Efficient Scheduling of Energy-Constrained Tasks in Internet of Things Edge Computing Networks

Abstract

We offer task scheduling algorithms that are economical in terms of energy consumption for edge computing networks that are supported by the Internet of Things (IoT). The challenges of spectrum utilization and energy-efficient work scheduling that lead to novel design are not addressed in this study, despite the fact that it provides encouraging results for task offloading. There is a possibility that the larger homogeneous fog computing architecture will include all homogeneous nodes, in addition to additional spectrum for node-to-node and device-to-device communications and work scheduling. We create a fog computing architecture that is efficient in terms of energy consumption for edge computing networks that are supported by the Internet of Things. By utilizing this approach, user-device nodes are able to collaborate while simultaneously reaping the benefits of diverse computing and network resources. In addition to this, we provide a solution to the problem of task scheduling that maximizes energy efficiency across all of the help nodes.