Studi Kinerja Transmisi Data Menggunakan ESP32 dan Raspberry Pi Pico Berbasis Simulasi Wokwi

The development of the Internet of Things (IoT) has increased rapidly, and microcontrollers play a central role in the control and data communication in the IoT ecosystem. This research compares the data transmission performance between two popular microcontrollers, ESP32 and Raspberry Pi Pico, using the Wokwi simulation platform. The ESP32 is known for its Wi-Fi and Bluetooth connectivity, while the Raspberry Pi Pico with its RP2040 processor offers solid performance at an affordable price. In this context, MQTT is used as the communication protocol for data delivery with low latency and minimal bandwidth. This research includes three stages: system design, schematic design, and system testing. The scheme used includes DHT22 and LDR sensors, and the process of monitoring data to the Thingspeak server. The simulation results show that both microcontrollers can manage the data transmission well, but the Raspberry Pi Pico shows a faster transmission delay time than the ESP32. The average delay time for light intensity, temperature, and humidity parameters on the Raspberry Pi Pico was 2 minutes 36 seconds, 3 minutes 7 seconds, and 3 minutes 49 seconds, respectively, while the ESP32 had a longer delay time. This study provides insight into the performance of both microcontrollers in the context of the IoT and can help developers choose a suitable microcontroller for their applications

Authors

Institutions

Publication Details

Journal
JOURNAL ZETROEM
Published
2025-03-11
DOI
https://doi.org/10.36526/ztr.v7i1.5102
Primary Topic
Engineering and Technology Innovations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Studi Kinerja Transmisi Data Menggunakan ESP32 dan Raspberry Pi Pico Berbasis Simulasi Wokwi

Arif Fahmi, Indra Kurniawan
JOURNAL ZETROEM
Engineering and Technology Innovations
article

Studi Kinerja Transmisi Data Menggunakan ESP32 dan Raspberry Pi Pico Berbasis Simulasi Wokwi

Arif Fahmi, Indra Kurniawan
article en

Abstract

The development of the Internet of Things (IoT) has increased rapidly, and microcontrollers play a central role in the control and data communication in the IoT ecosystem. This research compares the data transmission performance between two popular microcontrollers, ESP32 and Raspberry Pi Pico, using the Wokwi simulation platform. The ESP32 is known for its Wi-Fi and Bluetooth connectivity, while the Raspberry Pi Pico with its RP2040 processor offers solid performance at an affordable price. In this context, MQTT is used as the communication protocol for data delivery with low latency and minimal bandwidth. This research includes three stages: system design, schematic design, and system testing. The scheme used includes DHT22 and LDR sensors, and the process of monitoring data to the Thingspeak server. The simulation results show that both microcontrollers can manage the data transmission well, but the Raspberry Pi Pico shows a faster transmission delay time than the ESP32. The average delay time for light intensity, temperature, and humidity parameters on the Raspberry Pi Pico was 2 minutes 36 seconds, 3 minutes 7 seconds, and 3 minutes 49 seconds, respectively, while the ESP32 had a longer delay time. This study provides insight into the performance of both microcontrollers in the context of the IoT and can help developers choose a suitable microcontroller for their applications

JOURNAL ZETROEMVol. 7(1)
Universitas Pgri Banyuwangi (ID)
Openalex Percentile: Top 94%
Engineering and Technology Innovations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.