NB. WiFiTrail is exclusively available to law enforcement agencies and is not offered for public or commercial use.
WiFi Trail API: Chronological Movement Mapping
The WiFi Trail API is designed to allow accurate path reconstruction. It retrieves a sequence of GPS coordinates where a mobile hotspot’s MAC address was detected, allowing investigators to map the exact route a device has traveled.
- Sequence-Based Tracking:
Offers a chronological record of latitude, longitude, and accuracy data for each observation point. - Temporal Context:
Every position is paired with a timestamp, enabling minute-by-minute analysis of travel history and movement speed. - Automated Mapping:
Designed for immediate integration with mapping tools to visualize the journey of a mobile hotspot across geographic regions.
WiFi Cluster API: Identifying Stay Points and Routines
The WiFi Cluster API identifies geographic “stay points” through analyzing high-density activity areas. By applying the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, the system filters raw data to identify significant locations where a device stays active.
- High-Density Requirements:
To preserve data integrity, a cluster is formed only when at least 6 GPS points are detected in a concentrated area, effectively filtering out transient signals. - Precise Center Calculations:
The API calculates the average position of all points within a cluster to determine its center, delivering a reliable coordinate for a “stay point.” - Distinct Location Intelligence:
New clusters are established only if they are at least 1,000 meters away from existing centers. This makes certain that the data clearly distinguishes between different neighborhoods or points of interest. - Adaptive Updates:
As new data is received, the system can expand existing clusters if points fall within 1,000 meters of the cluster center, updating the cluster radius and sample count in real time.
Forensic and Intelligence Applications
WiFiTrail is built to address the strict demands of modern digital forensics and location intelligence.
- Mobile Forensics:
Reconstruct device movement history to verify statements, confirm a presence at specific scenes, or identify suspect travel routes. - Pattern and Crime Scene Analysis:
The clustering logic highlights “hotspots” of activity. This helps identify frequent meeting places or routines that are not immediately obvious through individual data points. - Surveillance Integration:
REST APIs enable integration of historical MAC address tracking into existing security and telecommunications platforms.
Technical Specifications
- RESTful Architecture:
Accessed via a standard REST interface for maximum compatibility with investigative software. - Powered by CPS:
All location lookups rely on the Combain Positioning System (CPS), leveraging an extensive global database of observed Wi-Fi signals and associated GPS data. - Data Protection:
Your data is private by design, and there is no sharing of location data or requests with third parties. The WiFi Cluster API is also available as OnPrem service.
NB. WiFiTrail is exclusively available to law enforcement agencies and is not offered for public or commercial use.
Use Case Example: Forensics and Digital Investigations
A Real-World Scenario:
Reconstructing a Suspect’s Timeline When a suspect claims they were not present at a specific location, investigators need reliable data to verify the alibi. By querying the MAC address of the suspect’s mobile WiFi hotspot through the WiFiTrail API, law enforcement can retrieve a chronological sequence of the device’s historical coordinates, transforming isolated detections into a clear movement timeline.
Example Case:
Finding the Truth with Stay Points. Consider an investigation in which the suspects use a vehicle equipped with a mobile hotspot. Standard tracking requires an installed device or tracking a known cell phone, but the WiFi Cluster API automatically processes historical data to reveal where the hotspot’s MAC address has been seen, and that reveals where the vehicle actually stopped.
If the API identifies a high-density cluster (a minimum of six recorded data points) centered over an undisclosed warehouse, investigators immediately have a lead on a potential stash house or secondary meeting location. The system’s distance threshold, which retains clusters only if they are at least 1,000 meters apart, clearly separates this newly discovered location from the suspect’s known addresses, providing precise, actionable intelligence.
Key Benefits for Law Enforcement:
- Validate Alibis:
Confirm a device’s exact presence or absence at a specific scene using timestamped, chronological location trails. - Identify Hidden Routines:
Uncover secondary crime scenes, hideouts, or frequent meeting spots by examining geographic “stay points” rather than isolated data points. - Cross-Reference Evidence:
Align a device’s movement history with external data, such as security camera footage or transaction logs, to build a case. - Reduce Blind Spots:
Eliminate uncertainty by replacing fragmented raw data with a continuous, data-driven map of a device’s journey.
Conclusion:
Using historical MAC addresses and intelligent data clustering, investigators can identify reliable behavioral patterns. WiFiTrail closes major gaps in digital forensics, allowing law enforcement to validate statements, expose hidden routines, and strengthen evidence with confidence.
Key Takeaways
What is Combain WiFiTrail?
WiFiTrail is a platform that provides a chronological record of GPS positions where a specific WiFi mobile hotspot (identified by MAC address) has been detected.
It is used by security and forensic professionals to map device movement and behavior.
How does WiFiTrail track device movement?
WiFiTrail aggregates historical GPS observations tied to unique MAC addresses, offering a clear timeline and route of a device’s movement using its WiFi hotspot data.
What is the WiFi Trail API?
The WiFi Trail API allows users to retrieve a sequence of GPS coordinates (with timestamps and accuracy data) where a mobile hotspot was observed, enabling accurate path reconstruction and mapping.
What types of data does WiFiTrail provide?
Data includes latitude, longitude, accuracy, and timestamp for each detection event, supporting detailed movement and travel history analysis.
What is the WiFi Cluster API?
The WiFi Cluster API identifies geographic “stay points” where a device remains active. It uses the DBSCAN clustering algorithm to filter and identify significant locations based on high-density activity.
How are clusters or stay points determined?
A cluster (stay point) is formed when at least 6 GPS points are detected in a concentrated area. New clusters are only created if they are at least 1,000 meters from existing clusters.
Can the system update clusters over time?
What are some common use cases for WiFiTrail?
- Mobile forensics (reconstructing movement history)
- Pattern and crime scene analysis (identifying hotspots or routines)
- Integrating historical MAC address tracking into security/surveillance platforms
What are the technical specifications of WiFiTrail?
WiFiTrail uses a RESTful API architecture and is powered by the Combain Positioning System (CPS), which leverages a global WiFi and GPS database.
How is my data protected?
WiFiTrail is private by design and does not share location data or requests with third parties. The WiFi Cluster API is also available as an OnPrem service for additional security.
Submit anonymized training data to enhance service. Training data never shared with any third party.
Can be used independently of operators
The service uses Combain’s global database of GPS positioned WiFi hotspots. Our algorithms continuously update the database by processing millions of positions from global crowdsourcing.