## 4 Proposed Architecture  
The proposed architecture of flying ad-hoc network based on multi-access edge computing is illustrated in Figure 2. The application scenario considered is the surveillance of a specific area, which may collect data, that is, video streaming and images. We consider two representative classes of UAVs: monitoring UAV (M-UAV) and raspberry pi-based multi-access edge computing UAV (RMEC-UAV). M-UAV perform data acquisition and monitoring only from the assigned zone. In our proposed architecture, the set of M-UAVs are assigned to one RMEC-UAV that is used to reduce the  
Figure 1: Hyper elliptic curve of genus 2, that is, g = 2.  
power consumption while executing the security mechanism (i.e., sign, verify). The set of M-UAVs allocated to RMEC-UAV is essentially subjected to the load produced by M-UAV. RMEC-UAV collects data from M-UAVs and forwards this to the base station. RMEC-UAV can also connect with the IoT devices and collect data from them. Prior to transmitting, the RMEC-UAV validates the authenticity of the M-UAVs. Upon successful validation, the RMEC-UAV forwards the data to the BS. The RMEC-UAV transmits not only the IoT data but also the flight information and the information about the role of each M-UAV. Raspberry PI (RPI) board was considered for RMEC-UAV. Even though there are other substitutions for RPI, with sophisticated hardware configurations, such as LattePanda 4G/64 GB, Qualcomm Dragon board, ODROID-XU4, and ASUS Tinker Board, among others, RPI is nonetheless considered to be the most cost-effective and energy-efficient option. Other alluring features of RPI 4 that further defend its selection are the built-in wireless networking support, that is, Wi-Fi (dual-band 802.11 b/g/n/ac) and Bluetooth 5.0 BLE. RPI 4 is equipped with a 1.5 GHz 64-bit quad core ARM Cortex-A72 processor. The 5G and 802.11 ac wireless modules are enabled on RMEC-UAV in order to link it with the BS/IoT devices and hence provide a hotspot service over the M-UAVs. Fifth generation (5G) is further classified into enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low latency communications (URLLC) by the International Telecommunication Union (ITU) in order to fulfill the requirements of diverse industrial and market demands. However, we have considered URLLC in the proposed architecture, as of it offers very high mobility, which further defends its selection for UAV-based operations [55]. The images transmitted by the monitoring UAVs, ground cameras, and the sensors, among other sources, are all received by the RMEC-UAV on-board microcontroller. The microcontroller, then, generates the tasks that will be processed by the local microcomputer, or the decision support engine (DSE). The human operator receives a decreased share of the data flow so as to decide quickly. In case the human decisions are not timely, the predictive and interpolative/extrapolative modules mounted on the RMEC-UAV DSE step in. The probability of response-delays resulting from the queues of to-be-processed jobs can never be ignored. To compensate for such time lapse and to enhance reliability, the RMEC-UAVs synergize with each other. Further, each of the M-UAVs, after being equipped with the essential gadgets, these being cameras, IMU, sensors, and a GPS unit, among others, can be accustomed to different application scenarios. The proposed architecture can be divided into the following three main layers:  
Figure 2: Proposed architecture of flying ad-hoc network with 5G and multi-access edge computing (MEC) facilities. RMEC-UAV: raspberry pi-based multi-access edge computing unmanned aerial vehicle, M-UAV: monitoring UAV, IoT: Internet of Things, URLLC: ultra-reliable low latency communications, KGC: key generation center. * **Layer 1** consists of the ground-level IoT devices that are devoted to different tasks as per application scenario. The ground-level IoT devices are connected with the RMEC-UAV and BS via URLLC, a 5G wireless link.