Smart Sanitation System: An IoT and AI-driven Approach for Proactive Odor Management
Keywords:
Artificial intelligence, Edge computing, Internet of Things (IoT), Odor prediction, Predictive maintenance, Smart sanitationAbstract
Conventional urban sanitation management is inherently inefficient, such that an existing sanitation management process is only based on fixed urban cleaning schedules without any consideration of constantly varying usage dynamics. As a consequence of these inefficiencies, public facilities often end up being degraded due to the quick formation of malodors coupled with equally poor hygiene standards. This study discusses a novel smart sanitation system that provides the possibility of easily evolving from such conventional inefficiencies to higher efficiencies. In achieving this objective, the system architecture relies on an IoT sensor network comprising ESP32 microcontrollers with integrated MQ135 gas sensors and DHT11. The data collected is then processed to predict odor surges hours before they are detectable to human detection. With such a prediction, targeted condition-based maintenance alerts will be sent to cleaning personnel to ensure they are utilized only when necessary. The experimental results provided in this research demonstrate the predictive accuracy of this technique, which significantly reduces wastage of resources while maximizing efficiency. Additionally, this technique can help provide large-scale national programs like Swachh Bharat Abhiyan with improved user comfort and dignity. Ultimately, this research presents an excellent tool to incorporate intelligent sanitation management into the overall environment of smart technology.
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