Researchers from the College of Information Science and Engineering at Hunan University have developed a novel approach to enhance data transmission and processing in maritime environments. Their work focuses on optimizing the performance of cooperative satellite-aerial-maritime Internet of Things (MIoT) networks, addressing critical challenges in low-latency data transmission and efficient resource management.
In their study, the researchers consider a cooperative satellite-aerial-MIoT network (CSAMN) designed to prioritize delay-sensitive (DS) tasks through mobile edge computing while managing delay-tolerant (DT) tasks using the store-carry-forward method. The team formulated a constrained joint optimization problem aimed at maximizing the volume of data collected by satellites while minimizing system energy consumption. This complex problem involves controlling four interdependent variables: the transmit power of unmanned aerial vehicles (UAVs) for DS tasks, the start time of DT tasks, computing resource allocation, and the offloading ratio.
To tackle this non-convex and non-linear optimization challenge, the researchers proposed a joint computation offloading and resource management (JCORM) algorithm. This algorithm leverages the Dinkelbach method and linear programming techniques to achieve optimal solutions. The results of their experiments demonstrated significant improvements. The JCORM algorithm increased the volume of data collected by up to 41.5% compared to baseline methods. Additionally, it drastically reduced computational time from a maximum of 318.21 seconds to just 0.16 seconds per experiment, making it highly suitable for real-time maritime applications.
The practical implications of this research are substantial. In the marine sector, where timely data transmission and processing are crucial, the JCORM algorithm can enhance the efficiency and reliability of MIoT networks. By optimizing resource management and reducing latency, this technology can support a wide range of applications, from environmental monitoring to maritime safety and logistics. The researchers’ innovative approach not only advances the field of maritime edge computing but also sets a new standard for data handling in cooperative networks. Read the original research paper here.