Unmanned Aerial Vehicle Computation Task Scheduling Based on Parking Resources in Post-Disaster Rescue  
[PERSON]  
1 School of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China 1  
[PERSON]  
2 School of Cyberspace Security, Dongguan University of Technology, Dongguan 523106, China 2*Correspondence: [EMAIL_ADDRESS], Tel. +86-13929225512  
and behavior recognition, in a timely manner. The reason is that AI applications, which generally demand complicated operations, are compute-intensive tasks. Besides, most of the AI applications often have response-time constraints. The resulting response timeout will render the task invalid. Hence, the application data from UAVs during a relief process should be uploaded in a timely manner, with the aim of prolonging the service duration of the UAVs. Cloud computing, which is based on on-demand access via the Internet, is capable of offering reliable services for UAVs according to usage. This is because cloud computing offers various kinds of resources on demand. However, the geographical distance from the users to the cloud servers in the data center can be very long, which makes the quality of experience (QoE) of the users hard to be ensured. To assure a better QoE for UAVs, some studies described the transfer of data from UAVs to the edge server through multi-access edge computing (MEC) [5; 6]. Compared to cloud-assisted methods, offloading using MEC servers is more appropriate for delay-sensitive and compute-intensive tasks. The computation capacity of a MEC server, however, is much more restricted than that of centralized clouds, and many tasks may face the problem of not being carried out in a timely manner once the workload of the MEC server is heavy [7]. To resolve the vital problem of designing an efficient uploading strategy for UAVs, reference [8] employed a large number of mobile unmanned ground vehicles (UGVs) to take part in UAVs' task calculation in a post-disaster rescue. However, unmanned ground vehicles are expensive [9], and the cost of employing a large number of UGVs to perform a task is very high. Parked vehicles can be found everywhere in a city, and most automobiles have onboard units (OBUs) that provide computational and storage capabilities. Even if a disaster occurs, there are still some parked vehicles that are not damaged after the disaster. These parking resources can easily be utilized in task execution to satisfy the resource needs of UAVs. Therefore, we place emphasis on ground parked vehicles working together in order to efficiently perform the AI applications from UAVs. Specifically, to achieve a rational allocation of resources, undamaged vehicles that park outside within the monitoring range of a UAV are firstly formed into a cluster as much as possible. Then, we analyze the task execution cost, which considers both the time duration for finishing the task and the task performers' energy cost for performing the task. Finally, a deep reinforcement learning (DRL)-based uploading policy is constructed. By interacting with the environment in a smart manner, the task completion rate is maximized while the task execution cost is minimized. The simulation experiments show that our algorithm achieves a great improvement in task completion rate and task execution cost. The contributions of this paper are presented as follows:  
Undamaged parked vehicles are arranged and then applied to help UAVs during a post-disaster rescue. The use of parked cars makes full use of a city's useful resources. We arrange undamaged vehicles that park outside within the communication range of a UAV into a cluster as much as possible. The cluster architecture facilitates the management of parked vehicles and the allocation of the offloaded tasks. Taking the advantages of DRL, we propose to build a task-scheduling model based on DRL to tackle the offloading issue of multiple tasks to meet the strict quality of experience (QoE) of UAVs. The rest of this paper is organized as follows: Related works are discussed in Section 2.