Abstract

Urban drainage channels are often inspected only after water-logging has begun, and most low-cost IoT monitors raise a single alert when a water-level threshold is crossed. This paper reviews twenty studies on IoT blockage detection, level-based blockage analysis, ultrasonic sensing, machine learning for sewer systems and AIoT architectures, derives six research gaps from them, and proposes a conceptual AIoT-Based Drainage Blockage Early Warning System (AIoT stands for Artificial Intelligence of Things). The design places two HC-SR04 ultrasonic sensors upstream and downstream of a possible blockage together with a flow input, processes the readings on an ESP32 microcontroller every second, classifies the channel as Normal, Developing Blockage or Severe Blockage, and signals the result locally through green, yellow and red LEDs and a buzzer and remotely through a cloud dashboard. A transparent laboratory channel with a servo-controlled blockage is planned for repeatable testing. The circuit design and a Wokwi simulation are complete, and the estimated hardware cost is about ₹2,300. The contribution is a literature-grounded design and research agenda; the physical prototype and the machine learning classifier have not yet been built or validated.

Keywords

How to Cite This Article

Rights