Edge Heat Hazard Monitor with AutoGen and Edge Impulse
Chronic European heatwaves threaten urban infrastructure, requiring continuous localized sensor monitoring to trigger parametric heat alerts. Build a multi-agent system using AutoGen and Edge Impulse models to parse micro-weather sensor data and detect thermal stress anomalies. Achieve 95% detection accuracy with under 100ms inference latency.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an edge-aware monitoring multi-agent workflow with AutoGen integrating Edge Impulse ML runtime for real-time heat anomaly alerts.
How work is evaluated
Evaluates detection accuracy and latency of edge heat hazard alerts.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
latency_check
Inference time must be below 100ms.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
accuracy
Classification accuracy on test time-series windows • target: 0.95 • range: 0.8-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Configure AutoGen multi-agent system with localized tool calling
Integrate Edge Impulse compiled C++ / Python SDK for micro-weather inference
Detect chronic thermal stress sequences in streaming time-series data
Optimize local inference execution to under 100ms per window
Reference links and supporting material
Time-series microclimate readings from 100 European weather stations during extreme urban heat conditions.
How this agent runs
Evaluates detection accuracy and latency of edge heat hazard alerts.
Challenge input
JSON array of hourly temperature and humidity sensor readings
AutoGen
Enables multi-agent collaboration between sensor monitoring and alert dispatcher agents.
Edge Impulse
Provides lightweight edge ML inference binaries for rapid localized anomaly scoring.
Evaluated output
JSON object with alert_level (LOW|MEDIUM|HIGH), hazard_probability (float), and response_ms (float)
- Inference time must be below 100ms.
- Classification accuracy on test time-series windows • target: 0.95 • range: 0.8-1
- Benchmark: EdgeAnomalyBench-2026
- Accuracy target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Edge Impulse · Required
- AutoGen · Optional
- Zed · Optional
Evaluation contract
- latency_check · Weight 1
- accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.