The great amount of generated data from IoT infrastructures in Smart Cities, if properly leveraged, presents the opportunity to shift towards more sustainable practices in rapidly increasing urban areas. Reasoning upon this data in a proactive way, by avoiding unwanted future events before they occur, leads to more efficient services. For a system to do so, a robust reasoning model, able to anticipate upcoming events and pick the most suitable adaptation option is needed. Recently deployed smart waste management systems for monitoring and planning purposes report substantial cost-savings and carbon footprint reductions, however, such systems can be further enhanced by integrating proactive capabilities. This work proposes a novel reasoning model and system architecture called ProAdaWM for more effective and efficient waste operations when faced with severe weather events. A Bayesian Network and Utility Theory, as the basis of Decision Theory, are utilized to model the uncertainties and handle how the system adapts; the proposed model utilizes weather information and data from bin level sensor for reasoning. The approach is validated through the implementation of a prototype and the conduction of a case study; the results demonstrate the expected behavior.

Waste Management is one of the biggest challenges for modern cities caused by urbanisation and increased population. Smart Waste Management tries to solve this challenge with the help of techniques such as Internet of Things, machine learning and cloud computing. By utilising smart algorithms the time when a recycling container is going to be full can be predicted. By continuously measuring the filling level of containers and then partitioning the filling level data between consecutive emptyings a regression model can be used for prediction. In order to do this an accurate emptying detection is a requirement. This thesis investigates different data-driven approaches to solve the problem of an accurate emptying detection in a setting where the majority of the data are non-emptyings, i.e. suspected emptyings which by manual examination have been concluded not to be actual emptyings. This is done by starting with the currently deployed legacy solution and step-by-step increasing the performance by optimisation and machine learning models. The final solution achieves the classification accuracy of 99.1 % and the recall of 98.2 % by using a random forest classifier on a set of features based on the filling level at different given time spans. To be compared with the recall of 50 % by the legacy solution. In the end, it is concluded that the final solution, with a few minor practical modifications, is feasible for deployment in the next release of the system.