# Adaptive Orbital Direct-to-Device: Rethinking Satellite IoT Architecture

Hannaneh B. Pasandi, Mohammad Hosseini, Sina Darabi, Franck Rousseau, Juan A Fraire

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Hannaneh B. Pasandi, Mohammad Hosseini, Sina Darabi, Franck Rousseau, Juan A Fraire. Adaptive Orbital Direct-to-Device: Rethinking Satellite IoT Architecture. ACM MobiCom 2025 - 31st Annual International Conference on Mobile Computing and Networking, Nov 2025, Hong Kong China, France. pp.38-43, 10.1145/3737897.3767293 . hal-05423910

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RESEARCH-ARTICLE

Adaptive Orbital Direct-to-Device: Rethinking Satellite IoT Architecture

HANNANEH BARAHOUEI PASANDI, University of California, Berkeley, Berkeley, CA,
United States

MOHAMMAD HOSSEINI, Shahid Beheshti University, Tehran, Tehran, Iran

SINA DARABI, University of Italian Switzerland, Lugano, TI, Switzerland

FRANCK ROUSSEAU, Grenoble Polytechnic Institute, Grenoble, Auvergne-Rhone-Alpes,
France

JUAN ANDRES FRAIRE

Open Access Support provided by:

Grenoble Polytechnic Institute

University of Italian Switzerland

Shahid Beheshti University

University of California, Berkeley

<figure><img src="image_7.png" /></figure>

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ACM MobiCom '25: The 31st Annual
International Conference on Mobile
Computing and Networking

November 4 - 8, 2025

Hong Kong, China

Conference Sponsors:

SIGMOBILE
# Adaptive Orbital Direct-to-Device: Rethinking Satellite IoT Architectures

Hannaneh B. Pasandi

University of California, Berkeley
Berkeley, CA, USA
hanapasandi@eecs.berkeley.edu

Mohammad Hosseini

Shahid Beheshti University
Tehran, Iran
m-hosseini@sbu.ac.ir

Sina Darabi

Università della Svizzera italiana
Lugano, Switzerland
darabs@usi.ch

Franck Rousseau

Grenoble Alps University
Grenoble, France
franck.rousseau@
univ-grenoble-alpes.fr

Juan A. Fraire

INRIA
Lyon, France
juan.fraire@inria.fr

## ABSTRACT

Current satellite Direct-to-Device (D2D) architectures often hard-commit to an orbital regime, forcing suboptimal trade-offs between latency, reliability, and energy. We present, to our knowledge, the first adaptive orbital D2D architecture that dynamically switches among LEO, GEO, and hybrid paths based on real-time Doppler, load, and application QoS. Validated via a LoRa satellite-IoT implementation, our approach improves capacity 3x (936 vs. 312 nodes per gateway at 95% PDR), reduces tail latency by 62% (95th percentile: 342ms vs. 892ms), and lowers energy consumption by 31% via retransmission avoidance. Treating orbital choice as a dynamic optimization addresses core limitations of static designs and generalizes beyond LoRa to cellular and NB-IoT D2D systems, charting a path toward resilient, efficient space-terrestrial IoT.

## CCS CONCEPTS

* **Networks → Network architectures; Satellite networks; Mobile networks;**

## KEYWORDS

Direct-to-Device, Satellite IoT, LEO, GEO, Adaptive Orbital Selection, LoRa

<figure><img src="image_8.png" /></figure>

This work is licensed under Creative Commons Attribution International 4.0.

MobiArch '25, November 4-8, 2025, Hong Kong, China

© 2025 Copyright held by the owner/author(s).

ACM ISBN 979-8-4007-1974-5/25/11.

https://doi.org/10.1145/3737897.3767293

## ACM Reference Format:

Hannaneh B. Pasandi, Mohammad Hosseini, Sina Darabi, Franck Rousseau, and Juan A. Fraire. 2025. Adaptive Orbital Direct-to-Device: Rethinking Satellite IoT Architectures. In *Workshop on Mo-bility in the Evolving Internet Architecture (MobiArch '25), November 4-8, 2025, Hong Kong, China*. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3737897.3767293

## 1 INTRODUCTION

The explosive growth of Internet of Things (IoT) deployments projected to reach 75 billion devices by 2030 [3] has created an urgent need for ubiquitous connectivity beyond terrestrial infrastructure reach. Direct-to-Device (D2D) satellite communication emerges as a critical solution, offering low-power, long-range connectivity for the 2.7 billion users currently unconnected by terrestrial networks [8]. Recent commercial deployments validate this trend. SpaceX's Starlink Direct-to-Cell partnerships with T-Mobile [16] demonstrate LEO satellite cellular connectivity. Apple's Emergency SOS via Globalstar [1] enables satellite messaging for consumer devices. AST SpaceMobile's cellular connectivity demonstrations [2] signal a paradigm shift toward satellite-enabled ubiquitous IoT connectivity.

However, current satellite D2D architectures suffer from a fundamental limitation: static orbital commitments. Existing deployments force a binary architectural choice between Low Earth Orbit (LEO) satellites offering low latency but suffering from coverage gaps and Doppler-induced failures, or Geostationary Earth Orbit (GEO) satellites providing reliable coverage but imposing energy penalties unsuitable for battery-powered IoT devices. This static approach ignores the heterogeneous and dynamic nature of IoT workloads. Emergency alerts require sub-second to few-second latency targets (aligned with our <30s delay-sensitive class), while environmental sensors tolerate hundreds of seconds (e.g., ≈ 300 s) of delay. Dense urban deployments (1000+ nodes/km²)
have different optimization needs than sparse rural sensors. Traffic patterns vary from periodic sensor reports to bursty emergency communications, each benefiting from different orbital characteristics.

Recent advances in satellite networking have begun to address some of these challenges. These insights suggest that constellation-aware approaches can significantly improve performance [14], where intelligent scheduling across satellite constellations enhances throughput and reduces latency. Similarly, spectrum-efficient techniques using Doppler signatures for packet detection have shown promise in overcoming traditional satellite communication bottlenecks [15]. The democratization of direct-to-cell LEO satellite networks has opened new possibilities for adaptive approaches [10], while machine learning techniques are emerging as powerful tools for optimizing satellite IoT systems [7].

We present, to our knowledge, the first adaptive orbital D2D architecture that dynamically selects optimal transmission paths LEO direct, GEO indirect, or hybrid operation based on real-time Doppler conditions, gateway load, interference levels, and application QoS requirements. Our key insight is that treating orbital selection as a dynamic optimization problem, rather than a static deployment choice, unlocks significant performance gains previously thought impossible.

### Contributions.

* Quantify static-architecture limits using TLE-derived Doppler feasibility and Iridium RF measurements.

* Design an adaptive orbital switching framework with MCU-class decision latency (sub-10ms in our simulated implementation).

* Demonstrate gains in simulation validated by real traces: 3× capacity, 62% tail-latency reduction, 31% energy savings.

* Show generality to cellular/NB-IoT D2D beyond LoRa via an architecture-level formulation.

**Limitations.** Our current evaluation is simulation-heavy (validated against 847 real packets from FossaSat-2); large-scale field trials and multi-operator experiments are future work.

## 2 ADAPTIVE ORBITAL D2D FRAMEWORK

### 2.1 System Model

We model a satellite IoT network comprising N devices communicating via both LEO constellation L and GEO satellites G. Each device can select transmission path p ∈ {LEO, GEO, Hybrid} based on instantaneous conditions.

**Performance Metrics.** For path p, total delay comprises:

$$D_{total}^{p} = D_{prop}^{p} + D_{queue} + D_{proc} + D_{retry} \quad (1)$$

where propagation delay $D_{prop}^p$ ranges from 2–10ms (LEO) to 240ms (GEO), queueing delay $D_{queue}$ depends on gateway load, processing delay $D_{proc} \approx 5ms$ includes protocol overhead, and retry delay $D_{retry}$ accounts for failed transmissions.

**Reliability Model.** Success probability depends on Doppler feasibility, gateway capacity, and interference:

$$R_p(t) = P_{doppler}(t) \cdot P_{gateway}(t) \cdot P_{interference}(t) \quad (2)$$

**Energy Model.** Energy per successfully delivered message includes transmission energy and retransmission overhead:

$$E_{msg} = P_{tx} \cdot T_{air} \cdot (1 + N_{retry}) + E_{idle} \quad (3)$$

where $P_{tx}$ is transmission power (14dBm for LoRa), $T_{air}$ is time-on-air (varies with spreading factor), and $N_{retry}$ is the average number of retransmissions.

### Algorithm 1 Adaptive Orbital Selection

**Require:** Application profile ($\Delta_{max}$, $R_{min}$), network state

**Ensure:** Selected path $p^*$

1: // Evaluate Doppler feasibility for LEO

2: $f_{tol} \leftarrow \frac{BW}{2SF+2}$ // LoRa Doppler tolerance

3: **if** $|f_d(t)| > f_{tol}$ **then**

4: Mark LEO infeasible due to Doppler

5: **end if**

6: // Check gateway congestion

7: $D_{queue} \leftarrow \text{ComputeQueueDelay}(A_t, Q)$

8: **if** $D_{queue} > \Delta_{max} - D_{prop}^{min}$ **then**

9: Exclude congested gateways

10: **end if**

11: // Compute interference-aware reliability

12: $R_p \leftarrow \text{EstimateReliability}(I_{s,s'}, \text{traffic})$

13: // Select minimum-delay feasible path

14: $F \leftarrow \{p : D_{total}^p \le \Delta_{max} \land R_p \ge R_{min}\}$

15: **if** $F \ne \emptyset$ **then**

16: **return** $\arg\min_{p \in F} D_{total}^p$

17: **else**

18: **return** GEO // Fallback for best-effort delivery

19: **end if**

### 2.2 Adaptive Switching Algorithm

Algorithm 1 presents our adaptive orbital selection logic with sub-10ms decision latency achieved in simulation on MCU-class timing models.

**Key Functions.** ComputeQueueDelay returns $(A_t - Q) \cdot \bar{T}_{air}/Q$ if $A_t > Q$ where $A_t$ is active transmissions and $Q = 12$ is the gateway channel limit [13]. EstimateReliability computes $R = \prod_i P(s, s'_i, \Delta t_i)$ using capture probabilities from interference matrix $I_{s,s'}$ based on empirical measurements [4].
The algorithm's efficiency comes from early termination: if LEO Doppler exceeds tolerance (87% of passes based on our TLE analysis), we immediately exclude it without further computation.

## 2.3 Cross-Orbital Optimization

Cross-orbital optimization dynamically coordinates multiple satellite orbits to maximize system performance. This optimization operates through three complementary mechanisms that adapt to real-time conditions and application requirements.

**Opportunistic LEO Switching.** The system continuously monitors LEO satellite passes and Doppler conditions using TLE ephemeris data. When Doppler shift $|f_d(t)|$ falls below the LoRa tolerance threshold $f_{tol} = \frac{BW}{2^{SF+2}}$ (7.6 Hz for SF12 at 868 MHz), the optimizer immediately routes delay-sensitive traffic through LEO to achieve sub-100ms latency. This opportunistic approach captures 13% of satellite passes that meet Doppler constraints, providing significant latency benefits when available.

**Predictable GEO Fallback.** For delay-tolerant applications or when LEO Doppler exceeds tolerance, the optimizer leverages GEO's stable 289ms propagation delay. This predictable performance is particularly valuable for dense deployments where LEO gateways approach their 12-channel limit. The optimization algorithm weights GEO selection based on current gateway congestion levels and application latency tolerances.

**Hybrid Load Balancing.** The most sophisticated optimization strategy distributes traffic across multiple orbital regimes based on instantaneous network conditions. The optimizer maintains real-time estimates of gateway capacity utilization, interference levels, and application priority queues. When LEO capacity approaches saturation (>10 active channels out of 12), it proactively shifts delay-tolerant traffic to GEO while preserving LEO resources for critical applications. This dynamic load distribution achieves the 3x capacity improvement demonstrated in our evaluation.

The optimization framework implements these strategies through a unified cost function that weighs latency, energy, and reliability based on application QoS requirements. Practical implications include extending battery life through reduced retransmissions, improving service reliability through path diversity, and enabling network scaling beyond single-orbit limitations.

# 3 CASE STUDY: LORA SATELLITE IOT

## 3.1 Implementation

We validate our adaptive orbital framework through a LoRa satellite IoT system supporting dynamic LEO/GEO/hybrid

selection. Our implementation addresses three critical challenges identified in Section 2.

**Doppler Management.** LEO satellites traveling at 7.5km/s induce frequency shifts exceeding LoRa tolerance by orders of magnitude. Our system continuously monitors Doppler conditions using real-time ephemeris data and switches to GEO when $|f_d(t)| > f_{tol}$ where $f_{tol} = \frac{BW}{2^{SF+2}}$.

**Gateway Bottlenecks.** Commercial LoRa gateways support exactly 12 concurrent channels due to FPGA constraints in Semtech SX1302 chipsets [13]. Our system tracks gateway load using the queueing model in Equation 1 and performs load balancing across available satellites.

**Energy Optimization.** The adaptive approach reduces energy consumption through two mechanisms: (1) avoiding Doppler-prone transmission windows eliminates failed transmissions that require costly retransmissions, and (2) intelligent path selection minimizes the number of retransmission attempts. IoT devices perform lightweight orbital calculations (8.3ms on simulated 100MHz MCU) to determine optimal transmission timing, consuming minimal additional energy compared to the 31% savings from avoided retransmissions. By avoiding Doppler-prone transmission windows, our implementation reduces retransmission overhead from 2.3x to 1.2x, extending battery life significantly as shown in Equation 3.

## 3.2 Evaluation Setup

We evaluate our approach using a discrete-event simulator validated against 847 packets from FossaSat-2 CubeSat, achieving 96.8% Doppler prediction accuracy.

**Scenarios.** Six deployment scenarios combining spatial distributions (centralized 50–1000 nodes in 44×27km vs. wide-area 100 nodes across 500–25,000km) with traffic patterns (Poisson arrivals, 0.5–2 packets/minute).

**Baselines.** Compare against static LEO (sparse/dense), static GEO, and hybrid-static approaches using Walker-Delta (40/5/2) LEO constellation at 600km altitude and GEO satellite at 9°E.

**QoS Classes.** Three application types based on real deployments: delay-sensitive ($\Delta_{max}$ = 30s) for critical alerts, delay-tolerant ($\Delta_{max}$ = 300s) for routine monitoring, and best-effort for non-critical data.

**Hardware Configuration.** LoRa parameters: BW=125kHz, SF=7–12, TX power=14dBm. Gateway: 12-channel SX1302 with 8 SF7–12 channels plus 1 high-speed SF7 channel.

## 3.3 Performance Results

Figure 2 summarizes our implementation's performance advantages across three key metrics. The capacity improvement comes from intelligent load distribution: when LEO
<figure><img src="image_9.png" /><figcaption>Figure 1: Adaptive LoRa-satellite architecture and performance comparison showing static LEO/GEO limitations.</figcaption></figure>

approaches saturation (150–312 nodes depending on density), our algorithm offloads to GEO, achieving combined capacity of 936 nodes.

**Capacity Improvement.** Adaptive selection supports 936 nodes at 95% packet delivery ratio compared to 312 (LEO sparse), 156 (LEO dense), or 498 (GEO), achieving 3× improvement through intelligent load balancing shown in Figure 2(a).

**Latency Optimization.** 95th percentile latency improves by 62% compared to static LEO architectures (342ms vs. 892ms). Median latency of 92ms approaches sparse LEO levels while tail latency of 342ms matches GEO predictability, as demonstrated in Figure 2(b).

**Energy Efficiency.** 31% average reduction in energy consumption through intelligent retransmission avoidance shown in Figure 2(c). Critical for battery-powered IoT targeting 10-year lifetime requirements [19], [9], [18].

## 3.4 Architectural Insights

Our LoRa case study reveals key principles for adaptive orbital D2D that extend beyond specific modulation schemes. Recent work on constellation-aware scheduling [14] corroborates our findings on dynamic satellite selection importance. CosMAC's intelligent scheduling across satellite constellations aligns with our adaptive orbital switching, though they focus on intra-constellation coordination while we address inter-orbital regime selection.

**Condition-Aware Switching.** Real-time Doppler monitoring using TLE data [?] enables precise LEO feasibility assessment. 87% of satellite passes exceed tolerance, making adaptive selection crucial. The LoRaTrimmer approach [5] to weak signal decoding through chirp trimming offers complementary benefits where their sensitivity improvements could extend our LEO operational window by tolerating higher Doppler shifts.
<figure><img src="image_10.png" /><figcaption>Figure 2: Adaptive orbital selection achieves 3× capacity improvement (936 vs 312 nodes) and 62% latency reduction through intelligent LEO/GEO load balancing with 31–35% energy savings.</figcaption></figure>

**Application-Driven Optimization.** Different QoS classes benefit from different orbital selections, validating our adaptive approach. SateRIoT [12] measurements show 67% of rural IoT traffic is delay-tolerant agricultural sensing while 33% requires low-latency alerts, naturally accommodated by our framework.

**Graceful Degradation.** Hybrid operation provides fall-back paths when primary orbital regime fails. COTS device measurements [8] reveal computational constraints limit on-board processing, making our lightweight switching algorithm particularly suitable for satellite-side implementation.

**Cross-Layer Optimization.** These cross-layer opportunities are not implemented in our current prototype but represent promising directions for future exploration, where adaptive orbital selection could integrate with PHY/MAC scheduling and multipath routing.

# 4 ANALYSIS AND DISCUSSION

## 4.1 Broader Applicability

While demonstrated through LoRa, our adaptive orbital framework applies to diverse satellite D2D technologies. For breadth across direct-to-cell, DtS-IoT, and protocol stacks that our architecture can sit atop, we refer readers to [11].

**Cellular D2D.** Direct-to-cell systems like Starlink D2C [16] and AST SpaceMobile [2] face similar LEO/GEO trade-offs. Our framework could optimize between low-latency LEO cells and reliable GEO coverage.

**Narrowband IoT.** NB-IoT satellite systems encounter identical Doppler challenges described in [17]. Adaptive selection could enable energy-efficient battery-powered deployments.

**Emergency Communications.** Critical applications require adaptive strategies where these insights suggest that routing emergency alerts via LEO when available, with fall-back to GEO for guaranteed delivery, can provide optimal coverage.

**Opportunities for future exploration.** Opportunities for future exploration include large-scale field validation to

establish robustness across diverse traffic patterns, regulatory regimes, and hardware platforms extending beyond the simulation-heavy results of this work.

## 4.2 Scalability Analysis

Our framework scales across multiple dimensions, validated through theoretical modeling and empirical measurements [5].

**Constellation Size.** Switching decisions remain tractable (8.3ms simulated on 100MHz MCU) as constellation size grows, since evaluation complexity is $O(|satellites|)$ for visible satellites. Even with mega-constellations, hierarchical selection maintains sub-10ms latency by pre-filtering candidates based on geometric visibility. CosMAC [14] demonstrates similar scalability, handling large constellations through intelligent pruning.

**Device Population.** Distributed decision-making avoids centralized bottlenecks. SateRIoT's [12] deployment validates this where their distributed scheduling achieves high channel utilization without central coordination. Our framework extends this across orbital regimes, potentially supporting 10,000+ devices per coverage area through LEO/GEO load distribution combined with typical 1% IoT duty cycles.

**Geographic Coverage.** Earth-fixed cells simplify satellite handovers. Pre-computed orbital preference matrices reduce runtime complexity from $O(N \cdot S)$ to $O(1)$ for orbital selection, where $N$ is devices and $S$ is visible satellites.

**Temporal Scalability.** Dual-timescale adaptation handles short-term dynamics (100ms Doppler updates) and long-term evolution (constellation deployment phases), reducing signaling overhead while maintaining selection accuracy.

**Multi-Operator Scalability.** The framework extends to multi-operator scenarios. Using service agreements similar to D2Cell, adaptive selection can optimize across commercial boundaries, potentially increasing effective capacity further. Three key challenges require attention for practical deployment:

**Coordination Overhead.** Switching between satellites from different operators requires inter-provider agreements. Recent FCC supplemental coverage regulations [6] facilitate
such collaborations. Predictive switching announces transitions 30 seconds in advance, allowing upstream routers to pre-establish paths, reducing packet loss during transitions in our simulations from 12% to 0.3%.

**Device Complexity.** Adaptive algorithms must run on resource-constrained IoT devices. Our implementation demonstrates feasibility with sub-10ms simulated decision latency. LoRaTrimmer's hardware optimizations provide a blueprint where their FPGA design could be extended to include orbital selection logic with minimal overhead.

**Backward Compatibility.** Adaptive selection degrades gracefully for legacy devices. Dual-mode operation allows adaptive devices to signal capabilities in handshake. Even 30% adaptive device penetration improves overall network performance through better resource utilization.

**Regulatory Harmonization.** Operating across orbital regimes requires compliance with varying frameworks. Software-defined radio techniques dynamically adjust transmission parameters to meet requirements for each orbital regime.

## 5 CONCLUSION

We presented an adaptive orbital D2D architecture that recasts orbital selection as a dynamic, condition-aware optimization. Our LoRa-based implementation achieves 3× capacity, 62% reduction in 95th-percentile latency, and 31% lower energy versus static LEO/GEO. While our validation combines simulation with limited real traces, the approach is broadly applicable to D2D cellular and NB-IoT. These insights suggest that future work should include wider field trials, proactive ML-based switching, and multi-operator integration. The broader impact extends beyond technical contributions. Our approach could potentially reduce satellite IoT connectivity costs through improved spectrum efficiency. With projected growth to 75 billion IoT devices by 2030 [3], adaptive orbital architectures offer a path toward scaling satellite D2D systems efficiently. As commercial deployments expand [1, 2, 16], adaptive selection becomes increasingly important for managing network resources and achieving ubiquitous IoT connectivity.

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