# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 2 Related Work
### UAV-Aided Vehicular Networks  
Vehicles communicate with each other and with public networks through various communication systems, such as cellular networks and VANETs, to improve road efficiency and safety and access connected services [6]. UAVs mounted with communication equipment can improve the connectivity and efficiency of these networks in various ways [7]. UAVs can optimize the VANET routing process or they can be utilized as Store-Carry and Forward nodes to help ground vehicular networks improve connection and reliability in the presence of disconnected vehicles.  
Similar to our study, [PERSON] and [PERSON] [8] use UAVs as flying base stations to provide communications to rescue vehicles in disaster-affected areas. They look into the relationship between UAV altitude and vehicle connectivity for a single UAV scenario. In the case of many UAVs, they investigate the smallest number of UAVs required to achieve a certain level of vehicle communication. In [3], a case study on UAV-aided Vehicular Network (VN) architecture is presented, in which vehicles drive along a bi-directional two-lane straight highway while two mobile drones are flying over them to form a relay platform. They compared UAV-aided VN's throughput and latency performance to an 802.11p-based vehicular network, demonstrating its effectiveness. These two studies focus on infrastructure level rather than possible applications such as task offloading on the constructed network.  
[PERSON] et al. [9] proposed a sub-optimal method to increase coverage of full-duplex relay networks among ground vehicles by positioning UAVs. They have used a set of predefined UAV locations and have an estimated Signal-to-Interference-plus-Noise Ratio (SINR) between ground vehicles and UAVs. They have formulated an \(l_{0}\)-norm non-combinatorial and NP-hard minimization problem and used an \(l_{1}\)-norm approximation for it. With their approximation, a ten percent performance increase has been observed compared to a baseline scenario where UAVs have fixed locations.  
In [10], data dissemination in UAV-aided VANETs is investigated by formulating a network throughput maximization problem to find delivery strategies and select the optimal paths for data delivery while considering the transmission rate of links and the delay constraint for data dissemination. In their analysis, the graph knapsack problem is reduced to the throughput maximization problem, and a polynomial-time approximation scheme is proposed to solve that graph knapsack problem. In another study on UAV-aided data dissemination, [PERSON] et al. [11] propose a protocol to reduce delay and improve system throughput by using UAVs as relayers with caching capabilities in VANETs. UAV trajectories are planned based on vehicle distributions and topology, which directly impacts the performance of UAV-assisted data dissemination. A centralized UAV trajectory algorithm (CTS-DP) is developed based on dynamic programming. Their results imply that CTS-DP outperforms the distributed trajectory scheduling algorithm (MHC) studied in their previous work. In the prior algorithm MHC, each UAV schedules its trajectory by maximizing vehicle coverage.