A smart-based dustbin for office waste management

Abstract

Smart waste management systems have become increasingly popular in recent years, with a focus on developing more efficient and  sustainable waste management solutions. In particular, office waste management is a critical issue that requires more effective solutions.  This paper presents a novel approach for a smart dustbin system based on a Convolutional Neural Network (CNN) framework,  with secured Bluetooth connectivity using Elliptic Curve Cryptography (ECC) in sensitive situations. The proposed system is designed to  detect the level of waste in the dustbin, its gas purity, and recognize voice commands for automatic mobility. The CNN model is trained  on a dataset of spectrograms of speech signals to enable the detection and recognition of voice commands. The gas purity level is  detected using a gas sensor placed inside the dustbin, while an ultrasonic sensor is used to measure the waste level. The CNN model and  sensor data are integrated with an Arduino board to send notifications to a mobile application via Bluetooth connectivity with ECC for  secured data transmission. The proposed system was evaluated using a prototype implementation, and the results showed that the CNN  model achieved high accuracy in speech recognition, while the waste level detection and gas purity detection were accurate and efficient.  The use of ECC provided secured data transmission, which is crucial in protecting user privacy and preventing data tampering. The design  was evaluated with a usability experiment based on four key performance metrics. The experimental process provided an  average of 80% of participants' approval of the proposed system. Thus, the proposed smart dustbin system has the potential to improve  waste management efficiency by providing timely notifications to waste collection services and reducing environmental pollution. Future  research can explore the use of machine learning algorithms to adopt waste segregation in the smart dustbin system.