# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 5 Discussion and Open Issues  
Computational intelligence, when combined with UAVs, has improved the underlying technologies and implementations of IoT applications. Nevertheless, researchers should address the remaining challenges. Therefore, some significant issues need more attention in order to benefit from UAVs for the realization of flying ECs. These issues may be related to scientific, technological, and societal problems, including challenges regarding the technical aspects of the technology, security and privacy concerns, and standardization. The challenges concern both the UAVs themselves and EC devices. Therefore, great effort is needed to address the following challenges. Energy consumption: In any UAV-enabled IoT system, most sensors, edge devices, and drones are battery-powered and can only remain powered for a limited period. Therefore, efficient energy consumption protocols and mechanisms are essential for UAVs and IoT devices to ensure a long network lifetime. Improvement efforts should address the hardware and software architecture of the IoT components by integrating energy-harvesting solutions or efficient routing protocols. For example, AI schemes can be adopted to predict a UAV's residual energy and energy dissipation through the precise adjustment of transmission and control parameters concerning channel changes. The processing energy dissipation, communication energy, storage energy, and environmental reliability should be considered to mitigate the energy of UAVs [115; 140]. Moreover, the issues listed below should also be considered to mitigate energy consumption, to extend the on-air flying time of the UAVs employed. Interference: The communication links between UAVs and IoT devices may become disconnected due to interference, which reduces latency performance and may also affect the overall network, especially when the UAVs act as a mobile BS. Therefore, the problem of interference should be addressed, especially in urban and industrial environments, where the number of interconnected devices on the same frequency band is very dense. An AI-based solution can be applied in UAV-enabled MEC to predict the signal-to-interference ratio, based on the collected channel state information (CSI). It is then possible to decide on the most appropriate scheme to combat the impact of interference or to adjust transmission parameters in order to reduce power consumption. Interoperability: In any IoT environment, many devices are expected to handle the required tasks without any effort from the end-users. These devices try to connect and interact with each other using different protocols and communication paradigms. With this diversity, it is sometimes challenging to orchestrate the system smoothly. Therefore, further investigations are necessary to unify the controller and communication protocols to ensure interoperability among them. Mobility and path planning: UAVs require auto-maneuvering without distant human pilot intervention in random dynamic IoT applications. Therefore, many intelligent methods should be considered to enable autonomous UAV flights that include collision avoidance. AI algorithms are the most viable solutions that can be used to detect obstacles and avoid collisions [141]. For instance, DL techniques can enable learning from the gathered data, which makes their integration into UAV a significant solution. A serious effort should be considered to develop green-federated AI solutions, such as RL, to ensure accurate auto-flying decisions and path and trajectory planning [35; 132]. Scalability: The scalability challenges concern resources, applications, load balancing, and connections. Enabling high scalability requires designing efficient schemes that could effectively manage heterogeneous mobile devices and servers in a UAV-enabled MEC environment, with its highly dynamic demand behavior.