To this end, some key benefits of UAV-assisted wireless networking can be summarized as follows:  
* Ubiquitous connectivity can be provided to the users as UAV-BSs are mobile and are capable of tracking the users;
* Pop-up scenarios are well managed in terms of connectivity;
* The business of mobile network operators becomes more sustainable as their capital expenditure (CAPEX) and operational expenditure (OPEX) are reduced because they do not need to deploy new fixed BSs since the same UAV-BSs can be reused in various occasions;
* Emergency scenarios can be managed more efficiently, as the UAV-BSs provide a good amount of flexibility. However, despite its myriad of benefits, there are certain design challenges for UAV-assisted networking, including energy efficiency (EE), trajectory planning, positioning, resource management, privacy, regulations, etc. [13; 15]. Of these, EE has a particular place as UAV-BSs are mostly battery-operated, therefore, their energy storage is limited. As a result, energy consumption becomes critical in order to: (1) prolong the flight time of UAV-BSs; (2) minimize CO\({}_{2}\) emission; (3) reduce CAPEX and OPEX of mobile network operators by making them require less UAV-BSs (since each can serve more) and reducing the energy costs. Each of these items is quite important during the design process of the network. Prolonging the flight time, for example, is not only instrumental in reducing the costs of mobile network operators but it is also effective in keeping the system fully operational. In an extreme case where there is very high demand, if a mobile network operator does not possesses sufficient number of UAV-BSs to replace one another once their batteries die, then there will be coverage holes and some users will be out of coverage, making the system partially down. On the other hand, if the EE is in place, then the flight time of each UAV-BS increases and that minimizes the time without connectivity for some users. This suggests that energy optimization should always be in the equation when it comes to UAV-assisted networking, meaning that even the earlier-mentioned design challenges should also be coupled with EE through multi-objective optimization models. Positioning, for instance, can be done not only to increase the number of connected users or to maximize the quality-of-service (QoS) but also to minimize the energy consumption. In other words, the optimum position of a UAV-BS can be determined in such a way that it can cover more users while consuming less power. The main takeaway here is that the energy consumption of UAV-BSs requires special attention in order to make the concept more sustainable and feasible [16; 17]. A summary of energy optimization perspectives and approaches along with their brief descriptions are given in Figure 1.  
There is an avalanche of studies in the literature about the energy optimization for UAV-assisted wireless networking [18; 19]. As the nature of each problem is quite diverse, the studies in the literature employ various types of optimization techniques, including conventional (e.g., heuristics, game theory, etc.) and recent (e.g., machine learning (ML)) ones. Even though the conventional techniques are relatively stable and robust, we have witnessed a shift towards ML-based solutions, not only in energy optimization but also in UAV-assisted networking in general [16]. This mainly originated from the fact that ML techniques offer additional benefits, such as being adaptable and dynamic, which are vital features for wireless communication networks whose environments rapidly change. On the other hand, error is an inevitable outcome of ML techniques [20], and error rate and how much error can be tolerated are important design challenges. Moreover, the availability, quality, and sufficiency of the data to be employed in training is a big issue for ML-based solutions [21; 22], overshadowing the above-mentioned advantages of ML.