SDN-Controller Triggered Dynamic Decision Control Mechanism for Healthcare IoT

Due to the lack of an integrated communication and computation architecture for Software-Defined Healthcare IoT (SD-HI), provisioning critical services is challenging. In this paper, we propose SD-Health, an edge-based decision making and task allocation (EDT) scheme for SD-HI. The proposed SD-HI network uses Machine Learning (ML)-based approach to predict the criticality of flows and location of mobile devices. Based on the predicted values, the controller delegates the required EDT module to the respective edge node. The controller identifies the future healthcare-related decisions for an edge node and prepares the module accordingly. The ML-based trajectory prediction allows to find the future location of mobile devices in the network. Once the location of the mobile device is predicted, a set of computation tasks is dynamically allocated to the edge node. The results of performance analysis show that SD-Health has a significant improvement in latency by 43.3% and energy consumption by 30%, compared to the existing state-of-the-art, along with a fair improvement in packet delivery ratio.