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Design and Implementation of ESP32-Based Edge Computing for Object Detection.

Yeong-Hwa Chang^1,2, Feng-Chou Wu¹, Hung-Wei Lin¹

¹Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan.

Sensors (Basel, Switzerland)

|April 28, 2025

Summary

This summary is machine-generated.

This study demonstrates how an ESP32-based edge server enhances edge computing by reducing cloud burdens and latency. The system effectively lowers bandwidth usage and processing delays for AI-driven object recognition tasks.

Keywords:

ESP32 edge computing object detection tiny machine learning

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Area of Science:

Computer Science
Electrical Engineering
Artificial Intelligence

Background:

Growing need to reduce cloud server load and latency in IoT applications.
Limitations of traditional cloud-centric computing for real-time data processing.
Potential of edge computing to enhance performance and efficiency.

Purpose of the Study:

To design and implement an edge server system using the ESP32 microcontroller.
To evaluate the performance improvements of integrating edge and cloud computing.
To assess the effectiveness of localized AI processing for object recognition.

Main Methods:

Development of an ESP32-based edge server with detailed hardware and software architecture.
Implementation of communication protocols and a server framework for edge processing.
Deployment of a trained deep learning model for object recognition on the edge server.
Performance evaluation using metrics like classification time and MQTT transmission time.

Main Results:

Significant reduction in bandwidth usage and latency compared to cloud-only processing.
Effective alleviation of the load on the cloud server.
Demonstrated feasibility of deploying AI models for object recognition at the edge.

Conclusions:

Edge computing with ESP32 offers substantial benefits for IoT systems, enhancing efficiency.
Localized processing significantly reduces cloud dependency and improves response times.
The developed system provides a scalable solution for integrating AI and IoT at the edge.