# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 1 Introduction  
Future network infrastructure such as 6G networks is expected to provide much better QoS and user experience for a wide range of networked services anytime-anywhere in an edge-to-cloud continuum [1]. To this end, Unmanned Aerial Vehicles (UAVs) are instrumental to deploy a local network infrastructure dynamically or on an on-demand basis. These circumstances can occur during short-term demand surges for capacity, post-disaster communications, or dynamic data collection [2]. In that regard, UAV networks have three salient characteristics [3]. The first one is related to their location: since they are aerial systems, they enjoy a higher probability of line-of-sight (LoS) links for connecting ground nodes and other UAVs in comparison to terrestrial systems. Secondly, UAVs can be dynamically deployed in response to emerging capacity and connectivity requirements unlike the stationary ground infrastructure. Finally, a swarm of UAVs can construct scalable multi-UAV networks in a flexible configuration for pervasive and seamless services. On the ground level, novel intelligent mobility solutions such as self-driving cars are already in operation, and vehicle communications are becoming more vital as user adoption increases. Applicable use cases in 5G and future networks may also be mission-critical, relying on connected services, such as emergency communications or cloud robotics for search and rescue. For these applications, UAVs can assist user devices and vehicles by providing cloud access, packet relaying or edge services for computation, storage and networking. In this work, we consider such a UAV integrated edge network and focus on the use case in which UAVs create an ad hoc network for connectivity including emergency communications. The investigated edge network is software-defined as envisaged in current 5G as well as future networks, which allows our system to cope with network management issues such as mobility-based frequent topology change and dynamic system states [4]. A post-disaster operations management scenario is designed in which the network offers Vehicle-to-Vehicle (V2V) and Vehicle-to-Cloud (V2C) task offloading services to users, e.g., search and rescue team vehicles or related IoT devices. For such use cases where we have time-sensitive and mission critical services, it is imperative to minimize delays via appropriate resource allocation while satisfying resource constraints. In this paper, we work on this problem and develop a task offloading scheme comprising different heuristics. We adopt a priority based approach for computation tasks in our edge UAV network with limited connectivity and resource availability. We also integrate a processing deadline into our scheme for each task as the key QoS criterion. Moreover, we investigate the performance of this system under different operating conditions via simulation-based experiments. The contributions made in this paper can be listed as follows:  
* _Offloading management optimization model:_ We proposed a mathematical model to make centralized assignment/offloading decisions for the incoming task processing requests. Our model considers centralized assignment decisions for task offloading requests received from mobile ground user units. Offloading can be assigned to ground vehicles or a cloud processor service. The model's objective function is to minimize average penalty scores caused by task completion time;
* _Open-source contribution to Mininet-WiFi:_ The wmedium application, a Wi-Fi delay and loss simulator and used by a Mininet-WiFi emulator [5], did not support independent mediums simultaneously, which meant that all packets experience scheduling collisions with one another. It had prevented us from performing multiple data transfers at the same time without suffering a significant loss in throughput.