LoRaWAN-aided Waste-to-Energy Concept Model in Smart Cities

With the use of sensor networks and machine learning (ML) techniques in data analysis, the impact of the works for smart cities is getting greater. As a sub-field o f s mart cities, waste management, and related waste transformations study and plan waste collection, disposal, and recycling. Especially, waste to energy transformation composes the major part of waste disposal. Predicting the energy to be obtained from waste and planning the energy supply accordingly depend on estimating the amount of waste and knowing its content. However, energy prediction from solid waste suffers from weak forecasting models, which lead to misguided management strategies in smart cities. Internet of Things (IoT) technologies like low-power, wide-area networking protocol (LoRaWAN) offer new opportunities to collect, monitor, and analyze data in smart cities, including waste management. In this study, we propose the LoRaWAN-aided Waste-to-Energy Concept Model to build the waste-to-energy prediction model with the provided smart city use case using LoRa network as an underlying data collection step. Consequently, we benefit from the pre-trained Gradient Boosting Regression (GBR) model whose process details are provided in our previous study, to predict municipal solid waste using daily data along with other relevant variables such as temperature.