected.
Balance overlap Overlap improves robustness but wakes more nodes and can increase contention.
Model target speed Fast targets need earlier wake-up and may require larger prediction zones.
Handle missed detections A real design needs a timeout, search radius, and recovery policy for lost tracks.
Limit reporting load Many active sensors may detect the same target, so aggregation or leader selection is needed.
Account for battery Prediction saves energy only if wake scheduling and synchronization overhead stay small.
Technical Accuracy Notes
Ideal sensing model The circle is a simplification. Real sensing probability changes with environment, noise, and target type.
Not localization The demo shows detection-based tracking, not precise position estimation from RSSI or time-of-arrival.
Energy estimate The savings metric counts awake node time only; radio startup, synchronization, and retransmissions are not modeled.
Handoff meaning A handoff is counted when the closest detecting node changes between frames.
Recovery behavior The expanding ring is a common concept, but real search algorithms also consider routing, latency, and confidence.
Multiple targets This page tracks one target. Multi-target tracking needs identity management and data association.

Related Animations

These pages extend the same sensing, coverage, and network-energy ideas.

Sensor Network Pipeline Trace sensing, payload, network, and cloud steps for IoT data. Network Emulator Inspect how link rate, latency, retries, and payload size affect delivery. Particle Filter Compare geometric detection with probabilistic belief tracking.