## 1 Introduction  
By 2025, market revenue from unmanned aerial vehicles (UAVs) is expected to reach 52 billion dollars, a dramatic increase compared with today's UAV market [1, 2]. By then, UAVs will improve commercial, civil, and government sectors by introducing a wide range of heterogeneous applications [3, 4]. These applications will vary from simple environmental monitoring to complex high-security military applications. Based on release 14 of the 3 GPP and the technical reports of the ITU-R, UAVs are considered to be among the most important facets of the fifth-generation cellular system (5G/IMT2020), as well as beyond 5G and 6G networks [5, 6, 7, 8]. UAVs will be actively used for various purposes, such as military tasks, remote sensing, remote localization, target detection, transportation monitoring, and route assessment [9, 10, 11].  
Several related system architectures based on UAV-mobile edge computing (MEC) have recently been proposed, in which intensive computation tasks are offloaded from the mobile user to a richer resource located at the radio access network (RAN) edge [12, 13, 14, 15, 16]. Most of these architectures address only two layers of computation offloading in UAV-MEC systems [17, 18],although a few studies address multi-layer computation offloading [19]. Most of these architectures share the goal of minimizing energy consumption, allocating radio and computation resources efficiently, and/or meeting mobile user delay requirements.  
However, in order to support the network in which UAVs operate, several features such as low power consumption and information processing must be considered. In addition, in a large-scale environment, the connected servers at the edge network are limited by computational capacity, which leads to high delays and makes these devices unsuitable for real-time tasks. Moreover, deriving the best offloading decision in a multi-user multi-UAV wireless environment is considered a non-trivial problem.  
Confronting these challenges, we believe that constructing an energy-efficient model for a multi-user multi-UAV-enabled mobile edge-cloud computing system would be useful. Therefore, we proposed an efficient task offloading and allocation of resources model for a multi-user with multi-UAV-enabled mobile edge-cloud computing system. In addition, a combination model of resource allocation and offloading is formulated as a problem which aims to minimize the energy consumption of the whole system with a latency constraint. Moreover, a multi-UAV energy-efficient task offloading algorithm is designed to derive the task offloading decision for all device users. The main contributions of this paper include:  
* An energy-efficient system model for multi-user multi-UAV-enabled mobile edge-cloud computing. This system is a scalable system and can support increased network traffic without performance degradation;
* A network of UAVs is deployed to cover the dead and high-density network areas. In addition, UAVs can provide other applications over the smart city network in which the multilevel MEC technology is deployed to provide both the computational capabilities at the edge network as well as a core network based on SDN technology to manage the network traffic and provide an innovative interface to the network operators;
* A combination model of resource allocation and offloading is formulated as a problem which aims to minimize the energy consumption of the whole system with a latency constraint;
* Simulations validate our proposed model and demonstrate that the system's energy consumption can be significantly decreased.  
The remainder of this paper is organized as follows. Related work on computation offloading approaches is presented in Section 2. In Section 3, the system model is presented, followed by the optimization problem formulation. Then, experimental simulations are evaluated in Section 4 to demonstrate the performance improvement of our system model. Finally, the paper's conclusion is deduced in Section 5.