Wireless Sensor Network Anomaly Detection

I need a complete solution that pinpoints abnormal behaviour across my wireless sensor network. You will have direct access to three rich data streams—raw sensor readings, detailed network-traffic captures, and accompanying environmental context—so the approach can fuse information rather than relying on a single source.

Your job is to design, implement, and validate an anomaly-detection model that flags faulty nodes, suspicious traffic, or out-of-range measurements in near-real time. Feel free to select the technique that best fits the data volume and complexity; I am open to statistical, classic machine-learning, or deep-learning pipelines as long as the final system is transparent and reproducible. Python (NumPy, Pandas, Scikit-Learn, TensorFlow/PyTorch) is preferred for ease of deployment, though MATLAB is acceptable if it yields stronger results.

The deliverables should be:
• Well-commented source code and any trained models
• A concise README explaining setup, model logic, and how to retrain with fresh data
• A short performance report summarising detection accuracy, false-positive rate, and a few illustrative anomaly cases pulled from my data

I will supply sample datasets at project start and a secure channel for larger transfers. Let me know the turnaround you expect and any clarification you need before we begin.