Build Drift-Proof IoT Systems with the Pi 5 Continual Learning Sensor Fusion 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.

In production IoT environments, sensor patterns shift over time due to environmental changes, hardware aging, or process drift, silently degrading model accuracy until failure. This kit puts you in control of that exact scenario. You’ll train a Raspberry Pi 5 AI model using Elastic Weight Consolidation (EWC) to continuously absorb new sensor streams from three ESP32 nodes without forgetting previously learned patterns—the same technique used in smart factories and edge AI systems that must run reliably for months.

What You'll Build

You’ll assemble a multi-node IoT sensor network where three ESP32 boards stream heterogeneous sensor data (temperature, vibration, light, etc.) to a Pi 5 edge server over WiFi. On the Pi 5, you’ll set up a continual learning pipeline powered by an NVMe SSD for fast model checkpointing and incremental training. By the final episode, you’ll have a deployed inference engine that retains high accuracy across all learned data distributions, even as the sensor environment drifts. The output is a production-ready baseline for any embedded AI product that must evolve without catastrophic forgetting.

What You'll Learn

• Implement Elastic Weight Consolidation on a Raspberry Pi 5 to freeze critical model parameters and prevent catastrophic interference
• Deploy a three-ESP32 sensor mesh that streams time-series data using a lightweight MQTT protocol, with configurable sampling rates
• Configure an NVMe storage layer on the Pi 5 M.2 HAT+ for rapid model persistence and incremental weight updates
• Inject controlled sensor drift into the data pipeline and quantify EWC’s retention performance against a naive fine-tuned baseline

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This advanced kit is built for final-year B.Tech ECE/EEE students tackling AI IoT capstone projects, participants in the Smart India Hackathon building edge intelligence solutions, and research scholars at IITs, NITs, VIT, or BITS working on continual learning algorithms. Early-career ML engineers validating production drift strategies for their startup’s IoT product will also find the exact hardware stack and methodology needed to move from simulation to real-world deployment.

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.