## Related Work
### Multi-Access Edge Computing  
It is mandatory for a FANET system to diminish latency to the maximum possible extent. MEC can solve the problem of latency resulting from long communication distance. So far, studies have been conducted to examine the usage of edge computing for UAVs [23; 24; 25]. However, the studies do not discuss the topic of communication link quality. ETSI proposed a reference architecture of MEC [26]. Primarily, the MEC reference architecture is composed of user equipment, mobile edge applications, and networks. The network is classified into either of the following three levels: system level, host level, and network level. The reference points and functional elements of the reference architecture are depicted by the reference architecture. [PERSON] et al. aimed to answer the surveillance-related concerns by proposing a data-driven transportation optimization model [27]. The model comprises UAV, dispatcher, aggregator, and edge devices. Each of the constituents undertake the designated tasks as follows: the UAV captures and validates the date; the dispatcher, in addition to validating the tasks, schedules the processing tasks in the edge computing devices; the aggregator assures a secure transmission of data; and the edge devices analyze the data. A hierarchical MEC architecture has been proposed by [PERSON] et al. [28]. It involves utilizing the resources of the MEC server for providing services customized on the basis of content type and the computing demand. After exploring the major causes of communication and computational latencies, [PERSON] et al. proposed a mathematical model. The model is used to estimate the computing latency in an edge node selected on the basis of either of the three policies [29]. A game theoretic model is proposed by [PERSON] et al. in which the UAVs, as game players, strategize to achieve the optimal tradeoff between energy overhead and the execution delay. As a result, the UAVs do not stay overburdened anymore [30]. [PERSON] et al. addressed the issue of end-to-end delay between the proxy virtual machine and the device. They claim to resolve the problem by suggesting two dynamic proxy virtual machine migration methods, which is corroborated by simulation results [31]. [PERSON] et al. attempted to resolve the issue of increased energy consumption and longer execution time by proposing a mobility-aware hierarchical MEC framework [32]. The proposed solution involves the MEC servers and, for sharing the computing tasks, a backup computing server. An incentive-based optimal computational offloading scheme was developed. The objective of quick-response and energy conservation was achieved to a significant extent.  
The methodology proposed by [PERSON] et al. [33] increases the system reliability and reduces the end-to-end source-actuator latency. Their work intends to broaden the 5G network edge by making the FANET UAVs fly close to the monitoring layer. The UAVs are accoutered with MEC facilities while carrying out the processing tasks and they follow a policy for mutual help for improved performance. However, the work fails to address the issue of limited battery duration of MEC UAVs.