Edge AI and Causal Attribution for Cold-Chain Integrity in Agri-Food Export Corridors
Edge AI and Causal Attribution for Cold-Chain Integrity in Agri-Food Export Corridors
Description
Details
Context and Problem Statement
Cold-chain failures cause major losses in perishable exports, but conventional loggers usually flag only threshold violations without identifying the root cause.
Research Question
Can a thermal anomaly be reliably attributed to an identifiable causal mechanism from multi-sensor edge data under energy and intermittent-connectivity constraints?
Proposed Approach
Instrument shipments with temperature, humidity, door-state, position, and refrigeration-power sensors. Detect anomalies using compact edge models, then perform causal attribution using a mechanism graph validated through controlled experiments. Estimate downstream shelf-life impact.
Expected Contribution
A shift from threshold alarms to experimentally validated causal attribution.
Expected Prototype
An autonomous tracking device and dashboard reporting likely cause, confidence, and estimated shelf-life impact for each incident.
Datasets
Public logistics sensor datasets, real export-route measurements, and cold-room experiments.
Challenges
Sensor autonomy, intermittent connectivity, experimental validation cost, and commercially sensitive traceability data.
Research Question
Innovation
Expected Deliverable
Technologies & Tools
Required Skills
- Embedded Systems and IoT
- Signal Processing and Anomaly Detection
- Applied Causal Inference
- Experimental Design
Datasets
- Public logistics sensor time series
- Measurements from real export routes
- Controlled cold-room experiments