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EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles

Shuai Yu, Xiaowen Gong, Qian Shi, Xiaofei Wang, Xu Chen

arXiv:2101.06056v1cs.NIcs.AIcs.DC

TL;DR

The paper addresses how to support IoV services where terrestrial edge coverage is unavailable while managing SAGIN resource constraints. It surveys orbital and aerial edge computing, proposes an EC-SAGIN framework with pre-classification and offline DIL-based offloading and caching, and reports improvements over benchmark policies in simulation.

  • Problem

    Terrestrial IoV edge computing lacks global coverage, while SAGIN edge computing must manage dynamic resources, latency, bandwidth, and limited satellite or UAV energy.

  • Method

    The paper proposes EC-SAGINs with fine-grained task partitioning, pre-classification to reduce the action space, and offline deep imitation learning for joint offloading and caching decisions.

  • Results

    The proposed DOCS outperforms benchmark policies, reducing system reward by up to 29.40%, achieving 36.52% action accuracy, and reducing task completion time by at most 53.75%.

  • Takeaways & Limitations

    EC-SAGINs and DIL-based decision making provide a reported approach for supporting remote-area IoV services with real-time joint offloading and caching.

  • Takeaways & Limitations

    Orbital edge computing remains constrained by physical radiation damage and long propagation latencies, especially for GEO and MEO systems.

Abstract

from arXiv · show

Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data processing and content sharing among vehicles and terrestrial edge computing (TEC) infrastructures (e.g., 5G base stations and roadside units) with little or no human intervention, plays a key role in the intelligent transportation systems. However, EC-IoV is heavily dependent on the connections and interactions between vehicles and TEC infrastructures, thus will break down in some remote areas where TEC infrastructures are unavailable (e.g., desert, isolated islands and disaster-stricken areas). Driven by the ubiquitous connections and global-area coverage, space-air-ground integrated networks (SAGINs) efficiently support seamless coverage and efficient resource management, represent the next frontier for edge computing. In light of this, we first review the state-of-the-art edge computing research for SAGINs in this article. After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas. The main objective of the framework is to minimize the task completion time and satellite resource usage. To this end, a pre-classification scheme is presented to reduce the size of action space, and a deep imitation learning (DIL) driven offloading and caching algorithm is proposed to achieve real-time decision making. Simulation results show the effectiveness of our proposed scheme. At last, we also discuss some technology challenges and future directions.

I. INTRODUCTION

IoV needs edge computing to process growing vehicular data, but terrestrial coverage and resources remain limited in remote areas. The paper surveys edge computing for SAGINs and proposes EC-SAGINs with reduced-action-space offloading and caching decisions for real-time IoV services.

  • IoV and terrestrial edge-computing motivation: IoV supports road information, automatic control, and intelligent transportation services but faces limited capacity for processing massive vehicle and sensor data.Edge computing places computation and storage near vehicles to reduce bandwidth demands and latency.
  • IoV and terrestrial edge-computing motivation: Terrestrial EC-IoV cannot provide global, seamless coverage, leaving remote-area vehicles vulnerable to limited connectivity and scarce network resources.The paper specifically identifies rural and remote highways as settings where vehicles may lose network connections.
  • SAGIN motivation: SAGINs combine orbital, aerial, and terrestrial networks to provide ubiquitous connections and global-area coverage for edge computing in remote areas.Their segments may operate independently or inter-operationally, while aerial systems can supplement capacity and coverage for isolated areas.
  • SAGIN challenges: Integrating edge computing into SAGINs remains constrained by propagation delay, bandwidth, limited satellite and HAP energy, and complex IoV transmission, processing, and caching workloads.LEO satellites may struggle to run high-energy applications such as deep learning, motivating low-latency processing and efficient resource management.
  • Paper scope and contributions: The paper reviews SAGIN edge-computing architectures and proposes EC-SAGINs, formulating fine-grained offloading and caching as a multi-label classification problem.The framework targets IoV services in remote areas and provides a survey of architectures, use cases, advantages, and challenges.
  • Paper scope and contributions: A pre-classification scheme reduces the action space, while offline deep imitation learning enables real-time offloading and caching decisions under limited satellite energy.The approach addresses the computation and energy costs associated with online deep reinforcement learning.

A. Orbital Edge Computing

Orbital edge computing brings processing closer to space-generated data and can support globally covered applications, including intelligent transportation. Its practical use is constrained by radiation, latency, unreliable links, and limited nanosatellite energy and computing capacity.

  • Orbital edge computing: Orbital edge computing processes sensed data in orbit instead of sending raw data to the ground, alleviating downlink burden and potentially reducing edge-processing latency.The described orbital architecture uses satellites and ground stations, with satellites organized into computational pipelines.
  • Applications: Global coverage makes orbital edge computing applicable to Earth observation, space exploration, smart cities, emergency relief, weather reporting, military missions, and intelligent transportation.On-orbit image recognition is presented as an Earth-observation example that can provide processed images directly to requesters.
  • Challenges: Radiation threatens orbital computing hardware because solar flares and cosmic radiation can affect RAM, CPUs, and GPUs.The passage notes that radiation can cause RAM bit flips and application reboots, motivating careful nanosatellite hardware design.
  • Challenges: GEO and MEO systems face large one-way delays, with approximately 270 ms for GEO and 110 ms for MEO, which can degrade real-time applications.Intermittently available downlinks also contribute to high latency in orbital edge computing.
  • Challenges: Nanosatellite energy supply and computing capacity are limited, making high-energy applications such as deep learning impractical on a single nanosatellite.The limitation is attributed to the small satellite size and restricted solar-panel surface area.
  • Aerial edge computing: Aerial edge computing offers flexible deployment, agile management, large coverage, and low cost compared with terrestrial edge computing.The passage identifies HAPs, including UAVs and balloons, as carriers for data transmission and task execution.

1) Architecture:

The proposed EC-SAGIN architecture combines orbital, aerial, and terrestrial edge resources, extending edge services to remote vehicles. Its layers support computation, relaying, caching, and connectivity, but aerial platforms remain energy- and coverage-constrained.

  • Existing aerial systems: Existing aerial edge-computing studies use UAVs for IoT services such as vehicular VR/AR gaming, crowd sensing, and urban monitoring.Reported challenges include battery constraints, weight-versus-flight-time trade-offs, and unstable links caused by mobility.
  • Architecture: EC-SAGINs are hierarchical structures combining orbital, aerial, and terrestrial edge-computing layers.The architecture is introduced as a layered system for edge computing in space-air-ground integrated networks.
  • LEO layer: LEO satellites provide onboard CPU and GPU resources for local processing when ground-station backhaul is unavailable, or relay data when their resources are occupied.This dual server-relay role supports vehicles and raw-data transfer under changing coverage and resource conditions.
  • Relay layer: GEO and MEO satellites relay raw data for LEO satellites or terrestrial vehicles, but this relaying can produce much higher transmission delay than LEO transmission.A remote vehicle can connect through a nearby LEO satellite even when that satellite is outside ground-station coverage.

3) Aerial Layer:

The EC-SAGIN system model combines HAP, terrestrial, satellite, and cloud resources, while emphasizing orbital offloading and caching for remote vehicles. Tasks are partitioned into dependent subtasks that may be computed or transmitted.

  • Aerial and terrestrial layers: The aerial layer uses edge-enabled HAPs for broadband connectivity, real-time edge computing, computation offloading, and caching, while the terrestrial layer includes MEC facilities and ground stations.Ground stations analyze LEO-received data or forward it to a remote data center.
  • Orbital focus: The proposed scheme focuses on orbital edge computing and targets real-time services for vehicles in remote areas without TEC infrastructure.Vehicles first offload tasks to nearby LEO satellites, which then decide whether to offload received data and whether to cache required data.
  • System model: The system model includes a remote vehicle, edge-enabled LEO satellites, MEO/GEO relay satellites, and a ground cloud server.Vehicles include terrestrial, aerial, and marine platforms such as automobiles, UAVs, liners, and warships.
  • Task model: A computational task is split into multiple dependent subtasks that a LEO satellite may execute locally or offload to a ground station.DNN model partitioning is used as an example of fine-grained task partitioning.
  • Task model: Each subtask may represent computation or data transmission, with workload determined from input data and the subtask’s CPU-cycle complexity.The model distinguishes input data, output data, and required CPU cycles per input byte.

B. Satellite Coverage Time Model

The coverage-time and communication models describe when a moving LEO satellite can serve a vehicle and how vehicle-to-satellite transmission is evaluated. Coverage depends on orbital geometry, while the link rate accounts for bandwidth, power, fading, noise, and rain attenuation.

  • Satellite coverage: LEO satellites serve vehicles only during a limited satellite-coverage period because their lower altitudes are accompanied by higher orbital velocity.The model defines the vehicle’s remaining coverage time for each satellite pass.
  • Coverage-time model: The coverage-time model uses satellite and Earth angular velocities, orbital inclination, minimum elevation geometry, and the minimum angular distance to the vehicle.θ0 depends on the minimum elevation angle, satellite altitude, and Earth radius; θm captures the satellite ground trace’s closest angular distance.
  • Communication model: EC-SAGIN communication is modeled through separate vehicle-to-satellite and satellite-to-ground interfaces using different spectrum bands without mutual interference.The model analyzes transmission delay for both interfaces.
  • Vehicle-to-satellite link: The vehicle-to-satellite link uses Ku-band, whose channel condition is affected by communication distance and rain attenuation.The rain attenuation ratio is fixed in the paper for simplicity, while bandwidth, vehicle power, noise, and channel fading characterize the rate.
  • Transmission delay: Vehicle-to-satellite transmission delay includes propagation delay because communication distance to the LEO satellite substantially affects performance.The propagation delay is represented explicitly in the communication model.

2) Satellite-to-ground:

The EC-SAGIN computing and caching model combines satellite-to-ground transmission, local processing, task offloading, and popularity-based caching. It represents cache placement for task outputs and accounts for relay delay when direct ground-station coverage is unavailable.

  • Ka-band satellite-to-ground backhaul supports high-speed data transmission between LEO satellites and ground stations or relay satellites.The satellite-to-ground backhaul link occupies wider bandwidth than the fronthaul link.
  • LEO satellites use relay satellites when outside ground-station coverage, so relay delay must be included.
  • Task processing decisions include transmitting data to the ground station or processing it locally at the LEO satellite.
  • A popularity-based policy estimates task-output request probabilities, with δ controlling whether popularity is uniform or skewed.
  • The caching placement matrix uses k_l = 1 for cached task outputs and k_l = 0 for outputs that are not cached.

VI. PROBLEM FORMULATION

The framework formulates EC-SAGIN control as a reinforcement-learning or Markov decision process problem aimed at minimizing task completion time and satellite resource usage.

  • The EC-SAGIN optimization problem is modeled as a Markov decision process with state, action, and reward functions.
  • The framework’s objective is to minimize both task completion time and satellite resource usage.

A. State Space

The state and action representations capture task, network, satellite, and caching conditions, while fine-grained actions specify offloading and caching for each sub-task.

  • State Space: The state space includes task state, networking state, satellite state, and caching placement state.
  • Action Space: The action matrix jointly represents fine-grained offloading and caching decisions for the current task.
  • Action Space: An offloading value of 1 sends a sub-task to the ground station, while 0 means it is not offloaded.
  • Action Space: A caching value of 1 stores a sub-task output, while 0 indicates that the output is not cached.
  • State Transition: State transitions specify the probability of reaching S(t + 1) after taking action A(t) in state S(t).

D. Reward Function

The framework defines reward components for resource usage and task completion, then uses action pre-classification and offline deep imitation learning to make fine-grained decisions more efficiently.

  • Reward Function: The optimization seeks an offloading and caching decision that minimizes satellite resource usage and task completion time.
  • Reward Function: The reward combines computation, communication, caching, and task-completion components weighted by resource prices and a completion-time factor.
  • Pre-classified Offloading and Caching Scheme: Action complexity grows exponentially with sub-task count, reaching o(4^|V|) for the optimal decision problem.
  • Pre-classified Offloading and Caching Scheme: Pre-classification removes impossible offloading and caching actions based on sub-task type and satellite coverage conditions.
  • Decision-Making Scheme: The reduced action space is followed by a deep imitation learning decision-making scheme.

B. Deep Imitation Learning-based Decision Making Scheme

The scheme trains a deep imitation learning model offline to make joint offloading and caching decisions for EC-SAGIN tasks in real time. Simulations compare DOCS with benchmark policies across reward, action accuracy, task completion time, model depth, and rain attenuation.

  • Decision-making method: Offline deep imitation learning trains DOCS from optimized demonstrations generated at the ground station.The satellite sends system states to the ground, where optimal decision samples are generated and used for offline DNN training.
  • Decision-making method: Pre-classification reduces the action space, while DOCS jointly selects offloading and caching actions for each task’s sub-tasks.The output layer contains |V| neurons representing joint actions for the |V| sub-tasks.
  • Simulation results: DOCS reduces system reward by 19.74%, 29.40%, 23.26%, 12.95%, 13.85%, and 10.08% relative to TO-MRC, LE-MRC, TO-MPC, LE-MPC, GO-MRC, and GO-MPC, respectively.The comparison uses local, total-offloading, greedy, most-recent-caching, and most-popular-caching benchmarks.
  • Simulation results: 36.52% is DOCS’s highest accuracy for joint offloading and caching actions relative to the optimal actions.The reported advantage is attributed to imitating optimal actions through offline training.
  • Simulation results: 53.75% is the maximum task-completion-time reduction achieved by DOCS compared with total offloading schemes.The evaluation reports task completion time for different offloading and caching schemes.
  • Sensitivity and deployment: Both DIL and DRL accuracy peak at three hidden layers, while DIL supports offline training and real-time online decisions.Training can be moved to a more powerful ground station, reducing computation and energy demands on the satellite.
  • Sensitivity and deployment: Rain attenuation has little effect on accuracy, indicating robustness to weather between the LEO satellite and ground station.The evaluation also notes unstable task completion time caused by long GEO-relay propagation latencies.

IX. FUTURE DIRECTIONS

The paper identifies artificial intelligence, resource allocation, security and privacy, and hardware design as directions for integrating edge computing into SAGINs.

  • Future directions: Future SAGIN edge-computing research should address artificial intelligence, resource allocation, security and privacy, and hardware design.These directions reflect the complexity of integrating edge computing across hierarchical space, air, and ground networks.

A. Artificial Intelligence

The paper highlights AI as a tool for optimizing complex SAGIN edge computing, while emphasizing resource, security, privacy, and hardware constraints. It proposes placing computation-intensive training on ground stations and retaining delay-sensitive decisions locally.

  • Artificial intelligence and resource allocation: AI methods are promising for optimizing edge computing in SAGINs, whose hierarchical structure makes integration more complex than in terrestrial networks.Deep learning is identified as a state-of-the-art tool for improving SAGIN edge-computing performance.
  • Artificial intelligence and resource allocation: Energy-limited orbital nodes motivate offloading computation-intensive training to ground stations while keeping delay-intensive online decisions on satellites.This division is presented as a way to accommodate the higher resource consumption of AI methods.
  • Artificial intelligence and resource allocation: Real-time, energy-efficient resource allocation must track dynamic node states such as battery level, location, speed, and available storage.SAGIN resource allocation must account for satellite trajectories, UAV movement, multidimensional resources, and limited energy supplies.
  • Security and privacy: Open links, high mobility, and network dynamics expose EC-SAGINs to message tampering, malicious attacks, and jamming.Security mechanisms must protect privacy-intensive data without imposing excessive delay and energy overhead.
  • Hardware design: Hardware integration requires radiation protection, sufficient GPU resources for deep learning, and attention to the expense of large-scale satellite systems.Radiation is identified as the biggest threat to orbital edge computing hardware.
  • Paper-level synthesis: The proposed EC-SAGIN framework combines joint offloading and caching with pre-classification and DIL-based decision making for IoV services in remote areas.Numerical results are reported to outperform benchmark policies.
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