A distributed delay-constraint routing algorithm for satellite networks

Satellite communication networks, as an effective complement to terrestrial communication networks, enable global coverage communications. The Low-orbit satellite network has the characteristics of low transmission delay, short operation period and global coverage communication, it has become a popular research direction in satellite communication networks. With the continuous development of digital and intelligent communication in the world, satellite network services continue to expand, and higher requirements are placed on network transmission performance. The traditional single-structure low-orbit satellite network using centralized routing has been unable to meet current needs.. Therefore, with the low-orbit satellite network as the core, people have conducted in-depth research on satellite communication network routing algorithms that meet the requirements of high-quality, high-efficiency and largecapacity communication. Especially in the three hot issues of low-orbit satellite network routing algorithms, a lot of research results have emerged, including: QoS-based artificial intelligence algorithm routing algorithm; multi-layer satellite network routing algorithm; satellite network multipath routing algorithm. Through the analysis and discussion of these problems and achievements, the idea of combining the three algorithms on demand is proposed, and the challenges are analyzed.

The increasing demand for ultra-low-latency and high-throughput communication necessitates network architectures capable of ensuring reliable real-time connectivity. To address this requirement, integrated architectures combining terrestrial networks (TN) and non-terrestrial networks (NTN)—particularly hierarchical multi-layer satellite systems composed of geostationary Earth orbit (GEO), medium Earth orbit (MEO), and low Earth orbit (LEO) satellites—have attracted considerable attention. Such hierarchical systems offer global coverage, low latency, and high capacity. However, their highly dynamic topology, fast-moving satellites, and fluctuating link quality pose significant challenges to establishing efficient end-to-end routing paths. Traditional routing methods based on static paths or centralized control lack the adaptability required for such environments. To address these challenges, this paper introduces a neural hierarchical reinforcement learning (HRL)-based routing algorithm for satellite networks. The framework exploits the layered structure of GEO, MEO, and LEO satellites, enabling gateway-assisted routing and adaptive path selection. Control responsibilities are distributed across orbital layers: the GEO layer allocates hop-count budgets, the MEO layer selects feasible LEO paths, and the LEO layer forwards data packets. When inter-satellite links are unavailable, terrestrial gateways provide alternative routes. Routing decisions further account for satellite-to-satellite and satellite-to-ground link quality, with an end-to-end delay-based reward function capturing transmission, processing, and propagation effects. Simulation results show that the proposed neural hierarchical framework, supported by integrated terrestrial and non-terrestrial networking, achieves faster convergence, greater route stability, and better adaptability than conventional routing algorithms and HRL approaches.

The performance of satellite networks largely depends on their routing algorithm. Existing routing algorithms for satellite networks usually are not quite adaptive or have the disadvantage of a large consumption in both computation and storage resources. In this paper, we propose a novel routing algorithm based on dynamic clustering for LEO satellite networks. By dynamic forming cluster of the satellite network, the routing algorithm is both adaptive, computational effective and need much less storage space. Simulation results show that our routing algorithm performs better and are more robust when the network topology changes.

More and more network operators plan to use broadband satellite systems as part of their global broadband multimedia infrastructure because of their large geographic coverage, inherent broadcast capabilities, and fast deployment features. The recent advances on Ka-Band transmission, fast on-board switching and spot beam technologies mean that satellites can be employed efficiently to help provide high-speed, global connectivity to users all around the world. Satellite systems are multiple access systems with limited transmission capacity, very limited on-board buffering facilities, and long propagation delays compared to terrestrial network. Because of the limited on-board buffer space, high packet loss could be expected on board the satellite if no special protections were built-in. End-to-end resource management in on-board processing (OBP) satellite networks is key to provide fair and efficient sharing of traffic resources while delivering acceptable Quality of Service (QoS) to users. In this paper, we propose a Weighted Fair Bandwidth-on-Demand (WFBoD) process, which is a Demand Assignment Multiple Access (DAMA) based resource management protocol. It allows the network operator to implement various levels of service segregation by providing both absolute and differential QoS to the end-users by sharing the transmission resources fairly and efficiently. We formulate the problems associated with the implementation of WFBoD in OBP geostationary satellite networks, and present some simulation results in a Bent-Pipe satellite network scenario.

This paper presents a Constraint-Based Routing (CBR) algorithm for Multiprotocol Label Switching (MPLS) network which is designed for mapping among any layer-2 protocols and any layer-3 protocols of the OSI model. The MPLS network has capability of routing with some specific constraints for supporting desire Quality of Service (QoS). The most important constraint is the demand bandwidth. The efficient routing algorithm should have low percentage of call-setup request blocking, high network utilization and low calculation complexity. There are many previously proposed algorithms such as minimum hop algorithm, widest shortest path algorithm, and minimum interference routing algorithm. In this paper, a new algorithm is proposed. The proposed algorithm is based on the weight calculation of an amounts of paths routed over each links in the network. From the simulation results, the proposed algorithm has well performance over high network utilization, low blocking percentage and low complexity.

Aiming at the problems of high dynamic topology and limited on-board resources in Low Earth Orbit (LEO) satellite networks leading to poor performance of traditional routing algorithms, this paper combines the centralized control idea of Software-Defined Networking (SDN) with the collective learning ability of the Particle Swarm Optimization (PSO) algorithm to propose an adaptive potential field routing algorithm based on PSO. This algorithm models the network as a dynamic potential field, defines potential energy based on the cumulative congestion degree to guide data packet transmission; adaptively learns and optimizes link cost weights through the PSO mechanism, achieving dynamic evolution of the path evaluation criterion; and adopts a probability-based multi-path forwarding mechanism to achieve load balancing. Simulation results show that compared to traditional algorithms such as Dijkstra and ACO (Ant Colony Optimization), this algorithm significantly improves performance in terms of average end-to-end delay, network throughput, and load balancing, especially suitable for dynamic traffic scenarios.

Several IP-based routing algorithms have been developed for low-Earth orbit (LEO) satellite networks in recent years. The performance of the satellite IP networks can be improved drastically if multiple satellite constellations are used in the architecture. In this work, a multilayered satellite IP network is introduced that consists of LEO, medium-Earth orbit (MEO), and geostationary Earth orbit (GEO) satellites. A new multilayered satellite routing algorithm (MLSR) is developed that calculates routing tables efficiently using the collected delay measurements. The performance of the multilayered satellite network and MLSR is evaluated through simulations and analysis.

Localised quality of service (QoS) routing has recently been proposed as a viable alternative approach to traditional QoS routing algorithms that use global state information. In this approach, problems associated with maintaining global state information are avoided by having the source nodes to infer the network QoS state based on flow blocking statistics collected locally, and perform flow routing using this localised view of the network QoS state. A credit based routing algorithm (CBR) which is a simple yet effective localised QoS routing algorithm is introduced. Its performance is compared against the localised proportional sticky routing (PSR) algorithm using different types of network topologies, QoS requirements and traffic patterns and under a wide range of traffic loads. Extensive simulations show that the algorithm outperforms the PSR scheme while maintaining the same level of adaptivity and stability. Its performance is also compared against the global widest-shortest path algorithm (WSP) and it is shown that CBR gives a comparable performance with better time complexity and very low communication overhead.

Uneven traffic demand in satellite networks can lead to link congestion and influence network communication quality. To tackle this problem, we propose a state‐aware routing algorithm based on traffic prediction to disperse network traffic. Reducing the onboard processing requirements, the routing algorithm in this paper introduces a software‐defined network to decouple the control plane and data plane of the satellite network. Also, to better sense the trend of network state, the routing algorithm devises a Spatial‐Temporal Attention Fusion Graph Neural Network (STAFGNN) to predict the future network state by capturing the spatial‐temporal correlation of satellite network traffic. In the control plane, the routing algorithm senses the network state by predicting the future satellite traffic and aggregates the real‐time states of neighboring satellites to identify the load level of the zone. The data plane forwards data based on the control information. Based on the routing algorithm in this paper, multipath routing is designed to reduce the probability of link congestion by dispersing each end‐to‐end traffic. The simulation results demonstrate that the routing algorithm in this paper reduces the number of congestion links and balances the load distribution. The proposed algorithm provides an idea to introduce deep learning to solve the load balancing problem in satellite networks.

Load balancing-based hierarchical routing algorithm for wireless multimedia sensor networks

For multimedia applications, the routing algorithms should consider many Quality of Service (QoS) parameters. However, to find routes with two or more QoS parameters is an NP-hard problem. Therefore, recently many researchers are trying to use heuristic methods such as Genetic Algorithm (GA). In a previous work, a QoS multicast routing based on GA was proposed. However, in this algorithm, the multicast routing falls in the local minimum and cannot satisfy the application requirements. In this paper, we propose a new QoS multicast routing protocol, which has better genetic operations than the conventional algorithm, resulting in a better performance.

Two quasi-static minimax open shortest path first (OSPF) routing algorithms in networks supporting the Switched Multi-megabit Data Service (SMDS) are presented and compared. In OSPF routing, the network is modeled as a graph and each link is associated with a nonnegative arc weight. A shortest path spanning tree is calculated for each origin to carry both the individually addressed and the group addressed (multicast) traffic. The OSPF routing protocol is adopted as a major part of the default inter-switching system interface (ISSI) routing algorithm for SMDS networks where arc weights are inversely proportional to the aggregate link set capacities. The problem of choosing a set of link set metrics is considered so that the maximum link utilization factor is minimized in an SMDS network. The problem is formulated as a nonlinear mixed integer programming problem.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In position-based routing algorithms for ad-hoc networks, the nodes use the geographical information to make the routing decisions. Recent research in this field primarily addresses such routing algorithms in two dimensional space (2D). However, in real applications, nodes may be distributed in 3D space. In this paper we extend previous randomized routing algorithms from 2D space to 3D space, and we propose two new position-based routing algorithms that combine randomized AB3D routing algorithms with a deterministic CFace (coordinate face) algorithm. The first algorithm AB3D-CFace(1)-AB3D starts with AB3D routing algorithm until a local minimum is reached. The algorithm then switches to CFace routing using one projected coordinate. IfCFace(l) enters a loop, the algorithm switches back to AB3D. The second algorithm AB3D-CFace(3) starts with AB3D, until a local minimum is reached. The algorithm then permanently switches to CFace routing using three projected coordinates, in order. We evaluate our mechanisms and compare them with the current routing algorithms. The simulation results show the significant improvement in delivery rate over pure AB3D randomized routing (97% compared to 70%) and reduction in path dilation (up to 50%) over pure CFace algorithm.

We study end-to-end delay performance of different routing algorithms in networks with random failures. Specifically, we compare delay performances of differential backlog (DB) and shortest path (SP) routing algorithms and show that DB routing outperforms SP routing in terms of throughput when the network is heavily loaded and/or the failure rate is high while SP routing achieves better delay performance in the low load regime. Then, we investigate delay performance of a hybrid routing algorithm that combines principles of both SP and DB routing algorithms and show that it outperforms both of these routing algorithms. Finally, we demonstrate improvements in delay performance of DB routing through the use of a digital fountain approach which was originally proposed for multicast applications. In addition, our results show that there exists an optimal coding rate where digital fountain based DB routing achieves minimum end-to-end delay. To the best of our knowledge, this is the first work which investigates delay performance of DB routing and its enhanced versions for networks with link failures.

The scheme of Quality of Service (QoS) routing algorithms based on local state information has recently been proposed as an alternative approach to the traditional QoS routing algorithms. By implementing this localized QoS routing algorithm, each source node predetermines and maintains a set of candidate paths for each destination. These sets of paths will help the source node to decide the most appropriate path for a connection request. Hence, it helps to avoid the problems associated with the maintenance of the global network state information. In this paper, we propose a new and effective localized QoS routing algorithm, compare its performance with those of other localized algorithms and a traditional QoS routing algorithm under the same type of network topology, QoS requirements and traffic patterns. The simulations results show that our proposed algorithm can perform better than other routing algorithms. DOI: 10.32913/rd-ict.vol3.no14.258