An artificial intelligence model for energy-aware task scheduling based on learning automation for IoT applications .

In this paper, an artificial intelligence model for energy-aware task scheduling is based on learning automata in Fog Computing applications. fog computing is a distributed computing layer that acts as an intermediate layer between the cloud and the Internet of Things to improve the quality of services. One of the most important factors in energy consumption is related to task scheduling. This paper examines the timing of tasks in a fog environment to reduce energy consumption, makespan, and cost using learning automata. Then, for the first time, a neural network is introduced as a model of artificial intelligence based on learning automata in fog. The proposed neural network model has the ability to predict the relationship between the parameters of completion time, energy, and cost based on the number of virtual machines. The results of the proposed artificial intelligence model show that all the mentioned parameters are predicted with less than one percent accuracy.