IOT-BASED SENSOR-CONTROLLED PET FEEDER WITH MEALTIME TRACKING AND ACTIVITY NOTIFICATION APPLICATION
DOI:
https://doi.org/10.35631/JISTM.1143013Keywords:
Internet of Thing (IoT), Pet FeederAbstract
Due to the absence of a cohesive and user-friendly system, pet feeding solutions often suffer from irregular feeding schedules, limited health tracking, and poorly integrated technologies. To enhance feeding management and general pet well-being, a comprehensive automated solution with a monitoring system is required. Thus, this study presents the design and simulation of an automated pet feeding and monitoring system optimised through the integration of intelligent sensors, actuator control, and IoT-based data management. The system is powered by an ESP32 microcontroller that supports Wi-Fi for seamless data interchange and remote control. The system uses an ultrasonic sensor to measure water levels, a load cell and HX711 amplifier accurately quantifies food, and a PIR sensor detects pet presence near the feeding station. To enhance monitoring, a camera module provides live video streaming, offering remote users improved situational awareness. The ESP32 is the main processing unit, which would interpret sensor data and activate core actuators in the case of a servo motor to disperse food on command, water to refresh it, and a buzzer to alert users when food or water levels are low. The LCD shows important data like food amount, water content, detection of pets and a schedule of approaching the next feeding period. One-minute syncing between sensor data and a cloud platform makes it easy to monitor and remotely control by using IoT applications. The results indicate that the system is capable of improving feeding accuracy, routine compliance, and promoting pet health through the availability of food and water regularly. By combining real-time monitoring with automated control, the system delivers a reliable, forward-looking solution ideal for smart-home and pet-care contexts. Future improvements may explore adaptive feeding strategies, energy saving techniques and machine learning for forecasting pet behaviour.
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