The modern Internet supports diverse applications with heterogeneous quality of service (QoS) requirements. Low Earth orbit (LEO) satellite constellations offer a promising solution to meet these needs, enhancing coverage in rural areas and complementing terrestrial networks in urban regions. Ensuring QoS in such networks requires joint optimization of routing, bandwidth allocation, and dynamic queue scheduling, as traffic handling is critical for maintaining service performance. This paper formulates a joint routing and bandwidth allocation problem where QoS requirements are treated as soft constraints, aiming to maximize user experience. An adaptive scheduling approach is introduced to prioritize flow-specific QoS needs. We propose a Monte Carlo tree search (MCTS)-inspired method to solve the NP-hard route and bandwidth allocation problem, with Lyapunov optimization-based scheduling applied during reward evaluation. Using the Starlink Phase 1 Version 2 constellation, we compare end-user experience and fairness between our proposed DSROQ algorithm and a benchmark scheme. Results show that DSROQ improves both performance metrics and demonstrates the advantage of joint routing and bandwidth decisions. Furthermore, we observe that the dominant performance factor shifts from scheduling to routing and bandwidth allocation as traffic sensitivity changes from latency-driven to bandwidth-driven.

Large-scale low-Earth-orbit (LEO) constellations demand routing that simultaneously minimizes energy, guarantees delivery under congestion, and meets latency requirements for time-critical flows. We present a segment routing over IPv6 (SRv6) flexible algorithm (Flex-Algo) framework that consists of three logical slices: an energy-efficient slice (Algo 130), a high-reliability slice (Algo 129), and a latency-sensitive slice (Algo 128). The framework provides a unified mixed-integer linear program (MILP) that combines satellite CPU power, packet delivery rate (PDR), and end-to-end latency into a single objective, allowing a lightweight software-defined network (SDN) controller to steer traffic from the source node. Emulation of Telesat’s Lightspeed constellation shows that, compared with different routing schemes, the proposed design reduces the average CPU usage by 73%, maintains a PDR above 91% during traffic bursts, and decreases urgent flow delay by 18 ms between Ottawa and Vancouver. The results confirm Flex-Algo’s value as a slice-based traffic engineering (TE) tool for resource-constrained satellite networks.

Internet of Things (IoT) devices have become increasingly ubiquitous with applications not only in urban areas but remote areas as well. These devices support industries such as agriculture, forestry, and resource extraction. Due to the device location being in remote areas, satellites are frequently used to collect and deliver IoT device data to customers. As these devices become increasingly advanced and numerous, the amount of data produced has rapidly increased potentially straining the ability for radio frequency (RF) downlink capacity. Free space optical communications with their wide available bandwidths and high data rates are a potential solution, but these communication systems are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being inefficient in terms of the amount of data received versus the power expended. In this paper, we propose a deep reinforcement learning (DRL) method using Deep Q-Networks that takes advantage of weather condition forecasts to improve energy efficiency while delivering the same number of packets as schemes that don't factor weather into routing decisions. We compare this method with simple approaches that utilize simple cloud cover thresholds to improve energy efficiency. In testing the DRL approach provides improved median energy efficiency without a significant reduction in median delivery ratio. Simple cloud cover thresholds were also found to be effective but the thresholds with the highest energy efficiency had reduced median delivery ratio values.

Software-defined networking (SDN) has emerged as a promising approach for managing traditional satellite communication. This enhances opportunities for future services, including integrating satellite and terrestrial networks. In this paper, we have developed an SDN-enabled framework for Low Earth Orbit (LEO) satellite networks, incorporating the Open-Flow protocol, all within an OMNeT++ simulation environment. Dynamic controller assignment is one of the most significant challenges for large LEO constellations. Due to the movement of LEO satellites, satellite-controller assignments must be updated frequently to maintain low propagation delays. To address this issue, we present a dynamic satellite-to-controller assignment (DSCA) optimization problem that continuously adjusts these assignments. Our optimal DSCA (Opt-DSCA) approach minimizes propagation delay and optimizes the number of active controllers. Our preliminary results demonstrate that the DSCA approach significantly outperforms the static satellite-to-controller assignment (SSCA) approach. While SSCA may perform better with more controllers, this scheme fails to adapt to satellite movements. Our DSCA approach consistently improves network efficiency by dynamically reassigning satellites based on propagation delays. Further, we found diminishing returns when the number of controllers is increased beyond a certain point, suggesting optimal performance with a limited number of controllers. Opt-DSCA lowers propagation delays and improves network performance by optimizing satellite assignments and reducing active controllers.

For decades, satellites have facilitated remote internet of things (IoT) services. However, the recent proliferation of increasingly capable sensors and a surge in the number deployed, has led to a substantial growth in the volume of data that needs to be transmitted via satellites. In response to this growing demand, free space optical communication systems have been proposed, as they allow for the use of large bandwidths of unlicensed spectrum, enabling high data rates. However, optical communications are highly vulnerable to weather-induced disruptions, thereby limiting their high potential. This paper proposes the use of high altitude platform station (HAPS) systems in conjunction with delay-tolerant networking techniques to increase the amount of data that can be transmitted to the ground from satellites when compared to the use of traditional ground station network architectures. The architectural proposal is evaluated in terms of delivery ratio and buffer occupancy, and the subsequent discussion analyzes the advantages, challenges and potential areas for future research.

Due to the unprecedented advances in satellite fabrication and deployment, innovative communications and networking technologies, ambitious space projects and programs, and the resurgence of interest in satellite networks, there is a need to redefine space networks (SpaceNets) to incorporate all of these evolutions. This paper introduces a vision for future SpaceNets that considers advances in several related domains. First, we present a reference architecture that captures the various network entities and terminals in a holistic manner. Based on this, space, air, and ground use cases are studied. Then, the architectures and technologies that enable the envisaged SpaceNets are investigated. In so doing, we highlight the activities and projects of different standardization bodies, satellite operators, and national organizations towards the envisioned SpaceNets. Finally, the challenges, potential solutions, and open issues from communications and networking perspectives are discussed.

Direct-to-Satellite-IoT (DtS-IoT) is a promising approach for data transfer to/from IoT devices in remote areas where deploying terrestrial infrastructure is not appealing or feasible. In this context, Low-Earth Orbit (LEO) satellites can serve as passing-by IoT gateways to which devices can offload buffered data to. However, transmission distance and orbital dynamics, combined with highly constrained devices on the ground makes DtS-IoT a very challenging problem. In fact, existing IoT medium access control protocols, negotiations schemes, etc. need to be revised and/or extended to scale up to these challenging conditions. The intricate time-dynamic aspects of DtS-IoT networks require of adequate simulation environments to assess the expected performance of enabling technologies. To make up for the lack of such tools, we present a novel event-driven open-source end-to-end simulation tool coined FLoRaSAT. The simulator leverages Omnet++ and includes a benchmarking DtS-IoT scenario comprising 16 cross-linked LEO satellites and 1500 IoT nodes on the surface. Satellites and devices are connected using the standard LoRaWAN Low-Power Wide Area (LPWAN) protocol (Class A and B). FLoRaSAT allows the easy implementation and study of DtS-IoT radio access and core network protocols, and we take advantage of this flexibility to investigate expected network metrics and non-intuitive phenomena emerging from the resulting multi-gateway setup.

Delay Tolerant Networking (DTN) has been proposed to provide efficient and autonomous store-carry-and-forward data transport in space networks. Since these networks relay on scheduled contact plans, Contact Graph Routing (CGR) can be used to optimize routing and data delivery performance. However, transient or permanent faults can occur in large space networks, thus introducing uncertainties on the original contact plan reducing CGR performance. In this work, we propose alternative CGR metrics and study a novel replication-based CGR formulation that improves data delivery when operating under uncertain contact plans. This proposal is analyzed by means of simulation and compared with other scheduled and opportunistic routing approaches. Results provide a baseline to develop more specialized probabilistic CGR routing strategies that could cope with unplanned and transient faults in future space DTN networks.

Delay and disruption tolerant networks (DTNs) are becoming an appealing solution for extending Internet boundaries so as to embrace disruptive communications. In particular, if node trajectory can be predicted as in space networks, routing schemes can take advantage of the a-priori knowledge of a contact plan. Despite mechanisms such as Contact Graph Routing (CGR) exist, they might derive in harmful overbooking of forthcoming contacts, also known as congestion. In order to tackle congestion, we initially formulate the problem by means of a linear programming (LP) model so as to establish an upper theoretical bound of performance. Next, we survey existing congestion mitigation mechanisms for predictable DTNs to later contribute with a CGR extension named PA-CGR. Finally, in the pursuance of an optimal congestion avoidance approach, we also propose and evaluate in a realistic scenario a novel multi-graph technique (MG-CGR) that outperforms existing solutions by exploiting traffic predictability.