### Motivation, Contributions, and Organization of the Survey  
There is a myriad of survey papers on UAVs and their roles in different domains, including agriculture, surveillance, search and rescue, etc. As discussed so far in this survey paper, their roles in wireless communications are also phenomenal due the additional degrees of freedom that they provide. In this regard, the number of studies investigating the assistance of UAVs in wireless communications is huge, and there is also a considerable amount of survey papers that bring the studies in particular perspectives together. Since energy optimization lies at the heart of UAV-assisted wireless communication networking, we intend to produce a dedicated survey paper on this topic in order to compile the research efforts in energy optimization, with three main objectives:  
* To highlight the cruciality of energy optimization in UAV-assisted wireless networking;
* To reveal the state of the art in order to understand where we currently stand;
* To identify the gaps in the literature, on which further research should focus. Furthermore, given that ML has proven to be an efficient and effective tool in wireless communications networking [38], it has been widely used in UAV-assisted networking as well [16; 35; 39]. Therefore, the application of ML in energy-efficient UAV-assisted networking is also included in this survey in order to capture the current research trend in the literature. In addition, conventional optimization algorithms, such as genetic algorithm, game theory, and particle swarm optimization, are covered in this survey since they also have a large application area in the case of energy efficient UAV-assisted networking. The primary intention of including both the conventional and ML methods is to provide a holistic approach that does not prioritize one over another, given that the corresponding works themselves have studied all types of optimization methods. Therefore, with this approach, the literature is better represented. To this end, although there are many survey papers on UAVs in the literature--some are detailed in Section 1.1, to the best of our knowledge, there is no survey paper that precisely investigates the energy optimization methods for UAV-assisted wireless communication networking by combining the conventional and ML methods. To be more specific, even though almost all the surveys include a small or large amount of content on energy consumption in UAV-assisted wireless networking [13; 18; 33; 34], none of them has the energy optimization methodology as the sole focus. The work in [18] is the closest to our survey as its primary focus is also green UAV-assisted networking; however, our survey has a number of differences:  
* The focus of our survey is not only on energy optimization in general but also on the optimization methods employed in energy efficient UAV-assisted wireless communication networking;
* We categorized the methods according to their type (i.e., conventional and ML) and investigated each energy optimization method accordingly;
* For the sake of completeness, a brief tutorial about the optimization methods is included in our survey;
* We also included the novel landing spot approach, which has gained momentum in the research community. The summary of contributions and the paper organization is as follows:
* Section 2 discusses different types of UAVs in order to reveal their characteristics and capabilities, which is quite important in selecting the UAV for a particular application;
* The power supply and charging mechanisms of UAVs are extensively covered in Section 3. This is particularly important because the optimization can be performed according to the power supply (e.g., battery, grid, fuel, renewable, hybrid), and various charging/recharging mechanisms (e.g., battery swapping, refuelling, wireless power transfer, etc.) can be placed into the optimization model. * The role of UAV-BSs in wireless communication networks is investigated in Section 4.