Distributed-Reasoning for Task Scheduling through Distributed Internet of Things Controller

Abstract

Abstract The adoption of distributed reasoning through ubiquitous instrumentation in the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault tolerance, traffic, healthcare, etc. Using a ubiquitous controller to interconnect devices in the IoT is still in the embryonic stage. However, it has the potential to create distributed-intelligent IoT solutions that are more efficient and secure than centric intelligence. It is essential to take a new direction to design a distributed intelligent controller for task scheduling that can firstly dynamically interact with a smart environment in efficient real-time data processing and secondly respond to flexible changes. To address these issues, we outline a two-level intelligence scheme that leverages edge computing to improve distributed IoT. The edge scheme shifts the capability of streaming processing from the cloud to edge devices to alleviate latency, support better reliable streaming analytics, and improve smart IoT applications’ performance. In this work, to enable better, reliable, and flexible streaming analytics and overcome the data uncertainties, we proposed an IoT gateway controller that provides low-level intelligence by using a fuzzy abductive reasoner. Numerical simulations support the feasibility of our proposed approaches.