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

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 article, we consider a cooperative satellite–aerial–MIoT network (CSAMN) for maritime edge computing and maritime data storage to prioritize delay-sensitive (DS) tasks using mobile edge computing (MEC), 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 nonconvex and nonlinear 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 with baselines. Moreover, JCORM algorithm achieves a dramatic reduction in computational time, from a maximum of 318.21 s down to just 0.16 s per experiment, making it highly suitable for real-time maritime applications.