Proactive Adaptation of Behavior for Smart Connected Objects

Abstract: 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.