Raspberry Pi 5 Bayesian IoT Sensor Fusion Research Kit

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Equip yourself to deploy a Bayesian neural network directly on the Raspberry Pi 5, fusing real-time data from three ESP32 sensor nodes. This kit elevates your IoT project from a deterministic classifier to a safety‑aware system that outputs full probability distributions — confidence intervals that let an autonomous drone, industrial monitor, or medical device know when it is unsure. Instead of a single prediction that could hide critical doubt, you get actionable uncertainty metrics for every decision.

What You'll Build

You will assemble a multi-sensor fusion network that ingests heterogeneous data (temperature, vibration, gas, IMU readings, etc.) wirelessly via ESP32 boards and processes them on the Raspberry Pi 5 with an NVMe‑accelerated Bayesian model. The output is not just a fused state estimate but a calibrated confidence interval, displayed live on a dashboard. The system can trigger a safety fallback when uncertainty exceeds a threshold — a core requirement for autonomous vehicles, structural health monitoring, and any application where a wrong decision carries high risk.

What You'll Learn

• Implementing Monte Carlo dropout for approximate Bayesian inference on the Raspberry Pi 5’s ARM Cortex‑A76 cores, balancing accuracy and latency.
• Fusing heterogeneous sensor data from multiple ESP32 nodes into a unified probabilistic model that distinguishes aleatoric noise from epistemic uncertainty.
• Quantifying and visualizing real‑time confidence intervals for each fused prediction, enabling safety‑critical decision logic.
• Optimising a Bayesian neural network with ONNX Runtime and NVMe storage to achieve near‑real‑time inference on a compact edge device.

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Final‑year B.Tech ECE/EEE students at IITs, NITs, VIT, and BITS Pilani who need a research‑grade probabilistic sensor fusion platform for their thesis. Smart India Hackathon teams building industrial safety monitors or autonomous systems that must report confidence in every sensor reading. Makers and PhD researchers exploring the intersection of Bayesian deep learning and edge computing, targeting publications on uncertainty‑aware IoT perception.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.