Overview#
In a suspected homicide investigation, the victim's smart thermostat logs show no occupancy movement after 22:47 on the night in question. The Ring doorbell recorded a vehicle matching the suspect's leaving at 23:12. The victim's Fitbit registered no heart rate data after 22:51. None of these devices were ever considered traditional evidence sources, yet together they construct a timeline that contradicts the suspect's alibi and places them at the scene.
IoT devices are now everywhere: in homes, vehicles, workplaces, hospitals, and public spaces. They generate continuous logs of human behaviour, location data, and environmental state that investigators increasingly cannot afford to ignore. Argus IoT Forensics provides the tools to acquire, interpret, and present evidence from the full spectrum of connected devices, each presenting unique extraction challenges, proprietary data formats, and privacy considerations that traditional forensic methods were not designed to handle.
Open Standards#
- IEEE 11073 (Personal Health Device): Wearable and medical IoT device observations are ingested using IEEE 11073 MDC codes, which are mapped to LOINC identifiers before storage and export.
- HL7 FHIR R4: Health data extracted from medical IoT devices (pacemakers, glucose monitors, fitness trackers) is serialised as FHIR R4 Observation resources, enabling interoperability with clinical and legal evidence systems.
- SAE J1939 / OBD-II (SAE J1979): Connected vehicle fault codes and diagnostic trouble codes are classified and preserved under their originating bus protocol (J1939 for heavy vehicles, OBD-II for passenger cars), maintaining evidential accuracy for crash and incident reconstruction.
- OGC SensorThings API (OGC 15-078r6): Environmental and industrial sensor data is acquired via a SensorThings API client, supporting forensic extraction from smart building, infrastructure, and field sensor networks.
- OPC-UA (IEC 62541): Industrial IoT and SCADA system data is retrieved through OPC-UA REST gateways, enabling forensic analysis of operational technology environments including manufacturing, warehouse, and critical infrastructure systems.
- ISO 8601: All timestamped evidence records across every device class use ISO 8601 date-time formatting, ensuring precision and unambiguous chronology in timeline reconstructions presented to courts.
Last Reviewed: 2026-02-05 Last Updated: 2026-04-14
Key Features#
Smart Home Forensics#
- Smart home ecosystem analysis extracting voice recordings, automation logs, and sensor data from Amazon, Google, and Apple devices.
- Smart lock and security system analysis recovering access logs, video recordings, and alarm events.
- Environmental sensor data recovery from thermostats, air quality monitors, and lighting systems.
- Home automation routine analysis reconstructing occupant behaviour patterns from device interactions.
- Network traffic analysis identifying device communications and data exchange patterns.
Wearable and Medical Devices#
- Wearable device forensics recovering heart rate, sleep patterns, step counts, and precise location data from fitness trackers and smartwatches.
- Medical IoT device analysis of pacemakers, insulin pumps, and continuous glucose monitors for health data and usage logs.
- Biometric data extraction including heart rate variability, blood oxygen, and activity intensity records.
- Health app data recovery from connected platforms and cloud services.
- Wearable notification and communication logs documenting alerts and interactions.
Vehicle and Industrial Systems#
- Connected vehicle data extraction covering speed, braking, location, route history, and video footage from vehicle telemetry systems.
- Industrial IoT sensor log extraction from manufacturing, warehouse, and critical infrastructure systems.
- Fleet management and commercial vehicle data recovery including driver behaviour and load monitoring.
- SCADA and operational technology system forensics for critical infrastructure investigations.
- Firmware extraction and analysis for investigating device behaviour and stored data.
Analysis and Documentation#
- Proprietary data format parsing across hundreds of device types and manufacturers.
- Timeline correlation combining IoT data with other digital and physical evidence sources.
- Location data reconstruction mapping movements through connected device interactions.
- Environmental condition reconstruction using smart home sensor data to establish conditions at specific times.
- Evidence documentation meeting legal admissibility standards for IoT-sourced evidence.
- Privacy compliance tools ensuring extraction activities meet legal requirements and warrant specifications.
- Expert report templates for presenting IoT forensic findings in court-ready formats.
Use Cases#
Homicide Investigation. Extract smart home device logs, wearable health data, and connected vehicle records to establish timelines, verify alibis, and place suspects at crime scenes through IoT evidence. Reconstruct the sequence of events using data from multiple connected devices operating simultaneously.
Insurance Fraud Investigation. Analyse connected home devices, vehicle telemetry, and wearable data to verify or contradict claims about property damage, accidents, and personal injury. Objective device data provides evidence that cannot be manipulated after the fact.
Workplace Safety Investigation. Extract industrial IoT sensor data to prove negligence, safety violations, or sabotage in workplace accident investigations. Reconstruct environmental conditions and equipment operation at the time of incidents using sensor records.
Domestic Violence and Stalking. Analyse smart home device logs, shared account data, and location-tracking wearables to document patterns of surveillance, control, and abuse. Preserve digital evidence of stalking behaviour for protective orders and prosecution.
Integration#
- Connects with digital forensics and evidence management platforms for chain of custody.
- Integrates with investigation and case management workflows for seamless evidence delivery.
- Links to timeline reconstruction tools for multi-source evidence correlation.
- Works with vehicle accident reconstruction and analysis systems for crash investigation.
- Supports export of forensic reports and evidence for legal proceedings and court presentation.
- Compatible with cloud forensics platforms for analysing IoT data stored in cloud services.
- Feeds into evidence correlation engines for cross-device pattern identification.
- Connects with manufacturer systems for device-specific extraction techniques and updates.