Emotion-aware Task Scheduling for Au-tonomous Vehicles in Software-defined Edge Networks

• Mengmeng Sun,
• Lianming Zhang,
• Jing Mei,
• Pingping Dong,

Vol. 16, No. 11, November 30, 2022
10.3837/tiis.2022.11.004, Download Paper (Free):

• software-defined edge network
• autonomous vehicles
• Emotion Model
• task scheduling

Abstract

Autonomous vehicles are gradually being regarded as the mainstream trend of future devel-opment of the automobile industry. Autonomous driving networks generate many intensive and delay-sensitive computing tasks. The storage space, computing power, and battery ca-pacity of autonomous vehicle terminals cannot meet the resource requirements of the tasks. In this paper, we focus on the task scheduling problem of autonomous driving in soft-ware-defined edge networks. By analyzing the intensive and delay-sensitive computing tasks of autonomous vehicles, we propose an emotion model that is related to task urgency and changes with execution time and propose an optimal base station (BS) task scheduling (OBSTS) algorithm. Task sentiment is an important factor that changes with the length of time that computing tasks with different urgency levels remain in the queue. The algorithm uses task sentiment as a performance indicator to measure task scheduling. Experimental results show that the OBSTS algorithm can more effectively meet the intensive and delay-sensitive requirements of vehicle terminals for network resources and improve user service experience.

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