Performance Evaluation of IoT-Based AC Control Using Multi-Modal Fuzzy Sensors

Abstract

his study addresses the challenge of controlling Air Conditioner (AC) temperature in enclosed spaces in tropical climates, where improper operation often leads to thermal discomfort and excessive energy consumption. The research aims to develop and implement an Internet of Things (IoT)-based system for monitoring and controlling AC temperature by integrating multi-modal sensors and applying a fuzzy logic approach. The proposed system employs a DHT22 sensor to measure temperature and humidity, a thermopile sensor to capture human body temperature, and a PIR sensor to detect occupancy and movement within the room. Sensor data are processed using an ESP32 microcontroller with FreeRTOS-based multitasking and transmitted to the Blynk platform for real-time monitoring. Decision-making is carried out using fuzzy logic based on the temperature difference (ΔT) between body temperature and ambient conditions to automatically regulate AC operation. Experimental results indicate that the system performs reliably and provides adaptive control, achieving a fuzzy logic accuracy of 64.34% under real-world conditions. Furthermore, the automated control mechanism reduces energy consumption by 35.7% compared to conventional manual operation. Overall, the findings confirm that the integration of multi-modal sensing, IoT technology, and fuzzy logic can effectively enhance energy efficiency while maintaining thermal comfort in indoor environments.