PlanIoT: A Framework for Adaptive Data Flow Management in IoT-enhanced Spaces


This paper presents PlanIoT, a middleware approach for enabling adaptive data flow management in IoT-enhanced spaces (e.g., buildings) using automated planning methodologies. Today’s sensorized spaces deploy applications falling to diverse categories such as analytics, real-time, transactional, video streaming and emergency response. Depending on the category, applications have different QoS requirements related to timely delivery, networking resources, accuracy, etc. Typically, state-of-the-art data exchange systems introduce policies for bandwidth allocation or prioritization for specific data types and applications (e.g., camera data). PlanIoT introduces a generic QoS model to evaluate the performance of data flowing in Edge infrastructures and generates their performance metrics dataset. Such a dataset is used as input to automated planning representations to intelligently satisfy QoS requirements of deployed applications. The experimental results show that PlanIoT improves the end-to-end response time of time-sensitive flows by more than 50%, especially with an overloaded Edge infrastructure. We also show the adaptivity of our approach by considering emergency cases that require Edge infrastructure reconfiguration.

18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)
Houssam Hajj Hassan
PhD Student (Télécom SudParis / IP Paris)