## 2 Related Works  
Recent studies have placed considerable emphasis on enhancing the signal reach for end-user devices through the integration of terrestrial and space-based communication systems and the optimization of service quality. [PERSON] et al. [17] presented a joint design of trajectory and resource allocation for energy-efficient secure UAV communication systems, optimizing the performance in the presence of multiple users and eavesdroppers through a non-convex optimization framework. The article [19] by [PERSON] et al. investigated the optimization of UAV trajectory and data collection, assisted by LEO satellites, for energy-efficient operations in the context of 6G aerial access networks serving the IoRT. [PERSON] et al. [21] proposed a joint optimization approach for gateway selection and resource allocation to enhance spectral efficiency in an SAG-IoRT network, addressing the challenges of cross-tier communication with UAVs as relays. [PERSON] et al. [23] investigated the enhancement of maritime coverage through the integration of UAVs with hybrid satellite-terrestrial networks, optimizing UAV trajectory and transmit power to maximize the minimum ergodic achievable rate under various constraints. Some studies have concentrated on the allocation of communication resources, with a predominant focus on maximizing channel capacity optimization. For instance, [PERSON] et al. [26] focused on optimizing the trajectory and transmission power of UAVs, as well as selecting Earth-orbit satellites. They aimed to jointly optimize a weighted objective function, considering the total data uploaded by UAVs, energy consumption, and the overall average channel capacity of IoT devices. The optimization process involved the use of successive convex approximation and block coordinate descent techniques for effective problem resolution. [PERSON] et al. [27], with the goal of maximizing the total system throughput, determined the optimal probability of employing the SAGIN transmission scheme for ground terminals. In another study [28], a SAGIN framework based on a HAP as an air relay was proposed, with optimized resource allocation and HAP placement to maximize throughput for ground users. Addressing the maximization of channel capacity per unit timeslot, the work in [29] iteratively optimized connection scheduling, transmitting power, and UAV trajectory for ground intelligent terminals. [PERSON] et al. [30] studied optimal three-dimensional trajectory design and resource allocation for UAVs to maximize the total system throughput within a given time period. Considering user fairness, reference [31] aimed to maximize the minimum throughput of users serviced by UAVs. In [32], a dual-objective optimization problem for a SAGIN scenario was proposed, considering both channel capacity consumption and maximization of ground terminal network coverage. A multi-objective evolutionary algorithm was adopted for its solution. Similarly, [PERSON] et al. [39] employed airships for ground observation missions, utilizing a multi-objective evolutionary algorithm based on decomposition (MOEA/D) [40], dynamic weight vector (DW), and stable matching (STM) schemes to design airship deployment parameters. [PERSON] et al. [41] built an Earth observation network platform using an airship and designed a multi-objective evolutionary algorithm to simultaneously optimize coverage and observation benefits. These related works are compiled in Table 1.  
These studies focusing on optimizing channel capacity do not simultaneously account for the following factors:  
1. Equity in the allocation of communication resources among devices is not considered. Current methods optimize resource allocation based on the channel capacity of all IoT devices, potentially leading to substantial discrepancies in channel capacity among devices and inadequate capacities for certain IoT devices. 2. Variations in the demand for channel capacity among different device types are overlooked.