Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices
As the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space–Air–Ground Integrated Netw...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/274 |
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| author | Yongnan Xu Xiangrong Tang Linyu Huang Hamid Ullah Qian Ning |
| author_facet | Yongnan Xu Xiangrong Tang Linyu Huang Hamid Ullah Qian Ning |
| author_sort | Yongnan Xu |
| collection | DOAJ |
| description | As the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space–Air–Ground Integrated Network (SAGIN). This paper discusses an uplink signal scenario in which various types of data collection sensors as IoT devices use Unmanned Aerial Vehicles (UAVs) as relays to forward signals to low-Earth-orbit satellites. Considering the fairness of resource allocation among IoT devices of the same category, our goal is to maximize the minimum uplink channel capacity for each category of IoT devices, which is a multi-objective optimization problem. Specifically, the variables include the deployment locations of UAVs, bandwidth allocation ratios, and the association between UAVs and IoT devices. To address this problem, we propose a multi-objective evolutionary algorithm that ensures fair resource distribution among multiple parties. The algorithm is validated in eight different scenario settings and compared with various traditional multi-objective optimization algorithms. The experimental results demonstrate that the proposed algorithm can achieve higher-quality Pareto fronts (PFs) and better convergence, indicating more equitable resource allocation and improved algorithmic effectiveness in addressing this issue. Moreover, these pre-prepared, high-quality solutions from PFs provide adaptability to varying requirements in signal collection scenarios. |
| format | Article |
| id | doaj-art-b8f770b656584c09b5342f41b3e5cad9 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b8f770b656584c09b5342f41b3e5cad92025-01-10T13:21:26ZengMDPI AGSensors1424-82202025-01-0125127410.3390/s25010274Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT DevicesYongnan Xu0Xiangrong Tang1Linyu Huang2Hamid Ullah3Qian Ning4College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaAs the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space–Air–Ground Integrated Network (SAGIN). This paper discusses an uplink signal scenario in which various types of data collection sensors as IoT devices use Unmanned Aerial Vehicles (UAVs) as relays to forward signals to low-Earth-orbit satellites. Considering the fairness of resource allocation among IoT devices of the same category, our goal is to maximize the minimum uplink channel capacity for each category of IoT devices, which is a multi-objective optimization problem. Specifically, the variables include the deployment locations of UAVs, bandwidth allocation ratios, and the association between UAVs and IoT devices. To address this problem, we propose a multi-objective evolutionary algorithm that ensures fair resource distribution among multiple parties. The algorithm is validated in eight different scenario settings and compared with various traditional multi-objective optimization algorithms. The experimental results demonstrate that the proposed algorithm can achieve higher-quality Pareto fronts (PFs) and better convergence, indicating more equitable resource allocation and improved algorithmic effectiveness in addressing this issue. Moreover, these pre-prepared, high-quality solutions from PFs provide adaptability to varying requirements in signal collection scenarios.https://www.mdpi.com/1424-8220/25/1/274UAVinternet of thingssystem capacityspace–air–ground integrated networkmulti-objective optimization evolutionary algorithm |
| spellingShingle | Yongnan Xu Xiangrong Tang Linyu Huang Hamid Ullah Qian Ning Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices Sensors UAV internet of things system capacity space–air–ground integrated network multi-objective optimization evolutionary algorithm |
| title | Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices |
| title_full | Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices |
| title_fullStr | Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices |
| title_full_unstemmed | Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices |
| title_short | Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices |
| title_sort | multi objective optimization for resource allocation in space air ground network with diverse iot devices |
| topic | UAV internet of things system capacity space–air–ground integrated network multi-objective optimization evolutionary algorithm |
| url | https://www.mdpi.com/1424-8220/25/1/274 |
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