EDGE-INTELLIGENT DATA REDUCTION AND ADAPTIVE COMMUNICATION IN STM32–ESP32 BASED IOT GATEWAYS

Abstract

The rapid growth of Internet of Things (IoT) systems has led to massive volumes of sensor data being generated continuously from remote and distributed sensing nodes. Transmitting all raw sensor data directly to the cloud results in excessive bandwidth usage, increased power consumption, higher operational costs, and network congestion. These problems arise especially in battery-powered IoT gateways using Wi-Fi-based communication. This project proposes an edge-intelligent IoT gateway using a dual-MCU architecture consisting of STM32 and ESP32 microcontrollers. The STM32 performs local data preprocessing, filtering, aggregation, and event detection, while the ESP32 is used only for uploading meaningful or exception-based data to the cloud. The system uses adaptive communication techniques such as time-based, threshold-based, and event-based data transmission to balance data accuracy and power efficiency. The objective is to significantly reduce transmitted data volume Without losing meaningful and actionable information. By moving intelligence to the edge, the system improves battery life, reduces network load, and enhances scalability. The proposed framework is suitable for industrial, environmental, agricultural, and smart city applications where long-term, low-power operation is required.