Space-air-ground integrated network (SAGIN), as an integration of satellite systems, aerial networks, and terrestrial communications, has been becoming an emerging architecture and attracted intensive research interest during the past years. Besides bringing significant benefits for various practical services and applications, SAGIN is also facing many unprecedented challenges due to its specific characteristics, such as heterogeneity, self-organization, and time-variability. Compared to traditional ground or satellite networks, SAGIN is affected by the limited and unbalanced network resources in all three network segments, so that it is difficult to obtain the best performances for traffic delivery. Therefore, the system integration, protocol optimization, resource management, and allocation in SAGIN is of great significance. To the best of our knowledge, we are the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the integration of all the three network segments. In light of this, we present in this paper a comprehensive review of recent research works concerning SAGIN from network design and resource allocation to performance analysis and optimization. After discussing several existing network architectures, we also point out some technology challenges and future directions.

Space-Air-Ground Integrated Network: A Survey

Multipath routing can significantly improve the network throughput and end-to-end (e2e) delay. Network coding based multipath routing removes the complicated coordination among multiple paths so that it further enhances data transmission efficiency. Traditional network coding based multipath routing protocols, however, are inefficient for Low Earth Orbit (LEO) satellite networks with the long link delay and regular network topology . Considering these characteristics, in this paper, we first formulate the multipath cooperative routing problem, then propose a Network Coding based Multipath Cooperative Routing (NCMCR) protocol for LEO satellite networks to improve the throughput. We propose source-based and destination-based multipath cooperative routing algorithms, which deliver different parts of a data flow along multiple link-disjoint paths dynamically and cooperatively. Furthermore, we design an efficient No-Stop-Wait ACK mechanism for our NCMCR protocol to accelerate the data transmission, where a source node continuously sends subsequent batches before it receives ACK messages for the batches sent previously. Under the proposed acknowledgement mechanism, we theoretically analyze the number of coded packets that should be sent and the transmission times of each batch for successfully decoding a batch. NS2-based simulation results demonstrate that our NCMCR outperforms the most related protocols.

Multipath Cooperative Routing with Efficient Acknowledgement for LEO Satellite Networks

In recent years, Small Satellite Networks (SSNs) are attracting increasing attention due to its economical prospects and advantages in high bandwidth and low latency. More and more companies and organizations are planning to construct large-scale SSNs to provide global services. In this way, the traffic pattern is more complicated because of random transmission requirements and stochastic packet generations/arrivals, and routing faces more challenges due to network scale and limited resource in small satellites. Traditional satellite routing algorithms, which attempt to exploit the predictable satellite's trajectory with time-discrete graph models, cannot handle these challenges. In this paper, we propose a novel Temporal Netgrid Model (TNM) to portray the time-varying topology of large-scale SSNs. In TNM, the whole space is divided into small cubes (i.e., netgrids) and then, satellites can be located by netgrids instead of coordinates. By doing so, we can construct a network topology for random traffic routing. Furthermore, an efficient Netgrid-based Shortest Path Routing (NSR) algorithm is proposed based on TNM; NSR attempts to find the optimal path from source netgrid to any other reachable netgrids. In this way, the routing complexity is significantly reduced. We also develop a Large-scale Satellite Network Simulator (LSNS) to validate our study. The results show that NSR achieves a significant reduction in computational complexity as well as near-optimal routing performance in terms of end-to-end delay and packet drop rate in scenarios of large-scale SSNs compared with existing satellite routing algorithms.

Temporal Netgrid Model-Based Dynamic Routing in Large-Scale Small Satellite Networks

As the typical delay tolerant networks, the satellite networks possess the intermittent connections, the large-scale time delays and the time-varying topologies. Obviously, such features seriously affect the delivery of mission data with certain requirements on the traffic or latency. To decrease the delivery time, existing relay-based contact graph routing (CGR) method selects the earliest reachable path to forward mission data. However, this method cannot guarantee the transmission of a large amount data and wastes the rare contact opportunities. Therefore, in this paper, we focus on the transmission QoS problem of multiple missions over the satellite networks, and design a QoS support routing strategy to achieve multiple flow-maximizing paths with acceptable delivery delays. Especially, with the storage time aggregated graph (STAG), we construct an on-demand mission model to depict both the network dynamic characteristics and the different mission requirements, and then reduce the QoS support problem as a graph-based maximum flow problem. To solve this problem, one STAG-based multiple flow-maximizing routing scheme is proposed to ensure the mission QoS and match the rare network resources, which has low computation complexity. Finally, compared with CGR, simulation results demonstrate the proposed scheme can achieve a higher mission completion rate and resource utilization rate.

STAG-Based QoS Support Routing Strategy for Multiple Missions Over the Satellite Networks

In remote regions (e.g., mountain and desert), cellular networks are usually sparsely deployed or unavailable. With the appearance of new applications (e.g., industrial automation and environment monitoring) in remote regions, resource-constrained terminals become unable to meet the latency requirements. Meanwhile, offloading tasks to urban terrestrial cloud (TC) via satellite link will lead to high delay. To tackle above issues, Satellite Edge Computing architecture is proposed, i.e., users can offload computing tasks to visible satellites for executing. However, existing works are usually limited to offload tasks in pure satellite networks, and make offloading decisions based on the predefined models of users. Besides, the runtime consumption of existing algorithms is rather high.In this paper, we study the task offloading problem in satellite-terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. The proposed Deep Reinforcement learning-based Task Offloading (DRTO) algorithm can accelerate learning process by adjusting the number of candidate locations. In addition, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that DRTO achieves near-optimal offloading cost performance with much less runtime consumption, which is more suitable for satellite-terrestrial network with fast fading channel.

Deep Reinforcement Learning-based Task Offloading in Satellite-Terrestrial Edge Computing Networks

Satellite systems have attracted much attention from academic and industrial communities in the past 20 years. There are many satellites that have been launched for all kinds of applications. Broadcast is a fundamental operation in satellite networks. It is frequently used for satellites self-organization, coordination and collaboration. The problem of our concern is, given an Low earth orbit (LEO) satellite network and a delay-bound, to find a broadcast schedule that delivers a message to all satellites within the delay-bound and the total number of transmissions is minimized. In this paper, we propose a broadcast routing and transmission scheduling algorithm for satellite networks. We first develop a space-time graph that models the dynamic connectivity of the satellite network as time progresses forward. The broadcast message is carried from one position to the other where it is forwarded to other recipients. A greedy algorithm with back-track search is designed to find the best transmitting nodes and the best transmission time in order to achieve the minimal number of transmissions subject to the constraint of delay-bound. Simulations have been conducted in the real LEO satellite constellations. Simulation results show good performance of our proposed scheme.

Delay-bounded and minimal transmission broadcast in LEO satellite networks

In recent years a large number of LEO (Low Earth Orbit) satellites have been launched into space for various applications, such as weather forecast, environment monitoring and military surveillance. One important task of these satellites is to collect data and download them to Earth Stations (ESs) for further processing. Since LEO satellites fly at high speeds and have very limited contact time with ESs, they may not have sufficient time to download data to the ground. Existing work focused on scheduling data downloading from satellites to the ES such that the throughput of the downloading is maximized. However, what the scheduling algorithms can do is very limited in this scenario due to the disparity of the contact time a satellite has and the amount of data it carries for downloading. In this paper, we propose a novel scheme that uses inter-satellite links (ISLs) to offload data among satellites before they contact with the ES. A satellite with large amount of data but little contact time can offload data to other satellites that have surplus contact time. By offloading data among satellites on-the-fly in space, each satellite can carry the right amount of data when it comes to download its data to the ES, such that its contact time can be fully utilized. Extensive simulations have been conducted to evaluate the effectiveness of our proposed method. The simulation results show that the data downloading throughput by using ISL data offloading can be increased significantly.

Optimal Data Downloading by Using Inter-Satellite Offloading in LEO Satellite Networks

There has been explosive growth of satellite applications in the past decades. Broadcast is a fundamental operation in satellite networks, which is required for satellites self-organization, routing discovery, coordination and collaborations. In this paper, we study the problem of delay bounded and energy efficient broadcast in satellite networks. Broadcast routing is a challenging issue in satellite networks due to highly dynamic change and intermittent connectivity of the network. We propose a method that is based on a Multi-power Level Multi-Transmission (MLMT) Space-Time Graph. The algorithm repeatedly finds the most energy efficient ‘caterpillars’ in the graph, and merges caterpillars into an energy efficient broadcast tree. Simulations have been conducted in the real satellite constellations and the results show our proposed scheme has achieved good performance compared with other broadcast algorithms.

Tao Lv

Papers

Delay-bounded and minimal transmission broadcast in LEO satellite networks

Optimal Data Downloading by Using Inter-Satellite Offloading in LEO Satellite Networks

Delay-bounded and minimal energy broadcast in satellite networks with multi-power levels