# A Review of Flying Ad Hoc Networks: Key Characteristics, Applications, and Wireless Technologies
## 7 Integration of Technologies with the UAV-Networked Systems
These challenges can be mitigated by edge and fog computing in a distributed manner, with storage and processing of the data carried out near the places where the data are generated. Therefore, integrating edge and fog computing with UAVs can provides better results in certain cases [14]. * _Flying Edge Computing (Figure 6, box D):_ Edge computing allows data storage and computing closer to the sources of data. Edge computing has been integrated with FANET (flying edge computing) to mitigate the hardware limitations of UAVs and improve the performance of UAV networks [13]. Flying edge computing is employed to support real-time IoT applications such as video streaming surveillance, VR and AR, and smart transportation [112]. In flying edge computing, UAVs are associated with edge IoT devices such as GBSs to offload and migrate part of the data computation to the edge layer; the other parts of computation tasks are locally managed by the UAVs [113] without the intervention of the cloud [114]. Integrating UAVs with edge computing provides low latency and response time for different IoT real-time applications. However, certain applications require storage and computing of voluminous data such as video streams. The local resources of edge IoT devices cannot efficiently support such cases. Therefore, flying fog computing can be expanded to the core network to provide low latency for storage and processing of huge amounts of UAV data [10]. * _Flying Fog Computing (Figure 6, box E):_ Flying fog computing, which is located at the edge of the network, provides an intermediate level between the cloud and UAV layers. The fog layer communicates with the UAV layer through wireless connection and the cloud layer using the internet. The integration of fog computing and UAVs provides low latency for real-time UAV-assisted IoT applications along with high capacity in terms of computing and storage. Although the flying fog computing paradigm provides enough computing power for IoT nodes, a major issue involves the integration of the UAVs in the edge computing layer with the cloud computing layer [10; 14; 15]. Figure 7 shows the cloud, fog, edge, and IoT layers. * _Integrating UAVs into cellular networks (Figure 6, box F):_ Agile UAVs are a special class of lightweight fixed-wing UAVs with small control surfaces [115]; they can be used as flying base stations, mobile relays, users, sensors, network controllers, and even as a scheduler in a cellular network [16], providing high reliability and low latency in communications. In UAV-based cellular networks, UAVs are mostly equipped with small BSs to provide temporary required communication links and cover the hard-to-reach regions. These flying BSs are more adaptive, flexible, and cost-effective than conventional towers or pole-mounted or rooftop BSs [17]. However, cellular networks have limitations in terms of supporting UAV communications. For example, optimally deploying UAVs is one of the most challenging issues in this context [18; 19]. Therefore, new communication technologies such as 5G and 6G that support aerial and satellite communication are needed to manage UAV traffic in very dense air traffic scenarios [77; 103; 116; 117]. There are challenges with integrating 5G and 6G with FANETs, which can suffer from issues and is a very complex task with many technical issues which need to be addressed [66].