# Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
## 1 Introduction
### Related Works  
At present, many experts and scholars have carried out research on resource optimization in SAG-IoT network. [PERSON] et al. [9] proposed a resource allocation strategy for UAV networks within the context of a space-air-ground heterogeneous communication system. They investigated a two-stage joint hovering height and power control scheme to maximize system throughput. The feasible deployment scheme of UAV was given by Lagrangian dual decomposition and concave-convex process method. The authors in [10] studied the computation task scheduling problem in the SAGIN and designed a reinforcement learning algorithm based on deep deterministic policy gradient. By jointly optimizing task scheduling and UAVs trajectories, the algorithm minimizes task processing delays. A space-air-ground remote Internet of Things (SAG-IoRT) framework in which UAVs acted as relay stations was proposed in [11]. The joint optimization problem of smart device connection scheduling, power control, and UAV trajectory was solved to maximize system capacity by variable substitution, continuous convex optimization, and block coordinate descent algorithms. In addition to improving system throughput, system energy consumption has also attracted more and more attention, and improving energy efficiency has gradually become a research hotspot. An Internet of Things system with limited airborne energy was studied in [12]. The study considered the probability of the line-of-sight channel and planned the trajectory of a UAV to optimize the transmission scheduling between ground equipment and the UAV. [PERSON] et al. proposed a double deep Q-network-based RA framework, which can improve energy efficiency while ensuring a certain throughput in UAV-assisted terrestrial networks in [13]. And [PERSON] et al. proposed a novel multi-UAV-assisted multi-access MEC model, which can effectively reduce the energy consumption of users and UAVs in [14]. They jointly optimized the bit allocation, transmit power, CPU frequency, bandwidth allocation and UAVs' trajectories to minimize the weighted sum energy consumption of UAVs and users. [PERSON] et al. [15] proposed a layered SAG-IoRT network design, which maximized data capacity at the IoT-UAV layer, and considered data collection and energy consumption at the UAV-satellite layer. A approach based on block coordinate descent (BCD) algorithm was used to optimize bandwidth allocation, UAV power, trajectory, and satellite selection to solve the two-level optimization problem. The distance between UAVs and satellites is very large, so a large amount of bandwidth needs to be allocated to communication links for data transmission, which occupies a significant portion of limited spectrum resources. With the goal of maximizing the spectral efficiency of the system, [PERSON] et al. (2016) focused on cross-layer data transmission in SAGIN. The joint optimization of gateway selection, resource allocation, and UAV deployment was achieved using Dinkelbach, simulated annealing, and successive convex approximation (SCA) methods. In fact, improving the spectral efficiency of the system could potentially reduce the energy efficiency of the system. Thus, it is essential to comprehensively consider both SE and EE of the system for a compromise optimization approach ([PERSON] et al., 2017). At present, most of the existing papers related to the space-air-ground network only consider optimizing one aspect of performance, but there are also a few papers that focus on multi-objective optimization. For example. [PERSON] et al. (2018) provided a multi-objective optimization framework for SAG networks, and provided simple examples with total throughput and total latency as objective functions. A series of future research challenges were identified and a potential solution was emphasized in the paper. The authors focused on the tradeoff between energy efficiency and spectral efficiency in ([PERSON] et al., 2018).