EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles (2101.06056v1)

Abstract: 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.

• The paper proposes a novel EC-SAGIN framework that integrates space, air, and ground networks to enhance connectivity and resource efficiency for Internet of Vehicles (IoV).
• A deep imitation learning (DIL)-based algorithm is introduced to optimize dynamic task offloading and caching, significantly reducing task completion time and satellite resource usage.
• Simulation results demonstrate the proposed scheme's effectiveness in optimizing real-world edge computing performance, enabling more stable and efficient IoV services, especially in areas lacking terrestrial infrastructure.

Edge Computing-Enhanced Space-Air-Ground Integrated Networks: An Expert Overview

This paper explores the cutting-edge domain of edge computing-enhanced Space-Air-Ground Integrated Networks (EC-SAGINs) with specific applications for the Internet of Vehicles (IoV). The integration of traditionally disparate network layers—terrestrial, aerial, and orbital—forms a cohesive architecture aimed at overcoming limitations in coverage and resource availability inherent in isolated terrestrial edge computing systems.

Core Contributions

The paper begins with a comprehensive review of existing edge computing frameworks in SAGINs, pinpointing essential advancements in orbital and aerial computing architectures. The authors then propose a novel EC-SAGIN framework that promises to minimize task completion time and optimize satellite resource usage, a critical need for vehicular services in remote areas devoid of terrestrial infrastructure.

The framework's standout feature is a deep imitation learning (DIL)-based algorithm designed for dynamic offloading and caching. By leveraging pre-classification to streamline action space, this algorithm optimizes real-time decision-making while maintaining energy efficiency—a significant criterion given the stringent resource constraints of low Earth orbit (LEO) satellites.

Implications and Future Directions

The implications of EC-SAGINs are manifold:

• Practical Implications: For IoV, EC-SAGINs provide stable connectivity and real-time services in regions lacking terrestrial infrastructure, enhancing vehicle autonomy and responsiveness in critical scenarios like disaster relief and rural navigation.
• Theoretical Implications: The proposed system fosters a deeper understanding of integrating edge computing across multi-layered network architectures, focusing on seamless connectivity and resource management across diverse domains.

Looking ahead, several avenues merit exploration:

1. Artificial Intelligence: AI could further refine allocation and decision algorithms, addressing the exigency for intelligent, low-latency processing amidst dynamic network environments.
2. Resource Allocation and Management: Techniques that dynamically allocate satellite resources, taking into account their trajectory and limited energy supply, could further optimize performance.
3. Security and Privacy: Integrating secure transmission protocols and low-overhead privacy-preserving schemes within EC-SAGINs can protect sensitive data inherent in vehicular communications.
4. Hardware Design: As edge computing pushes to the forefront, advances in radiation-resistant satellite hardware and energy-efficient GPUs will be pivotal in supporting AI-driven applications.

Simulation Results and Analysis

The proposed DIL-based scheme demonstrated significant reductions in task completion time and resource usage compared to traditional methods. Notably, the system outperformances several benchmark strategies across key metrics, underscoring its efficacy in optimizing real-world edge computing within challenging environments.

Conclusion

In summary, the paper offers an insightful look into EC-SAGINs, positing a robust framework that synthesizes space-air-ground interconnectivity. It invites further research and refinement in edge computing methodologies, promising advancements that can propel IoV and other applications into new frontiers of connectivity and efficiency.