Design and Development of an AI–IOT Enabled Smart Vacuum Cleaner using Blower-Based Adaptive Suction Mechanism

A blower is a mechanical device used to move air at moderate pressure [6]. A smart vacuum cleaner that combines IoT and AI is presented in this paper [7][8].The construction, operation, and uses of blowers are examined in this paper, along with a case study of a smart vacuum cleaner that combines artificial intelligence (AI) with the Internet of Things (IoT). AI allows for cognitive navigation and decision-making, while the blower creates the suction needed for dust collection. IoT enables smart devices to be used for remote monitoring and control. Efficiency, automation, and user convenience are all improved by integrating these technologies. The system is appropriate for contemporary smart homes since it exhibits enhanced cleaning performance, adaptability, and smart functionality. The design and development of a smart vacuum cleaner system that makes use of blower technology combined with Internet of Things (IoT) and artificial intelligence (AI) modules is presented in this study. The suggested solution uses real-time data analytics, intelligent path optimisation, and adaptive suction control to overcome the drawbacks of traditional vacuum cleaners. IoT connectivity guarantees ongoing monitoring and user-centric insights, while AI integration allows for autonomous learning of room layouts and identification of often polluted zones. The study demonstrates how combining mechanical efficiency with digital intelligence can enhance cleaning performance, reduce energy consumption, and improve user experience. Household and industrial cleaning devices have evolved significantly, yet most conventional vacuum cleaners remain limited in adaptability, energy efficiency, and data‑driven functionality. The technology uses 15% less energy and increases cleaning efficiency by 20–25%. To address these issues, this study suggests a smart vacuum cleaner system that combines blower technology with AI and IoT modules. The system is built to be self-learning, energy-efficient, and able to give users useful insights.