508 research outputs found
Satellite-MEC Integration for 6G Internet of Things: Minimal Structures, Advances, and Prospects
The sixth-generation (6G) network is envisioned to shift its focus from the
service requirements of human beings' to those of Internet-of-Things (IoT)
devices'. Satellite communications are indispensable in 6G to support IoT
devices operating in rural or disastrous areas. However, satellite networks
face the inherent challenges of low data rate and large latency, which may not
support computation-intensive and delay-sensitive IoT applications. Mobile Edge
Computing (MEC) is a burgeoning paradigm by extending cloud computing
capabilities to the network edge. By utilizing MEC technologies, the
resource-limited IoT devices can access abundant computation resources with low
latency, which enables the highly demanding applications while meeting strict
delay requirements. Therefore, an integration of satellite communications and
MEC technologies is necessary to better enable 6G IoT. In this survey, we
provide a holistic overview of satellite-MEC integration. We first discuss the
main challenges of the integrated satellite-MEC network and propose three
minimal integrating structures. For each minimal structure, we summarize the
current advances in terms of their research topics, after which we discuss the
lessons learned and future directions of the minimal structure. Finally, we
outline potential research issues to envision a more intelligent, more secure,
and greener integrated satellite-MEC network
Deep Reinforcement Learning for Privacy-Preserving Task Offloading in Integrated Satellite-Terrestrial Networks
Satellite communication networks have attracted widespread attention for
seamless network coverage and collaborative computing. In satellite-terrestrial
networks, ground users can offload computing tasks to visible satellites that
with strong computational capabilities. Existing solutions on
satellite-assisted task computing generally focused on system performance
optimization such as task completion time and energy consumption. However, due
to the high-speed mobility pattern and unreliable communication channels,
existing methods still suffer from serious privacy leakages. In this paper, we
present an integrated satellite-terrestrial network to enable
satellite-assisted task offloading under dynamic mobility nature. We also
propose a privacy-preserving task offloading scheme to bridge the gap between
offloading performance and privacy leakage. In particular, we balance two
offloading privacy, called the usage pattern privacy and the location privacy,
with different offloading targets (e.g., completion time, energy consumption,
and communication reliability). Finally, we formulate it into a joint
optimization problem, and introduce a deep reinforcement learning-based
privacy-preserving algorithm for an optimal offloading policy. Experimental
results show that our proposed algorithm outperforms other benchmark algorithms
in terms of completion time, energy consumption, privacy-preserving level, and
communication reliability. We hope this work could provide improved solutions
for privacy-persevering task offloading in satellite-assisted edge computing
Cost-Efficient Computation Offloading and Service Chain Caching in LEO Satellite Networks
The ever-increasing demand for ubiquitous, continuous, and high-quality
services poses a great challenge to the traditional terrestrial network. To
mitigate this problem, the mobile-edge-computing-enhanced low earth orbit (LEO)
satellite network, which provides both communication connectivity and on-board
processing services, has emerged as an effective method. The main issue in LEO
satellites includes finding the optimal locations to host network functions
(NFs) and then making offloading decisions. In this article, we jointly
consider the problem of service chain caching and computation offloading to
minimize the overall cost, which consists of task latency and energy
consumption. In particular, the collaboration among satellites, the network
resource limitations, and the specific operation order of NFs in service chains
are taken into account. Then, the problem is formulated and linearized as an
integer linear programming model. Moreover, to accelerate the solution, we
provide a greedy algorithm with cubic time complexity. Numerical investigations
demonstrate the effectiveness of the proposed scheme, which can reduce the
overall cost by around 20% compared to the nominal case where NFs are served in
data centers.Comment: 10 pages, 3 figure
Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G
Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th
Generation (6G) networks, providing ubiquitous, continuous, and scalable
services. Satellites emerge as the primary enabler for NTN, leveraging their
extensive coverage, stable orbits, scalability, and adherence to international
regulations. However, satellite-based NTN presents unique challenges, including
long propagation delay, high Doppler shift, frequent handovers, spectrum
sharing complexities, and intricate beam and resource allocation, among others.
The integration of NTNs into existing terrestrial networks in 6G introduces a
range of novel challenges, including task offloading, network routing, network
slicing, and many more. To tackle all these obstacles, this paper proposes
Artificial Intelligence (AI) as a promising solution, harnessing its ability to
capture intricate correlations among diverse network parameters. We begin by
providing a comprehensive background on NTN and AI, highlighting the potential
of AI techniques in addressing various NTN challenges. Next, we present an
overview of existing works, emphasizing AI as an enabling tool for
satellite-based NTN, and explore potential research directions. Furthermore, we
discuss ongoing research efforts that aim to enable AI in satellite-based NTN
through software-defined implementations, while also discussing the associated
challenges. Finally, we conclude by providing insights and recommendations for
enabling AI-driven satellite-based NTN in future 6G networks.Comment: 40 pages, 19 Figure, 10 Tables, Surve
Energy-Efficient Design of Satellite-Terrestrial Computing in 6G Wireless Networks
In this paper, we investigate the issue of satellite-terrestrial computing in
the sixth generation (6G) wireless networks, where multiple terrestrial base
stations (BSs) and low earth orbit (LEO) satellites collaboratively provide
edge computing services to ground user equipments (GUEs) and space user
equipments (SUEs) over the world. In particular, we design a complete process
of satellite-terrestrial computing in terms of communication and computing
according to the characteristics of 6G wireless networks. In order to minimize
the weighted total energy consumption while ensuring delay requirements of
computing tasks, an energy-efficient satellite-terrestrial computing algorithm
is put forward by jointly optimizing offloading selection, beamforming design
and resource allocation. Finally, both theoretical analysis and simulation
results confirm fast convergence and superior performance of the proposed
algorithm for satellite-terrestrial computing in 6G wireless networks
A Comprehensive Survey on Orbital Edge Computing: Systems, Applications, and Algorithms
The number of satellites, especially those operating in low-earth orbit
(LEO), is exploding in recent years. Additionally, the use of COTS hardware
into those satellites enables a new paradigm of computing: orbital edge
computing (OEC). OEC entails more technically advanced steps compared to
single-satellite computing. This feature allows for vast design spaces with
multiple parameters, rendering several novel approaches feasible. The mobility
of LEO satellites in the network and limited resources of communication,
computation, and storage make it challenging to design an appropriate
scheduling algorithm for specific tasks in comparison to traditional
ground-based edge computing. This article comprehensively surveys the
significant areas of focus in orbital edge computing, which include protocol
optimization, mobility management, and resource allocation. This article
provides the first comprehensive survey of OEC. Previous survey papers have
only concentrated on ground-based edge computing or the integration of space
and ground technologies. This article presents a review of recent research from
2000 to 2023 on orbital edge computing that covers network design, computation
offloading, resource allocation, performance analysis, and optimization.
Moreover, having discussed several related works, both technological challenges
and future directions are highlighted in the field.Comment: 18 pages, 9 figures and 5 table
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