The Shortest Distance Path (SDP) problem is a critical routing issue in communication networks, particularly in satellite networks. Typically, SDP is solved by graph-based iterative algorithms, while an explicit or analytic approach is challenging. However, considering the orbit dynamics and topology regularity, this paper proposes, for the first time, an explicit analytic phase-based algorithm STEPCLIMB to directly solve the SDP in low-Earth orbit (LEO) satellite networks. Based on the relationship between satellite phase and inter-satellite link distance, the SDP is modeled with the satellite phase, and SDP problem is converted into a total phase offset problem through theoretical derivations. Then STEPCLIMB is derived in two cases, respectively. Monte-Carlo simulations verify STEPCLIMB’s accuracy, which has zero error in the mono-valley case and has less than 0.1% error in the bi-valley case. The algorithm performs better in larger-scale constellations and can save over 99.4% computational cost compared to Dijkstra algorithm. Further, the SDP pattern and features in Starlink constellation are analyzed. The model proves that most inter-plane hops in the SDP occur successively, and the simulations further indicate that these hops prefer satellites in the higher latitude regions.

Software-defined networking (SDN) logically separates the control and data planes, thus opening the way to more flexible configurations and management of low-Earth orbit (LEO) satellite networks. Since one or, more generally, multiple distributed controllers are needed, a significant challenge in SDN is the controller placement problem (CPP). Due to characteristics such as the dynamic network topology, limited bandwidth and traffic variations, the CPP is quite complex in SDN-based satellite networks. In this paper, we propose solving the CPP by means of a static placement with dynamic assignment (SPDA) method for LEO satellite networks. The SPDA method has two parts: the first is to incorporate SDN controllers into some fixed satellites by formulating a mixed integer programming model; the second is to dynamically assign switches to existing controllers according to the switch-controller latency and the traffic load of controllers. The SPDA method takes the topological dynamics into account by effectively dividing time snapshots, and it has a lower bandwidth consumption compared with methods involving controller migrations. Real satellite constellations are used to evaluate the performance of our controller placement solution. The results show that SPDA outperforms existing methods in terms of reducing the switch-controller latency, and it also has good load balancing performance.

With the establishment of satellite constellations equipped with ship automatic identification system (AIS) receivers, the amount of AIS data is continuously increasing, and AIS data have become an important part of ocean big data. To further improve the ability to use AIS data for maritime surveillance, it is necessary to explore a ship classification and anomaly detection method suitable for spaceborne AIS data. Therefore, this paper proposes a ship classification and anomaly detection method based on machine learning that considers ship behavior characteristics for spaceborne AIS data. In view of the characteristics of different types of ships, this paper introduces the extraction and analysis of ship behavior characteristics in addition to traditional geometric features and discusses the ability of the proposed method for ship classification and anomaly detection. The experimental results show that the classification accuracy of the five types of ships can reach 92.70%, and the system can achieve better results in the other classification evaluation metrics by considering the ship behavior characteristics. In addition, this method can accurately detect anomalous ships, which further proves the effectiveness and feasibility of the proposed method.

Mega-constellation networks widely employ inter-satellite links (ISLs) to enhance network connectivity. However, the ISLs, particularly the inter-plane ISLs that span different orbital planes, are dynamic and difficult to maintain. The inter-plane ISLs are less used and can be partly saved. Recently, a special ISL configuration with pruned inter-plane ISLs has been implemented in Starlink, yet less studied. This paper delves into the topological characteristics and routing issues of this pruned ISL pattern, specifically the 3-ISL topology. This topology is mathematically modeled and indexed, and key routing properties are derived, including the minimum hop-count, network diameter, network cost, and average path length of the 3-ISL topology. These fundamental properties are rigorously proven for both Mesh and Torus topologies. Additionally, the shortest path searching region and path diversity are deduced. Numerical simulations verify these propositions, and the results of mega-constellation scenarios suggest that pruning ISLs could lead to increased path distance, higher delay, and reduced network capacity. However, in certain constellation configurations, some metrics are comparable to those of the traditional 4-ISL topology, thereby potentially reducing system costs. Constellations with fewer orbital planes and more satellites in each plane are less affected by pruning ISLs and thus preferred. The findings can provide analytic insights for routing strategies and guide the integrated design of constellation configuration and ISL topology.

Megaconstellation networks, represented by Starlink and OneWeb, have become a promising solution for the wide-area Internet of Things (IoT). IoT messages collected by the satellite can be routed to the ground gateway via multiple intersatellite link (ISL) relays and then access the ground network. Compared to traditional constellations, megaconstellations with massive satellites require more ISL relays that are greatly affected by the number and location of ground gateways. In this article, we focus on the ISL usage for connecting satellites and gateways and propose a novel method with low computation cost to evaluate the ISL usage metric. Then, we formulate a mixed-integer optimization model for the gateway site optimization (GSO) problem with minimizing the overall ISL usage, which is then simplified through model transformation. An IBD-PSO algorithm is proposed to solve the transformed GSO problem. Based on the Starlink constellation, simulation results have verified the proposed method by comparisons with previous studies. We further investigate how the gateway placement is affected by the gateway number and traffic demand pattern. The relations between gateway placement and ISL hop count of different satellites are also studied.