Energy and Security-Aware Task Scheduling in Fog Computing: A Comparative Analysis of Scheduling Algorithms using IoT

Abstract

Efficient task scheduling in fog-cloud computing environments is essential for optimizing critical parameters such as energy efficiency, security, and real-time performance. Existing scheduling algorithms like Pure Random (PR) and Earliest Deadline First Random (EDF_R) often lack in generalization as its focus is primarily on individual aspects such as task deadlines or random task assignments, leading to inefficiencies in load balancing, resource utilization, and security management. The Security Aware Scheduling (SAS) algorithm introduces a security-centric approach, prioritizing task assignment based on security requirements, thus ensuring tasks are allocated to nodes that meet or exceed their security needs. The Minimum Energy Security-Aware Scheduling (MESA) algorithm addresses the energy constraints by minimizing energy consumption during task scheduling while maintaining security compliance. Furthermore, the Minimum Response Time Security-Aware Scheduling (MRSA) algorithm aims to reduce response times by optimizing node selection based on both minimum response time and security requirements. Despite these advancements, there is a need for a more comprehensive solution that simultaneously addresses the challenges of energy efficiency, security, and real-time task execution. This research proposes the Optimal Energy-Security Aware Scheduling (OESAS) framework, which integrates these critical factors, providing an adaptive and efficient scheduling solution for heterogeneous fog-cloud systems.