In 2026, we saw rising numbers of proposals for orbital data centers to facilitate AI, e.g., by SpaceX (up to one million satellites), Blue Origin, and Google—yet none include rigorous lifecycle sustainability analysis. We present ESpaS-ODC, a lifecycle carbon model that accounts for the three subsystems physically unavoidable at GPU-class power densities but absent from prior work: a thermal radiator sized from ISS data, solar-array degradation and eclipse margin, and cold-standby spares for no-repair access. Applying the model to an edge data center in a 510 km orbit reveals that the service-overhead-scaled radiator weighs about eleven times the GPU it serves and that modeled power and thermal infrastructure dominate launched component mass—not compute. Using mission duration T as the denominator to amortize launch, re-entry, and manufacturing carbon, model-boundary parity with a global-average terrestrial DC occurs within the first two mission years in our parameter sweep. With respect to a renewables-powered green-energy DC (Finland grid intensity: 68 gCO2e/kWh, PUE: 1.2), parity requires multi-year missions with cold-standby spares compensating the shorter component lifetimes. Idealized dawn-dusk SSO (no-eclipse regime, β > βcrit) eliminates the eclipse battery, cutting modeled component mass significantly (e.g., 25% on the 1 kW-ODC for a 3 yr mission), but leaves the radiator unchanged: high-beta orbits solve the battery problem, not the thermal problem. Finally, carrying one full cold spare (r = 2) increases amortized carbon per GPU hour by 40% on Starship and 34% on Falcon-9 at T = 3 yr, while its dependability benefit remains to be quantified.

Existing LEO satellite network simulators widely approximate satellite coverage using a cone-based model that assumes uniform capacity distribution within a satellite's field of view. However, modern systems employ phased-array antennas that impose discrete beam constraints, fundamentally altering coverage characteristics. In this paper, we investigate the extent to which ignoring beam-level constraints biases network-level performance estimates. We introduce a simplified beam-aware model that captures beam count, capacity, and limited spatial reuse, and compare it against traditional cone-based approximations across a range of user distributions. Our results show that cone models can significantly overestimate service coverage, particularly under sparse or highly clustered demand, due to beam-count and beam-capacity exhaustion effects. We further analyze how beam-hopping and -stacking mitigate these limitations and quantify their impact on predicted coverage differences, demonstrating reductions in predicted coverage difference of 40% and 65% respectively. We position these results of our model as the first step towards higher-fidelity simulation to show the importance of modeling beams and we sketch directions for future work that further improves fidelity.

International audience

Delivering downlink ACKs to Class A LoRaWAN devices via LEO satellite constellations requires predicting end-to-end propagation delays of 10–500 ms to hit a 1-second receive window, a timing challenge absent from terrestrial schedulers. We present DORSAL, the first Mixed-Integer Linear Programming (MILP) scheduler for downlink delivery in direct-to-satellite LoRaWAN networks. It introduces theDownlink Arrival Receive-window Timing (DART)as the central constraint to jointly optimize gateway selection and packet injection timing across ISL-capable and bent-pipe architectures. Evaluation on a Walker 5×5 polar constellation (25 satellites, 650 km) and a commercial ground station network yields five results: (i) DART infeasibility blocks 73% of bent-pipe deliveries regardless of scheduler quality: DART-unaware approaches collapse from 93% nominal to only 20% real delivery; (ii) ISL routing raises %ACK by +67.4 pp over bent-pipe (91.6% vs. 24.2% atnGS=3 ground stations, ISM band) by eliminating the three-way simultaneous-visibility constraint, with 73% of ACKs delivered via ≥ 2-hop paths; (iii) ISL %ACK is nearly flat acrossnGS∈ {3, 6, 12} ground stations (91.6%–92.7%), showing that a minimal threestation polar network captures the full scheduling benefit of an ISL-equipped constellation; (iv) ISL hardware speed has negligible impact up to 300 ms/hop: %ACK is flat from 0 to 300 ms, then degrades in two discrete steps (−5.3 pp at 500 ms, −2 pp at 700 ms) as successive hop-count tiers lose causal injection feasibility; and (v) the MILP solves to proven optimality within seconds for up to 103devices, confirming that the sparse conflict-graph structure keeps the problem tractable at realistic IoT subscriber counts.

Low Earth Orbit (LEO) satellite constellations, such as Starlink, are increasingly promoted as a solution to the digital divide in rural and underserved communities. In this paper, we take a closer look at the limits of this approach. Using the insight that capacity limitations of LEO-based access networks are driven by peak demand density, we introduce a simple analytical model that brings together real-world demand data with the physical and regulatory limits of LEO satellite networks. Applying our model to broadband demand across the United States, we find that serving the current Starlink constellation size is likely insufficient for covering all un- and underserved locations in the US and we find diminishing returns that disincentivize scaling the constellation to serve the long-tail of these un(der)served locations. We also identify that Starlink's current pricing is likely unaffordable for the majority of these locations, even with existing government subsidies. We argue that LEO constellations, while technologically impressive, are just another piece of the solution, rather than a panacea. New, innovative approaches are still required to end the digital divide.

Low Earth Orbit (LEO) constellations are pivotal for the global connectivity of Internet of Things (IoT) networks. They facilitate Direct-to-Satellite (DtS) communications for ground nodes and address the absence of terrestrial infrastructure in remote regions. Although LoRa-based solutions provide low-cost connectivity to constrained devices in DtS-IoT networks, scalability remains a significant challenge due to heavy channel congestion associated with large-scale deployments. The LoRa Optimistic Collision Information-based (L-OCI) mechanism has been designed to estimate efficiently the network size of a LoRa-based DtS-IoT deployment. It enables a passing satellite to gauge the potential congestion in the served region by estimating the number of active ground nodes within its coverage area. However, this mechanism was devised to operate assuming a single LEO satellite. This paper introduces the constellation LOCI (CL-OCI), a new methodology for network size estimation designed for LEO satellite constellations. We thoroughly tested CL-OCI using FLoRaSat, a discrete-event satellite constellation simulator with realistic orbital DtS-IoT scenarios. Results indicate a substantial improvement in scalability, doubling the maximum number of nodes CL-OCI can estimate, and a sevenfold reduction in energy wasted due to collided transmissions compared to the baseline single-satellite L-OCI.

This paper presents a framework for integrating Low-Earth Orbit (LEO) platforms with Non-Terrestrial Networks (NTNs) in the emerging 6G communication landscape. Our work applies the Mega-Constellation Services in Space (MCSS) paradigm, leveraging LEO mega-constellations’ expansive coverage and capacity, designed initially for terrestrial devices, to serve platforms in lower LEO orbits. Results show that this approach overcomes the limitation of sporadic and time-bound satellite communication links, a challenge not fully resolved by available Ground Station Networks and Data Relay Systems. We contribute three key elements: (i) a detailed MCSS evaluation framework employing Monte Carlo simulations to assess space user links and distributions; (ii) a novel Space User Terminal (SUT) design optimized for MCSS, using different configurations and 5G New Radio Adaptive Coding and Modulation; (iii) extensive results demonstrating MCSS’s substantial improvement over existing Ground Station Networks and Data Relay Systems, motivating its role in the upcoming 6G NTNs. The space terminal, incorporating a multi-system, multi-orbit, and software-defined architecture, can handle Terabit-scale daily data volumes and minute-scale latencies. It offers a compact, power-efficient solution for properly integrating LEO platforms as space internet nodes.

Contact Graph Routing (CGR) is the state-of-the-art deterministic routing approach for scheduled space Delay-tolerant Networks (DTN). Indeed, CGR outlined the Schedule-Aware Bundle Routing (SABR) recommended standard from the Consultative Committee for Space Data Systems (CCSDS). The core approach exploits the predictability of space node mobility patterns and link availability, which is imprinted in time-varying graphs fed to Dijkstra and Yen's algorithms for route computation (pathfinding). This paper addresses the scalability issues CGR faces when computing routes over large-scale contact plans spanning several nodes and long-term planning horizons. After an in-depth analysis of the main CGR computation limitations, we propose multipath-tracking and tree-caching embedded in the Shortest-Path tree routing for Space Networks (SPSN) routing scheme. Simulation results show that SPSN's route computation time does not increase with increasing contact plan horizon while slightly improving on CGR's delivery rate.

The worldwide adoption of the Internet of Things (IoT) depends on the massive deployment of sensor nodes and timely data collection. However, installing the required ground infrastructure in remote or inaccessible areas can be economically unattractive or unfeasible. Cost-effective nanosatellites deployed in low Earth orbits (LEOs) are emerging as an alternative solution: on-board IoT gateways provide access to remote IoT devices, according to direct-to-satellite IoT (DtS-IoT) architectures. One of the main challenges of DtS-IoT is to devise communication protocols that scale to thousands of highly constrained devices served by likewise constrained orbiting gateways. In this article, we tackle this issue by first estimating the (varying) size of the device set underneath the (mobile) nanosatellite footprint. Then, we demonstrate the applicability of the estimation when used to intelligently throttle DtS-IoT access protocols. Since recent works have shown that MAC protocols improve the throughput and energy efficiency of a DtS-IoT network when a network size estimation is available, we present, here, a novel and computationally efficient network size estimator in DtS-IoT: our optimistic collision information (OCI)-based estimator. We evaluate OCI’s effectiveness with extensive simulations of DtS-IoT scenarios. Results show that when using network size estimations, the scalability of a frame-slotted Aloha-based DtS-IoT network is boosted eightfold, serving up to$4 \times 10^{3}$devices, without energy efficiency penalties. We also show the effectiveness of the OCI mechanism given realistic detection ratios and demonstrate its low computational cost implementation, making it a strong candidate for network estimation in DtS-IoT.

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.

Direct-to-Satellite IoT allows devices on the Earth surface to directly reach Low-Earth Orbit (LEO) satellites passing over them. Although an appealing approach towards a truly global IoT vision, scalability issues as well as highly dynamic topologies ask for dedicated protocol adaptations supported by novel models. This paper contributes to this research by introducing estimators and a transmission probability function to dynamically control the contending set of devices on a framed slotted Aloha model compatible with the LoRaWAN specification. In particular, we discuss techniques that account for particularities in the dynamics of sparse DtS-IoT constellations. Simulation analyses of a realistic case study show that >86% of the theoretical throughput is achievable in practice.

A global Internet of Things is possible by embracing constellations of satellites acting as orbiting gateways in a Direct-to-Satellite IoT (DtS-IoT). By removing the dependency on ground gateways, DtS-IoT enables a direct service on the regions illuminated by the passing-by satellite. After an in-depth overview of relevant experiments and candidate technologies, we discover that specific configurations of the Long-Range (LoRa) network protocol specification are particularly appealing to realize the DtS-IoT vision. Specifically, we profit from the maximum clock drift permitted on LoRa devices to propose the sparse satellite constellations concept. This approach significantly reduces the in-orbit DtS-IoT infrastructure at the expense of latency anyway present in resource-constrained IoT networks. We then introduce a novel algorithm comprising specific heuristics to design quasi-optimal topologies for sparse IoT constellations. Obtained results show that LoRa-compatible DtS-IoT services can already be provided world-wide with 10% and 4% of the satellites required for a traditional dense constellation, in different configurations.

Size and weight limitations of low-earth orbit (LEO) small satellites make their operation rest on a fine balance between solar power infeed and power demands of communication technologies on board, buffered by on-board battery storage. As a result, the problem of planning battery-powered payload utilization together with intersatellite communication is extremely intricate. Nevertheless, there is a growing trend toward constellations and megaconstellations that are to be managed using sophisticated software support. Earlier work has leveraged cost-optimal reachability in priced timed automata for deriving near-optimal finite-horizon schedules to operate a single LEO satellite in orbit. This article harvests that work and improves it in several dimensions, all needed for true in-orbit applicability: 1) the battery representation is no longer bound to be linear, but can be kinetic, which means that the optimization problem includes nonlinearities; 2) the management is perpetuated by a receding horizon scheduling strategy; 3) the model is continuously improved with the latest telemetry received from orbit; 4) a tandem of satellites equipped with state-of-the-art intersatellite link transponders is considered; 5) the core optimization problem is now solved using dynamic programming with antichain-based pruning, which is proven to be optimal and despite all the additional features outperforms the earlier approach by orders of magnitude; 6) the entire approach is grounded in the concrete requirements of the GOM X-4 LEO mission; 7) care is taken to make the approach usable by the space engineers, and robust against failures of parts of the toolchain; and 8) an extensive test campaign validates accuracy, efficiency, scalability, and robustness with respect to the operational requirements and constraints of LEO constellations.

Three-dimensional Networks-on-Chips (3D-NoCs) have emerged as an alternative to further enhance the performance, functionality, and packaging density of 2D-NoCs. However, the increasing complexity of NoC routers, the continuous miniaturization of silicon technology, the lower operating voltages, and the higher operating frequencies have made the NoC increasingly vulnerable to soft errors. In particular, transient faults occurring in the route computation unit (RCU) can provoke misrouting which may lead to severe effects such as deadlocks or packet loss, corrupting the operation of the entire chip. By combining a reliable fault detection circuit leveraging circuit-level double-sampling, with a cost-effective rerouting mechanism, we develop a full fault-tolerance solution that can efficiently detect and correct such fatal errors before the affected packets leave the router. To validate the proposed solution, we also introduce a novel method for simulation-based fault-injection based on the NoC's gate-level netlist. Experimental results obtained from a partially and vertically connected 3D-NoC indicate that our solution can provide a high level of reliability in the presence of errors, at the expense of an area and power overhead of 4.1% and 6.8% respectively.

Power demands of the transmission technologies for communication between LEO satellites are difficult to counterbalance by solar infeed and on-board battery storage, due to size and weight limitations in LEO. This makes the problem of battery-powered inter-satellite communication a very difficult one. Its management requires a profound understanding as well as techniques for a proper extrapolation of the electric power budget as part of the inter-satellite and satellite-to-ground communication design. We discuss how the construction of contact plans in delay tolerant networking can profit from a sophisticated model of the on-board battery behaviour. This model accounts for both nonlinearities in battery behaviour as well as stochastic fluctuations in charge, so as to control the risk of battery depletion. We take an hypothetical Ulloriaq constellation based on the GomX-4 satellites from GomSpace as a reference for our studies.

In this contribution, we address a specific question in the context of a low-cost communication network rendering possible interaction with outposts and sensors on the far side of the moon. The underlying approach brings together low-lunar orbit satellites with Delay-Tolerant Networking (DTN) protocols. In this application context we have addressed the question if there is a huge negative impact on the end-to-end delays if the communication within the network is completely tracked by mission control on Earth using a dedicated routing approach. The introduced approach is discussed with reference to a concrete case study. In the course of our analysis, an evaluation of the end-to-end delays is conducted using further routing strategies for comparison purposes. Taking some restrictions into account, the overall feasibility of the proposed tracking approach is shown.

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.

Ring Road Networks (RRNs) have been introduced as a solution to transfer data between different ground stations by means of a world-wide message-ferry system built upon low Earth orbit (LEO) satellites. The objective of this work is to study the benefits and challenges of including Inter-Satellite Links (ISL) capabilities into RRNs. In this context, an appealing RRN case study is presented and evaluated through simulations based on Delay/Disruption-tolerant Network (DTN) protocols and algorithms. Results show that even though the overall delivery metrics improve with ISLs, network congestion impedes a more efficient utilization of the communication resources.

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.