## 1 Introduction
Regarding reference [26], a weighted function was optimized, which, due to the fixed weights between objectives, was not adaptable to scenarios where the requirements between objectives vary. Therefore, this article simultaneously considers the aforementioned elements, aiming to delve into the challenges confronted by IoRT in terms of resource allocation fairness, demand-time variability, and diversity. This study encompasses a scenario involving multiple types of sensors, functioning as IoT devices, in data collection and uplink transmission. UAVs function as forwarding relays to transmit data collected by IoT devices to LEO satellites. By optimizing communication resources, including the deployment coordinates of drones, bandwidth allocation, and the association between drones and IoT devices, the minimum channel capacity of various IoT device types is maximized. Given limited communication resources, optimizing the channel capacity of different categories of IoT devices becomes a conflicting multi-objective optimization problem. Considering the involved variables, the problem also qualifies as a mixed-integer multi-objective optimization problem. In recent years, the MOEA/D algorithm has found widespread application in addressing diverse engineering problems with conflicting goals, spanning artificial intelligence [34; 35], power engineering [36; 37], the internet of vehicles [38], and more. To address this problem, this paper proposes an evolutionary algorithm for resource allocation based on the MOEA/D algorithm. This algorithm can generate a series of resource allocation solutions tailored to different scenarios of changing channel capacity demands for ground terminals. To the best of our knowledge, this type of scenario exploration has not been studied before. The main contributions of this paper are summarized as follows:  
1. We conducted simulations on a SAGIN model, encompassing the ground IoT terminal-UAV layer and the UAV-LEO satellite layer. Diverse categories of IoT devices relay collected data to LEO satellites through drone relays. Within this model, we formulated a mixed-integer multi-objective optimization problem, wherein the deployment location of drones, bandwidth allocation, and connection selection between IoT devices and drones are collectively optimized. This optimization, in turn, maximizes the minimum channel capacity of various types of IoT devices. 2. To address this non-convex problem, we propose a multi-objective evolutionary algorithm based on MOEA/D. The enhanced algorithm efficiently addresses the issue of equitable resource allocation among IoT devices of the same category within the SAGIN framework, while also exhibiting adaptability to the dynamic demand variations characteristic across various IoT device types. 3. The robustness and superiority of the proposed algorithm are substantiated. We devised various scenarios of IoT device distribution, altering the number of IoT device types and UAV relays to validate the algorithm's robustness. Additionally, the algorithm was compared with three other classic multi-objective optimization algorithms, affirming its superiority. The remainder of the article is structured as follows: Some related works are presented in Section 2. In Section 3, we present the SAGIN model and articulate the multi-objective optimization problem through formulas. The algorithm proposed, elucidated in Section 4, attains fair and adaptive resource allocation for various IoT device types. Section 5 presents an overview of the proposed algorithm. Section 6 outlines the simulation experiments employed to validate the robustness and superiority of the proposed algorithm, accompanied by an analysis of the experimental results. Lastly, Section 7 provides a summary of the entire paper.