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Mobile edge computing (MEC)-enabled satellite–terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications' tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite's MEC server are insufficient, but the computing resources of neighboring satellites' MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC)-enabled satellite–terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications’ tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite’s MEC server are insufficient, but the computing resources of neighboring satellites’ MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes.

The explosive growth of various services boosts the innovation and development in terrestrial communication systems for the implementation of the next generation mobile communication networks. However, simply utilizing limited resources in terrestrial communication networks is difficult to support the massive quality of service (QoS) aware requirements and it is hard to guarantee seamless coverage in far remote regions. Leveraging the intrinsic merits of high altitude and the ability of multicasting or broadcasting, satellite communication systems provide an opportunity for novel mobile communication networks with its tight interaction and complementary characteristics to traditional terrestrial networks. It is believed that the convergence of satellite and terrestrial networks can solve the problems existing in current mobile communication systems and make a profound effect on global information dissemination. In this paper, we make a comprehensive survey on the convergence of satellite and terrestrial networks. First, motivations and requirements of satellite-terrestrial network convergence are identified. Then, we summarize related architectures of existing literature, classify the taxonomy of researches on satellite-terrestrial networks, and present the performance evaluation works in different satellite-terrestrial networks. After that, the state-of-the-art of standardization, projects and the key application areas of satellite-terrestrial networks are also reviewed. Finally, we conclude the survey by highlighting the open issues and future directions.

With the development of satellite networks, there is an emerging trend to integrate satellite networks with terrestrial networks, called satellite-terrestrial networks (STNs). The improvements of STNs need innovative information and communication technologies, such as networking, caching, and computing. In this paper, we propose a software-defined STN to manage and orchestrate networking, caching, and computing resources jointly. We formulate the joint resources allocation problem as a joint optimization problem, and use a deep Q-learning approach to solve it. Simulation results show the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]