Energy-Aware Edge Intelligence for Real-Time Embedded Systems using Adaptive TinyML

The growing need for continuous environmental observation has created demand for compact, reliable, and energy-efficient monitoring systems that can collect environmental parameters in real time. Conventional monitoring systems often involve high power consumption, complex hardware, and limited remote accessibility. This paper presents the design and implementation of an energy-efficient IoT-based embedded system for real-time environmental monitoring. The proposed system uses an ESP32-based embedded controller integrated with environmental sensors for measuring parameters such as temperature, relative humidity, air quality, and ambient light intensity. The acquired sensor data are processed locally to reduce unnecessary communication and identify significant environmental events. Wireless connectivity is provided through Wi-Fi, while MQTT is used for lightweight data transmission to an IoT monitoring platform. An energy- management mechanism based on duty cycling and sleep operation is incorporated to reduce power consumption during periods of low activity. The system provides real-time visualization, threshold-based event detection, and remote monitoring through an IoT dashboard. The proposed architecture combines sensing, local processing, wireless communication, and energy management within a compact embedded platform, making it suitable for indoor environmental monitoring, smart buildings, agricultural environments, and other IoT-based applications.