Development of a Prototype Smart City System for Refuse Disposal Management

The future of modern cities largely depends on how well they can tackle problems that confront them by embracing the next era of digital revolution. A vital element of such revolution is the creation of smart cities. Smart city is an evolving paradigm that involves the deployment of information communication technology wares into public or private infrastructure to provide intelligent data gathering and analysis. To align concretely with the smart city revolution in the area of environmental cleanliness, this paper involves the development of a smart city system for refuse disposal management. The architecture of the proposed system is an adaptation of the Jalali reference smart city architecture. It features four essential layers, which are signal sensing and processing, network, intelligent user application and Internet of Things (IoT) web application layers. A proof of concept prototype was implemented based on the designed architecture of the proposed system. The signal sensing and processing layer was implemented to produce a smart refuse bin that contains the Arduino microcontroller board, Wi-Fi/GSM transceiver, proximity sensor, gas sensor, temperature sensor and other relevant electronic components. The network layer provides interconnectivity among the layers via the internet. The intelligent user application layer was realized with non-browser client application, statistical feature extraction method and pattern classifiers. Whereas the IoT web application layer was realised with ThingSpeak, which is an online web application for IoT based projects. The sensors in the smart refuse bin generate multivariate dataset that corresponds to the status of refuse in the bin. Training and testing features were extracted from the dataset using first order statistical feature extraction method. Afterward, multilayer perceptron artificial neural network and support vector machine were trained and compared experimentally. The multilayer perceptron artificial neural network model gave the overall best accuracy of 98.0% and the least mean square error of 0.0036.