# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
###### Abstract
As a result, MEC servers can deliver low latency and secure services, while saving energy for mobile users. Recently, UAVs have been integrated as air-ground equipment to fulfill the processing and storage needs at IoT network edges. As a promising solution, they could provide ultra-low latency support for latency-sensitive applications, facilitate the distribution of big data processing and analysis, improve the scalability and management of massive mobile connectivity, as well as content caching and mobile delivery [10]. The advantage presented by their flexible mobility has facilitated the integration of UAVs with MEC services to provide UAV-enabled MEC networks. UAV-enabled MEC is typically deployed in unpredictable environments such as in wild, desert, and complex landscapes. Furthermore, the computation performance may be adapted based on the distance that UAVs can smoothly control. This combination of UAV technology with MEC makes the system more advantageous and flexible than traditional MEC systems, which are constrained by high deployment costs due to their fixed architecture. In addition, the integration of UAV-enabled MEC with the most recent security approaches, such as the blockchain structure, increases the security level [11]. The blockchain is a distributed ledger that gives a secure, immutable, transparent, and trustworthy environment for the UAVs to store their data as a transaction in a chain of blocks [12]. This is a viable solution to mitigate the security and privacy issues related to UAV networks. Therefore, integrating blockchain into UAVs ensures the MEC system's trustworthiness with the additional, advantageous features of automating data transaction, decision-making, storage, and verification [13]. Therefore, UAVs can play a crucial role in enabling MEC capability in IoT applications. However, many critical issues have yet to be addressed to improve the performance of UAV-enabled MEC. Most of the impairments are related to drones as they are autonomous and battery-powered, with limited resources. Furthermore, given the broadcast nature of communications between ground-based users and UAVs, many performance aspects should be addressed, including security, task offloading, energy consumption, resource allocation, computing capacity, and user latency performance under various channel circumstances. Since this topic is revolutionary in the IoT research field, reviewing the current scientific achievements and technological limitations of UAV-enabled MEC networks is of paramount importance. Furthermore, the limits of existing surveys on UAV-enabled MEC in IoT applications have motivated us to put forward a detailed review. This paper explores the use of UAVs in emerging IoT applications and the utility of both deep learning (DL) and machine learning (ML) in UAV-enabled MEC systems. For instance, the authors of [14] have elaborated a review about the energy efficiency constraints related to UAV-enabled MEC systems for IoT devices. Additionally, the authors of [15; 16] have presented a survey about communication and networking technologies for UAVs. The research work presented in [17] has addressed both fundamental and recent advances of computation offloading in UAV-enabled MEC. However, only a few of the challenges are mentioned in the above works, including energy, communication facilities, and limited resource allocation issues. In this paper, the most critical issues and challenges of UAV-enabled MEC based on AI are accurately outlined and discussed. Indeed, there are several recent reviews about the UAV-enabled MEC paradigm. However, most of the previous reviews have focused on UAV energy efficiency, communication technologies connecting various UAVs, and computation offloading in UAV-enabled MEC. Our review considers a more practical and recent scenario wherein massively generated data need to be processed and analyzed using AI algorithms for decision-making at the edge of networks. This has not been considered in the previous works.