ArticleSensors (Basel, Switzerland)2025
Design and Implementation of ESP32-Based Edge Computing for Object Detection.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- On-Skin Wearable Health Monitoring Devices: Recent Trends and Perspectives.Sensors (Basel, Switzerland) · 2026Review
- Development of a Low-Cost Portable System for Text-to-Speech Conversion: A Mechanical-Aided Optical Solution for Educational Inclusion.Sensors (Basel, Switzerland) · 2026Article
- Characterization of Latency Sources in a MicroPython-Based ESP32 Edge-Cloud Sensor Network.Sensors (Basel, Switzerland) · 2026Article
- HydroNeuro: A Data-Efficient IoT Sensing and Edge-AI Framework for Real-Time Hydraulic Anomaly Detection.Sensors (Basel, Switzerland) · 2026Article
- Contactless Battery Sensing: A Survey.Sensors (Basel, Switzerland) · 2026Review
- Low-Cost IoT-Based Predictive Maintenance Using Vibration.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper explores the application of the ESP32 microcontroller in edge computing, focusing on the design and implementation of an edge server system to evaluate performance improvements achieved by integrating edge and cloud computing. Responding to the growing need to reduce cloud burdens and latency, this research develops an edge server, detailing the ESP32 hardware architecture, software environment, communication protocols, and server framework. A complementary cloud server software framework is also designed to support edge processing. A deep learning model for object recognition is selected, trained, and deployed on the edge server. Performance evaluation metrics, classification time, MQTT (Message Queuing Telemetry Transport) transmission time, and data from various MQTT brokers are used to assess system performance, with particular attention to the impact of image size adjustments. Experimental results demonstrate that the edge server significantly reduces bandwidth usage and latency, effectively alleviating the load on the cloud server. This study discusses the system's strengths and limitations, interprets experimental findings, and suggests potential improvements and future applications. By integrating AI and IoT, the edge server design and object recognition system demonstrates the benefits of localized edge processing in enhancing efficiency and reducing cloud dependency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.