Complex problems solution as a service based on predictive optimization and tasks orchestration in smart cities

Smart cities have different contradicting goals having no apparent solution. The selection of the appropriate solution, which is considered the best compromise among the candidates, is known as complex problem-solving. Smart city administrators face different problems of complex nature, such as optimal energy trading in microgrids and optimal comfort index in smart homes, to mention a few. This paper proposes a novel architecture to offer complex problem solutions as a service (CPSaaS) based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city. Predictive model optimization uses a machine learning module and optimization objective to compute the given problem’s solutions. The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators. The proposed architecture is hierarchical and modular, making it robust against faults and easy to maintain. The proposed architecture’s evaluation results highlight its strengths in fault tolerance, accuracy, and processing speed.