299 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
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
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
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
Edge computing and communication for energy-efficient earth surveillance with LEO satellites
Modern satellites deployed in low Earth orbit (LEO) accommodate processing payloads that can be exploited for edge computing. Furthermore, by implementing inter-satellite links, the LEO satellites in a constellation can route the data end-toend (E2E). These capabilities can be exploited to greatly improve the current store-and-forward approaches in Earth surveillance systems. However, they give rise to an NP-hard problem of joint communication and edge computing resource management (RM). In this paper, we propose an algorithm that allows the satellites to select between computing the tasks at the edge or at a cloud server and to allocate an adequate power for communication. The overall objective is to minimize the energy consumption at the satellites while fulfilling specific service E2E latency constraints for the computing tasks. Experimental results show that our algorithm achieves energy savings of up to 18% when compared to the selected benchmarks with either 1) fixed edge computing decisions or 2) maximum power allocation.Part of the research has been supported by the project SatNEx-V, co-funded by the European Space Agency (ESA). This work has also received funding by the Spanish ministry of science and innovation under project IRENE (PID2020-115323RB-C31 / AEI / 10.13039/501100011033) and grant from the Spanish ministry of economic affairs and digital transformation and of the European union – NextGenerationEU [UNICO-5G I+D/AROMA3D-Space (TSI-063000-2021-70).Peer ReviewedPostprint (author's final draft
Edge Computing and Communication for Energy-Efficient Earth Surveillance with LEO Satellites
Modern satellites deployed in low Earth orbit (LEO) accommodate processing
payloads that can be exploited for edge computing. Furthermore, by implementing
inter-satellite links, the LEO satellites in a constellation can route the data
end-to-end (E2E). These capabilities can be exploited to greatly improve the
current store-and-forward approaches in Earth surveillance systems. However,
they give rise to an NP-hard problem of joint communication and edge computing
resource management (RM). In this paper, we propose an algorithm that allows
the satellites to select between computing the tasks at the edge or at a cloud
server and to allocate an adequate power for communication. The overall
objective is to minimize the energy consumption at the satellites while
fulfilling specific service E2E latency constraints for the computing tasks.
Experimental results show that our algorithm achieves energy savings of up to
18% when compared to the selected benchmarks with either 1) fixed edge
computing decisions or 2) maximum power allocation.Comment: Submitted to ICC 202
Energy-efficient satellite joint computation and communication
The emerging interest in satellite networks will be a key driver in the path to 6G. The satellite segment must be conceived beyond a mere relay system, where nodes can process data and offload the terrestrial segment. Besides, evidence suggests that energy consumption is among the most important factors for the design of future communication networks. For this motivation, we introduce Sat2C, an energy-efficient algorithm for satellite joint routing, radio resource allocation and task offloading for latency-constrained services. We develop a novel energy model that incorporates the power amplifier subsystem and changes the geometry of the problem. Regarding the routing task, we propose the SHIELD algorithm, based on the submodularity framework and which achieves Pareto-efficient routes. Besides, the RRM problem is formulated as a log-log convex program. The experimental results reveal that Sat2C has low computational complexity, provides routes with low variance in the mean distance and the transmission powers are optimal to ensure energy minimization
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