Energy saving scheduling in a fog-based IoT application by Bayesian task classification approach

Abstract

The Internet of things increases information volume in computer networks and the concept of fog will helpus to control this volume more efficiently. Scheduling resources in such an environment would be an NP-Hard problem.This article has studied the concept of scheduling in fog with Bayesian classification which could be applied to gain thetask requirements like the processing ones. After classification, virtual machines will be created in accordance with thepredicted requirements. The ifogsim simulator has been applied to study our fog-based Bayesian classification scheduling(FBCS) method performance in an EEG tractor application. Algorithms have been evaluated on a practical applicationof brain signal tracking system. According to the results, the FBCS method, compared with other methods, has reducedthe energy consumption in the cloud and the executing task cost in cloud; and also the average of energy consuming inmobiles has been decreased by smart decision making.