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Joint Computation Offloading and Resource Management for Cooperative Satellite-Aerial-Marine Internet of Things Networks

Shuang Qi, Bin Lin, Yiqin Deng, Hongyang Pan, Xu Hu

TL;DR

This work addresses the joint optimization of computation offloading and resource management in cooperative satellite-aerial-MIoT networks (CSAMN) to satisfy delay-sensitive DS tasks while enabling delay-tolerant DT data collection via store-carry-forward. It proposes the JCORM algorithm, which alternates optimization over UAV transmit power, DT start times, LEO computing allocation, and offloading ratios, guided by a Dinkelbach-based fractional programming step and closed-form subproblem solutions. The architecture leverages two data-processing modes: cooperative edge computing on UAVs/LEO for DS tasks and SCF storage/collection on LEO for DT tasks, with the LEO satellite acting as both storage and MEC server. Results show JCORM increases satellite data collection by up to 41.5% and reduces per-experiment computation time to about 0.16 seconds, highlighting its potential for real-time maritime applications with energy-aware trade-offs. Overall, the work advances unified cross-layer optimization for heterogeneous maritime services, delivering practical gains in latency, throughput, and efficiency for CSAMN deployments.

Abstract

Devices within the marine Internet of Things (MIoT) can connect to low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) to facilitate low-latency data transmission and execution, as well as enhanced-capacity data storage. However, without proper traffic handling strategy, it is still difficult to effectively meet the low-latency requirements. In this paper, we consider a cooperative satellite-aerial-MIoT network (CSAMN) for maritime edge computing and maritime data storage to prioritize delay-sensitive (DS) tasks by employing mobile edge computing, while handling delay-tolerant (DT) tasks via the store-carry-forward method. Considering the delay constraints of DS tasks, we formulate a constrained joint optimization problem of maximizing satellite-collected data volume while minimizing system energy consumption by controlling four interdependent variables, including the transmit power of UAVs for DS tasks, the start time of DT tasks, computing resource allocation, and offloading ratio. To solve this non-convex and non-linear problem, we propose a joint computation offloading and resource management (JCORM) algorithm using the Dinkelbach method and linear programming. Our results show that the volume of data collected by the proposed JCORM algorithm can be increased by up to 41.5% compared to baselines. Moreover, JCORM algorithm achieves a dramatic reduction in computational time, from a maximum of 318.21 seconds down to just 0.16 seconds per experiment, making it highly suitable for real-time maritime applications.

Joint Computation Offloading and Resource Management for Cooperative Satellite-Aerial-Marine Internet of Things Networks

TL;DR

This work addresses the joint optimization of computation offloading and resource management in cooperative satellite-aerial-MIoT networks (CSAMN) to satisfy delay-sensitive DS tasks while enabling delay-tolerant DT data collection via store-carry-forward. It proposes the JCORM algorithm, which alternates optimization over UAV transmit power, DT start times, LEO computing allocation, and offloading ratios, guided by a Dinkelbach-based fractional programming step and closed-form subproblem solutions. The architecture leverages two data-processing modes: cooperative edge computing on UAVs/LEO for DS tasks and SCF storage/collection on LEO for DT tasks, with the LEO satellite acting as both storage and MEC server. Results show JCORM increases satellite data collection by up to 41.5% and reduces per-experiment computation time to about 0.16 seconds, highlighting its potential for real-time maritime applications with energy-aware trade-offs. Overall, the work advances unified cross-layer optimization for heterogeneous maritime services, delivering practical gains in latency, throughput, and efficiency for CSAMN deployments.

Abstract

Devices within the marine Internet of Things (MIoT) can connect to low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) to facilitate low-latency data transmission and execution, as well as enhanced-capacity data storage. However, without proper traffic handling strategy, it is still difficult to effectively meet the low-latency requirements. In this paper, we consider a cooperative satellite-aerial-MIoT network (CSAMN) for maritime edge computing and maritime data storage to prioritize delay-sensitive (DS) tasks by employing mobile edge computing, while handling delay-tolerant (DT) tasks via the store-carry-forward method. Considering the delay constraints of DS tasks, we formulate a constrained joint optimization problem of maximizing satellite-collected data volume while minimizing system energy consumption by controlling four interdependent variables, including the transmit power of UAVs for DS tasks, the start time of DT tasks, computing resource allocation, and offloading ratio. To solve this non-convex and non-linear problem, we propose a joint computation offloading and resource management (JCORM) algorithm using the Dinkelbach method and linear programming. Our results show that the volume of data collected by the proposed JCORM algorithm can be increased by up to 41.5% compared to baselines. Moreover, JCORM algorithm achieves a dramatic reduction in computational time, from a maximum of 318.21 seconds down to just 0.16 seconds per experiment, making it highly suitable for real-time maritime applications.
Paper Structure (30 sections, 54 equations, 9 figures, 2 tables, 2 algorithms)

This paper contains 30 sections, 54 equations, 9 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. SAGINs
  4. SAGSINs
  5. Summary
  6. System Models
  7. Network Model
  8. Communication Models
  9. MIoT Device-UAV Communication Model
  10. UAV-LEO Satellite Communication Model
  11. Computing Model for DS Tasks
  12. Storage Models for DT Tasks
  13. DT Data Collection from DT-MDs to UAVs
  14. DT Data Collection from UAVs to LEO Satellite
  15. Energy Consumption Models
  16. ...and 15 more sections

Figures (9)

  • Figure 1: CSAMN architecture.
  • Figure 2: Tasks processing within a single time slot.
  • Figure 3: Effect of the bandwidth of LEO satellite: (a) utility, (b) total data volume, and (c) total energy consumption.
  • Figure 4: Utility with the index of time slots.
  • Figure 5: The relationship between network performance and data size of one DS-MD: (a) utility vs. data size of one DS-MD and (b) average delay of DS tasks vs. data size of one DS-MD.
  • ...and 4 more figures