ESP32-S3 Smart Sports Stopwatch with Go Backend
A connected sports timing system powered by the ESP32-S3, featuring high-precision MQTT time synchronization with a Go backend, adaptive low-power operation, lap and split recording, local flash storage, and OTA firmware updates.
Project Overview
The ESP32-S3 Smart Sports Stopwatch is a connected timing platform developed for sports applications requiring accurate lap and split recording, device-to-server synchronization, reliable data storage, and battery-efficient operation. The wearable device is powered by an ESP32-S3 running ESP-IDF and FreeRTOS, with dedicated firmware functionality for stopwatch timing, lap and split capture, OLED display management, MQTT communication, local flash storage, OTA updates, and intelligent power management. A Go backend communicates bidirectionally with the stopwatch through a Mosquitto MQTT broker using publish/subscribe messaging. Both the Go backend and Mosquitto broker are hosted on a Raspberry Pi, creating a compact edge-server architecture. One of the primary engineering challenges was achieving accurate time synchronization between the ESP32-S3 stopwatch and backend despite wireless communication latency. A custom MQTT-based synchronization mechanism was developed and achieved approximately 1–5 ms synchronization accuracy under test conditions. The second major challenge was optimizing battery consumption without sacrificing connectivity. When connected to the BrainApp, the stopwatch must remain subscribed to MQTT commands, making deep sleep unsuitable. To address this, the firmware turns off the OLED and transitions the ESP32-S3 into a connected light-sleep operating state, reducing consumption to approximately 10–12 mA while maintaining MQTT communication. When the BrainApp is disconnected and the stopwatch is offline, an inactivity timeout transitions the device into deep sleep, reducing consumption to approximately 500 µA. Any stopwatch button can wake the device and restore normal operation. This adaptive architecture balances high-precision synchronization, network responsiveness, and battery life while maintaining reliable local and server-side timing data.
Challenge
The project presented two primary engineering challenges: high-precision time synchronization and power optimization. The first challenge was maintaining accurate synchronization between the ESP32-S3 stopwatch and Go backend over MQTT. Sports timing requires lap and split events captured by the watch to remain closely aligned with the backend time reference despite wireless communication latency and timing differences between the embedded device and server. The second challenge was reducing battery consumption without sacrificing connectivity and responsiveness. When connected to the BrainApp, the stopwatch must remain reachable through MQTT so it can subscribe to and receive commands, making deep sleep unsuitable. However, keeping the ESP32-S3 fully active consumed approximately 200 mA. When the BrainApp is disconnected and the stopwatch is offline, maintaining MQTT and Wi-Fi connectivity is unnecessary, creating an opportunity to enter a significantly lower-power state. The firmware therefore needed to dynamically manage active, connected low-power, and offline deep-sleep states while coordinating MQTT communication, OLED operation, timing functions, FreeRTOS tasks, and button wake-up events.
Solution
For high-precision synchronization, a custom bidirectional MQTT publish/subscribe mechanism was implemented between the ESP32-S3 and Go backend. The synchronization logic compensates for device-to-server timing differences and maintains a closely aligned timing reference, achieving approximately 1–5 ms synchronization accuracy under test conditions. For power optimization, a connectivity-aware multi-level power-management architecture was implemented. During normal stopwatch operation, the system remains fully active at approximately 200 mA. When the stopwatch is connected to the BrainApp but becomes inactive, the OLED is turned off and the ESP32-S3 transitions into a light-sleep operating strategy, reducing consumption to approximately 10–12 mA while maintaining MQTT connectivity so BrainApp commands can still be received. When the stopwatch is not connected to the BrainApp and remains inactive beyond a configured timeout, the device transitions into deep sleep at approximately 500 µA. GPIO/button wake-up functionality allows any stopwatch button to wake the ESP32-S3 and restore normal operation. This state-aware architecture provides both high-precision timing synchronization and efficient battery usage without sacrificing connectivity when it is required.
Key Features
Results & Impact
Project Info
Client
Michael Reber
Duration
4 Months
Year
2026
Technologies
Client Success
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We partnered with ZyntelliTech Development LLC to develop a custom IoT solution based on the ESP32-S3 platform—and the results were exceptional. Their team engineered a reliable, low-power controller that captures real-time UV sensor data and seamlessly transmits it to our AWS cloud infrastructure. ZyntelliTech handled every layer of the system: from embedded firmware with OTA update capabilities and AWS IoT Core fleet provisioning, to full mobile app development using Flutter. The mobile apps allow users to monitor and manage devices effortlessly, with real-time data visualization and secure device onboarding. Their ability to deliver a tightly integrated solution—from hardware to cloud to mobile—was exactly what we needed. The firmware was efficient, the cloud communication was rock-solid, and the apps were beautifully designed and intuitive. ZyntelliTech proved to be a reliable and forward-thinking development partner, and we highly recommend them for any end-to-end IoT project.
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