## 1 Introduction  
With the continuous maturation of Internet of Things (IoT) technology, the application of IoT sensors is becoming more prevalent [1, 2]. A myriad of IoT devices are deployed globally, endowed with reconnaissance and monitoring functionalities, extensively applied in domains such as fire monitoring [3, 4], ocean exploration [5, 6], and wilderness search and rescue [7, 8]. The smooth operation of these applications requires a reliable network coverage and sufficient channel rates. This is crucial because the data being transmitted often need to be conveyed to remote data processing centers [1]. However, the existing communication system-reliant base stations on the ground fall short of achieving global signal coverage. In regions such as the ocean, deserts, and areas afflicted by impaired or insufficient ground network facilities, data transmission becomes impeded and constrained. To address the signal coverage challenges of IoT sensors in remote areas (IoRT), the integration of a Space-Air-Ground Network (SAGIN) has emerged as a predominant solution [9; 10]. SAGIN involves the integration of diverse network resources, including ground networks, air networks, and satellite networks, into a unified system. This integration enables seamless connectivity and global-scale information transmission [11]. The overarching goal of this integrated network architecture is to enhance communication efficiency, stability, and coverage, thereby catering to the escalating demand for data transmission [12]. In existing research, air network nodes are commonly categorized into low-altitude platform (LAP) and high-altitude platform (HAP) [13]. LAP comprises airships, aircraft, and drones, while HAP encompasses balloons, solar-powered aircraft, and similar platforms. Notably, UAV-based LAPs offer advantages such as low energy consumption [14], high mobility [15], and extensive coverage [16]. Utilizing UAV-based relays can also establish line-of-sight (LoS) communication links for ground terminals. Space network nodes typically consist of low-Earth-orbit (LEO) satellites, medium-Earth-orbit (MEO) satellites, and geostationary-Earth-orbit (GEO) satellites. Among these, LEO satellites, characterized by the lowest altitude, offer advantages such as low latency and minimal propagation loss compared to MEO and GEO satellites. Recent research works have focused significantly on improving signal coverage for terminal devices by integrating air and space communication layers and optimizing service quality. These research studies cover a range of aspects, including energy efficiency [17; 18; 19; 20], bandwidth utilization efficiency [21; 22], coverage range [23], communication security [24; 25], and other parameters. Moreover, there is also a multitude of studies [26; 27; 28; 29; 30; 31; 32; 33] dedicated to enhancing system channel capacity, focusing on optimizing resource allocation within SAGIN to subsequently optimize the channel capacity for overall systems or devices. For instance, [PERSON] et al. [29] proposed a SAG-IoRT framework, where the authors jointly optimized UAV trajectory, smart device connection scheduling, and power control to enhance system capacity, addressing the challenges posed by the limited transmission power of remote devices. Furthermore, [PERSON] et al. [26] presented an integrated UAV-LEO data collection framework for B5G Internet of Remote Things networks, focusing on optimizing IoT device bandwidth allocation, UAV 3D trajectory, and UAV power allocation to enhance data collection efficiency and minimize energy consumption. More descriptions in the literature are presented in the section about related works. However, we observed some oversights in these studies. For instance, reference [29] optimized the overall system channel capacity, neglecting the varying demands and fairness among ground IoT devices, and it failed to rapidly adjust the resource allocation scheme in response to changes in ground demand.