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

TL;DR

This paper proposes a joint optimization algorithm for satellite-aerial-MIoT networks that enhances data collection and reduces computation time, effectively supporting low-latency maritime IoT applications.

Contribution

It introduces a novel joint computation offloading and resource management algorithm tailored for cooperative satellite-aerial-MIoT networks, addressing delay constraints and system efficiency.

Findings

Data collection volume increased by up to 41.5%

Computational time reduced from 318.21s to 0.16s

Effective support for real-time maritime IoT applications

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.