IoT

TinyML Edge Machine Monitor

Low-power embedded monitor that classifies vibration and acoustic anomalies on-device and transmits only actionable maintenance events.

Edge ImpulseTinyMLESP32-S3TensorFlow LiteSensorsMQTT

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Technical Overview

How the solution is organized and why the structure matters.

Low-power embedded monitor that classifies vibration and acoustic anomalies on-device and transmits only actionable maintenance events. The technical blueprint separates responsibilities into clear layers so the solution can be tested, explained, extended, and handed over confidently.

System Architecture

Four clear layers keep responsibilities understandable and maintainable.

Device and sensing layer

Reads physical inputs, validates sensor values, and manages device-side control logic.

Connectivity layer

Moves telemetry and commands through resilient, clearly named messages with reconnect handling.

Backend and storage

Validates incoming payloads, stores time-series events, and exposes controlled service endpoints.

Monitoring interface

Presents live state, history, alerts, and device health in a responsive dashboard or app.

Technology Stack

The practical role of each technology in this project.

Edge Impulse

Embedded machine-learning workflow for sensor data, model training, and edge deployment.

TinyML

Machine-learning inference on constrained microcontrollers close to sensor data.

ESP32-S3

Connected microcontroller suited to low-power edge AI and device control.

TensorFlow Lite

Supports the project's IoT implementation, integration, or evaluation requirements.

Sensors

Physical measurement inputs with calibration and validation requirements.

MQTT

Lightweight publish/subscribe messaging for device telemetry and commands.

Implementation Workflow

A reviewable path from requirements to tested handover.

Requirements and constraints

Confirm users, inputs, outputs, acceptance criteria, platform limits, and delivery scope.

Architecture and prototype

Validate the highest-risk technical decisions with a small working baseline before full implementation.

Implementation and integration

Build in reviewable modules, connect the selected technologies, and document key decisions.

Verification and handover

Test expected and failure paths, review outcomes, and prepare technical documentation and guidance.