# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 2 Related Work
### Task Offloading in UAV Networks  
UAVs face challenges acting as edge computing nodes because of their computing power, weight, and battery limits. As a result, task offloading is one of the key approaches to facilitate the limited resources in the overall system as much as possible and was recently researched as part of the edge-cloud continuum paradigm. [PERSON] et al. [12] propose a post-disaster rescue computation task offloading scheme for cooperative UAVs. UAV computation tasks are offloaded to Unmanned Ground Vehicles (UGVs) that have idle computation resources. Both UAVs and UGVs seek their own maximum profits in its setting. They propose a stable matching algorithm to transform the computation task offloading problem into a two-sided matching problem, taking into account that the algorithm iteratively solves the problem while maximizing the utility of UAVs. In [13], a delay-optimal task offloading approach is proposed for a multi-tier edge-cloud computing system in a multi-user environment. The main objective is the minimization of the total service time for UAVs applications. In this approach, UAV taskexecution is distributed across edge and cloud server computing nodes. Similarly, in [14], a dynamic heuristic algorithm is proposed to offload periodic tasks with the objectives of minimizing total time delay and energy consumption for the Software Defined Vehicular Network-supported services in the UAV-enabled MEC system. [PERSON] et al. [15] work on delay-sensitive task offloading to fog networks. They propose a heuristic particle swarm optimization algorithm based on a Lyapunov framework to complete tasks on time with the lowest energy consumption. In their stated scenario, computing units can be sped up to meet the time and stability constraints. In a multi-UAV-assisted road traffic scenario, authors in [16] construct a three-player sequential game-based computational offloading methodology for processing UAV data. The computation delay, energy overhead, and communication and computation costs are all part of the utility function being studied. Contrarily, task owners in our model are mobile ground units and task offloading decisions are centralized while UAVs only provide the network. In [17], an SDN-enabled UAV-assisted vehicular computation offloading cost optimization framework is defined. Vehicles can offload computationally intensive and time-sensitive tasks to reduce execution time and overall energy usage. Each vehicle receives network traffic data from the SDN controller. Vehicles make decentralized offloading decisions based on their own interests and global data. The stated problem is very similar to the one we addressed in our work, but task offloading to other mobile ground vehicles and UAV mesh networks is not possible in the given study contrary to ours. As an example of multistage optimization works, a UAV-assisted mobile offloading and the trajectory optimization problem is studied in [18]. The stated problem turns out to be non-convex, and the minimum user utility is maximized under the partial offloading schemes. Their three-stage optimization algorithm is based on successive convex approximation and nonlinear fractional programming. The power allocation, bandwidth allocation, offloading decision, and UAV trajectory are jointly optimized for the solution. In [19], [PERSON] et al. propose a hierarchical aerial computing system that would allow high altitude platforms (HAPs) and UAVs to work together to provide computing services for ground devices (GDs) with a variety of QoS requirements. In a hierarchical aerial computing system, they use the combined computing of UAVs and HAPs to meet the QoS requirements of GDs. They develop a partially observable Markov decision method to solve the very complex nonconvex optimization problem, which is constrained by multi-dimension resource management, energy constraints, and collision avoidance. In [20], [PERSON] et al. propose a self-learning strategy that assists a UAV with deciding whether to delegate its tasks.