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http://dx.doi.org/10.3837/tiis.2022.11.004

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

Sun, Mengmeng (College of Information Science and Engineering, Hunan Normal University)
Zhang, Lianming (College of Information Science and Engineering, Hunan Normal University)
Mei, Jing (College of Information Science and Engineering, Hunan Normal University)
Dong, Pingping (College of Information Science and Engineering, Hunan Normal University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.11, 2022 , pp. 3523-3543 More about this Journal
Abstract
Autonomous vehicles are gradually being regarded as the mainstream trend of future development of the automobile industry. Autonomous driving networks generate many intensive and delay-sensitive computing tasks. The storage space, computing power, and battery capacity 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 software-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.
Keywords
software-defined edge network; autonomous vehicles; emotion model; task scheduling;
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