Waste management in smart cities: Optimization of waste container’s capacity using fixed-frequency collection

• Autor(a) principal: Costa, José Tiago Barata Pereira da
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/20971
• topic: Frequency-capacity; Logistics; Transportation; Waste collection; Big data; Data mining -- Data mining; Machine learning; Internet of things; LoRa; LoRaWAN; Smart Cities; Capacidade-frequência; Logística; Transporte; Recolha de resíduos

One of the main problems of modern societies is the control of the production flow and removal of urban solid waste, due to the continuous massification of urban areas. This dissertation focuses on the need to reduce the impact of human activity over the environment through the management of urban solid waste. It describes how technological advancements can lead to an increase in the sustainability of urban development, through more efficient planning and reduction of logistics costs and pollution emissions. The work uses data of the product 360Waste from Evox Technologies, a company of a former student of ISCTE, operating in Castelo Branco. This company is specialized in creating an integrated solution for efficient collection of urban waste. This solution is composed of volumetric reading sensors, based on LoRaWAN technology. These sensors are installed in urban solid waste containers, which are always sending data to a LoRaWAN gateway, every time an individual opens the container. Based on the data collected from the sensors, the research work challenge will be to develop a solution to optimize the management of urban solid waste, by defining a uniform collection system and using technologies known as Data Sciences and Machine Learning.