AUTOMATIC POOL LIFE SUPPORT SYSTEM USING IOT TECHNOLOGY

Abstract

The rapid advancement of Internet of Things (IoT) technology has led to its widespread adoption across various sectors, including smart homes, healthcare, agriculture, and industrial automation. Among the growing range of applications, one of the most promising is in the domain of intelligent environmental monitoring and control, where real-time data acquisition and automated decision-making play a pivotal role in optimizing system performance and user experience.

Swimming pool systems, which have traditionally relied on manual maintenance and scheduled checks, present a valuable opportunity for automation through IoT technologies. These systems often require continuous oversight to maintain water quality, ensure user safety, and manage energy consumption effectively. Manual methods can be time-consuming, error-prone, and inefficient in responding to dynamic environmental changes.

An automatic pool life support system, empowered by IoT, consists of a network of sensors and actuators managed by a microcontroller-based platform. This system is capable of monitoring and controlling key environmental parameters such as water temperature, pH levels, turbidity, ambient humidity, lighting, and air quality. These sensors provide accurate, real-time data that are crucial for maintaining optimal swimming conditions and ensuring user comfort and safety.

The collected data is transmitted wirelessly to a cloud-based platform, enabling remote access, real-time notifications, and automated control via mobile or web applications. This integration enhances operational efficiency, allowing pool owners and facility managers to monitor system status, receive alerts, and adjust settings without physical presence.

By leveraging low-power, wireless communication technologies such as Wi-Fi or Bluetooth, and utilizing compact, energy-efficient microcontrollers like the ESP32, the system achieves a balance between performance and cost-effectiveness. These technologies support reliable, scalable solutions with minimal energy consumption, which is particularly important in continuous monitoring scenarios.