# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 5 Discussion and Open Issues
For example, some mobile applications, such as UAVs and autonomous driving, require high data throughput offloading to successfully provide such services, despite heterogeneity in the number of mobile devices and the dynamic demand behavior of the applications mentioned in the MEC environment. The MEC system should have an acceptable degree of scalability regarding the number of servers and services required. As a result, it is challenging for difficult areas to establish connections and communications between mobile devices and servers. To achieve convenient and cost-effective mobility and scalability, resulting in continuous service, it is necessary to have a stable connection, even in adverse environmental conditions. Security: Drones enable ubiquitous services on an IoT scale. The communications and controls are realized through P2P wireless transmissions and thus are open to many security threats. Therefore, unauthorized connections threaten the system's privacy. It is necessary to propose and design security mechanisms to improve UAV-enabled MEC systems' security [142]. The effort should focus on the physical, application, and MAC layers. The solutions should integrate recent robust techniques such as blockchains, especially in IoT applications where UAVs are expected to facilitate communication networks. The combination of UAVs with blockchain and AI is now an exciting avenue of research to predict all sorts of attacks and malware that threaten the system's privacy [127]. However, while there has been significant progress in this area, more work needs to be done. Power consumption might be a critical constraint for blockchain-based applications as they need more computing power. Increasingly complex attacks, such as quantum attacks, ML, and theory-based game attacks, could challenge the privacy of blockchain systems [143]. More research is needed to make private blockchain networks more immutable and secure in UAV-enabled MEC architectures. Moreover, blockchain-enabled softwarization for secure UAV network systems is also a critical challenge. Blockchain implementation in UAVsystems presents a complex system integration problem requiring rigorous testing before concrete implementation [12]. Integration of AI schemes: Recently, the utilization of AI schemes among DL, ML, and RL has become increasingly of interest, to benefit from their powerful performance. AI schemes are being integrated into UAV-enabled MEC systems as a way to make contextual decisions and learn from aggregated data gathered from previous tracking experiences. The data collected by the sensors in real-time can be profitably exploited for the autonomous piloting of UAVs. Therefore, processing tasks demand strict computing and storage requirements at computing facilities, to be executed exceptionally at the edge. UAVs also have energy and storage limitations; thus, portable and lightweight RL, ML, and DL algorithms should be designed to cope with these constraints. Regulations: Despite the rapid emergence of this technology, UAV regulations are still in their infancy, and a lack of heterogeneity of national rules and varying levels of implementation can be observed. These regulations differ from state to state, and the type of regulation is based on the requirements of that use. Several problems are associated with UAV regulation, including delays in flight approval and poorly documented administrative processes limiting the desired flexibility, preventing widespread use of the technology [144]. Latency and computation issues: Communication latency and computing optimization remain concerns in MEC-enabled UAV applications, affecting transmission efficiency and reaction time under communication and computing constraints. UAV transmission success may be accomplished by offering effective methods that decrease response delay. In one study [145], for example, stochastic geometry and queueing theory approaches were studied to minimize the response delay for a MEC-enabled UAV network.