Dynamic Fog Intelligence with Flow Control for Green Internet of Things

With the growing number of green Internet of Things (IoT) applications, the underlining network and decision services need more Machine Learning (ML) models at the same time while reducing latency and utilized energy. In this paper, we propose HI-SDN, a Heterogeneous fog Intelligence enabled architecture for complex Software-Defined Network (SDN)-based IoT network running applications such as a smart city and healthcare. HI-SDN proposes a fog node and ML algorithm selection mechanism with dynamic traffic forwarding for green IoT to reduce delay and energy consumption. It adopts a reconfigurable approach to the ML method to dynamically change the required prediction policy and flow rules to be executed in a selected edge/fog node for critical computation. The results from the performance analysis show that HI-SDN has a substantial reduction in delay by 32% and consumed energy by 25%, in comparison to the existing state-of-the-art, besides having an increase in packet delivery ratio.