Internet of Vehicles (IoV)-Based Task Scheduling Approach Using Fuzzy Logic Technique in Fog Computing Enables...

Area of Science:

• Intelligent Transportation Systems (ITS)
• Vehicular Ad Hoc Networks (VANETs)
• Internet of Vehicles (IoVs)
• Fog Computing

Background:

• Intelligent Transportation Systems (ITS) increasingly rely on Vehicular Ad Hoc Networks (VANETs) and the Internet of Vehicles (IoVs).
• Fog computing, as a cloud extension, offers processing infrastructure close to VANETs, supporting smart vehicles.
• VANETs face limitations in vehicle processing power, bandwidth, time, and high-speed mobility, necessitating efficient task offloading.

Purpose of the Study:

• To propose a fuzzy logic-based task scheduling system for VANETs within the IoV architecture.
• To minimize latency and enhance response times for vehicles offloading tasks to the fog layer.
• To effectively manage workloads considering the constrained resources of vehicle nodes.

Main Methods:

• Developed a fuzzy logic-based task scheduling algorithm for VANETs.
• The algorithm selects appropriate processing units and offloads tasks with associated resources to the fog layer.
• Utilized a dataset with over 5000 crisp values for fog computing metrics like system utilization, latency, and task deadlines.

Main Results:

• The proposed framework outperformed existing algorithms in task ratio (by 13%) and reduced average turnaround time (by 9%).
• Significant improvements were observed in minimizing makespan time (by 15%) and overcoming average latency.
• The system effectively schedules tasks to fog layers, demonstrating reduced response times and overall task completion duration.

Conclusions:

• The fuzzy logic-based task scheduling system effectively addresses the latency and response time challenges in VANETs for IoVs.
• The proposed method provides a robust solution for offloading tasks to fog computing environments while respecting vehicle resource constraints.
• Simulation results validate the superiority of the proposed technique over conventional algorithms in key performance metrics.