Joint Optimization of Computation Offloading and Resource Allocation in ISAC-assisted SAGIN-based IoT
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911146993254400 |
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| author | Jung, Sooyeob Jeong, Seongah Kang, Jinkyu |
| author_facet | Jung, Sooyeob Jeong, Seongah Kang, Jinkyu |
| contents | In this letters, an energy-efficient integrated sensing and communication (ISAC) for space-air-ground integrated network (SAGIN)-based Internet of Things (IoT) systems is proposed to facilitate wide coverage and real-time 6G services. For processing a sizable data collected at a IoT device, a hybrid edge computing scheme is applied with the cloudlets mounted at autonomous aerial vehicle (AAV) and low earth orbit (LEO) satellite, where the AAV with multiple antennas performs uplink sensing of the nearby target. With the aim of minimizing the total AAV's energy consumption, we optimize the duration of training and data phase and the bit allocation coupled with the offloading ratio under the constraints for offloading and sensing. Via simulations, the superiority of the proposed algorithm is verified to be pronounced with the sufficient mission time and the high sensing performance constraint. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08238 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Optimization of Computation Offloading and Resource Allocation in ISAC-assisted SAGIN-based IoT Jung, Sooyeob Jeong, Seongah Kang, Jinkyu Systems and Control In this letters, an energy-efficient integrated sensing and communication (ISAC) for space-air-ground integrated network (SAGIN)-based Internet of Things (IoT) systems is proposed to facilitate wide coverage and real-time 6G services. For processing a sizable data collected at a IoT device, a hybrid edge computing scheme is applied with the cloudlets mounted at autonomous aerial vehicle (AAV) and low earth orbit (LEO) satellite, where the AAV with multiple antennas performs uplink sensing of the nearby target. With the aim of minimizing the total AAV's energy consumption, we optimize the duration of training and data phase and the bit allocation coupled with the offloading ratio under the constraints for offloading and sensing. Via simulations, the superiority of the proposed algorithm is verified to be pronounced with the sufficient mission time and the high sensing performance constraint. |
| title | Joint Optimization of Computation Offloading and Resource Allocation in ISAC-assisted SAGIN-based IoT |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.08238 |