IOT BASED GARBAGE LEVEL MONITOR FOR DUSTBINS

Abstract

The rapid growth of urban populations, commercial activities, residential communities, educational institutions, healthcare facilities, and public infrastructure has significantly increased the quantity of solid waste generated every day. Conventional waste collection systems primarily depend on fixed collection schedules, manual inspection, and periodic transportation of garbage from dustbins to disposal or processing facilities. Such approaches frequently result in overflowing dustbins, unnecessary collection trips, unpleasant odors, environmental pollution, increased fuel consumption, inefficient utilization of municipal resources, and poor public hygiene. This research proposes an IoT Based Garbage Level Monitor for Dustbins that continuously measures the amount of waste accumulated inside a dustbin and transmits real-time status information to authorized users or waste-management personnel. The proposed system employs an ultrasonic sensor mounted near the upper section of the dustbin to measure the distance between the sensor and the surface of accumulated waste. An IoT-enabled microcontroller such as the ESP32 acquires the sensor readings, performs filtering and calibration, calculates the approximate fill percentage, and classifies the dustbin condition into predefined categories such as Empty, Low, Medium, High, and Full. When the garbage level exceeds a configurable threshold, the system automatically generates an alert and transmits the information through Wi-Fi using MQTT or HTTP(S) communication protocols. A cloud-supported monitoring platform stores timestamped garbage-level information, maintains historical records, manages alert priorities, and provides centralized visualization through mobile or web dashboards. The proposed architecture consists of five major layers: Sensor Data Acquisition, Edge Processing and Fill-Level Classification, IoT Communication, Cloud and Waste Analytics, and Application/User layers. The framework further incorporates duplicate-alert suppression, communication retry mechanisms, device identification, battery or connectivity status monitoring, and configurable collection thresholds. Prototype-oriented evaluation is defined using level detection accuracy, alert success rate, system reliability, data transmission success, and response latency. Illustrative conceptual results indicate that the proposed system can improve monitoring effectiveness and reduce response delays compared with manual inspection, fixed-schedule collection, and basic standalone monitoring. The framework is low-cost, scalable, and suitable for smart cities, residential communities, campuses, hospitals, railway stations, bus terminals, shopping complexes, industrial facilities, and public waste-management environments.