Edge-Distributed IoT Services Assist the Economic Sustainability of LEO Satellite Constellation Construction

Meng Zhang, Hongjian Shi and Ruhui Ma ()
Additional contact information
Meng Zhang: The School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Hongjian Shi: The School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Ruhui Ma: The School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Sustainability, 2024, vol. 16, issue 4, 1-20

Abstract: There are thousands or even tens of thousands of satellites in Low Earth Orbit (LEO). How to ensure the economic sustainability of LEO satellite constellation construction is an important issue currently. In this article, we envision integrating the popular and promising Internet of Things (IoT) technology with LEO satellite constellations to indirectly provide economic support for LEO satellite construction through paid IoT services. Of course, this can also bring benefits to the development of IoT. LEO Satellites can provide networks for IoT products in areas with difficult conditions, such as deserts, oceans, etc., and Satellite Edge Computing (SEC) can help to reduce the service latency of IoT. Many IoT products rely on Convolutional Neural Networks (CNNs) to provide services, and it is difficult to perform CNN inference on an edge server solely. Therefore, in this article, we use edge-distributed inference to enable the IoT services in the SEC scenario. How to perform edge-distributed inference to shorten inference time is a challenge. To shorten the inference latency of CNN, we propose a framework based on a joint partition, named EDIJP. We use a joint partition method combining data partition and model partition for distributed partition. We model the data partition as a Linear Programming (LP) problem. To address the challenge of trading off computation latency and communication latency, we designed an iterative algorithm to obtain the final partitioning result. By maintaining the original structure and parameters, our framework ensures that the inference accuracy will not be affected. We simulated the SEC environment, based on two popular CNN models, VGG16 and AlexNet, the performance of our method is varified. Compared with local inference, EdgeFlow, and CoEdge, the inference latency by using EDIJP is shorter.

Keywords: sustainability; Low Earth Orbit satellite; edge-distributed inference; Internet of Things; joint partition; linear programming (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/4/1599/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/4/1599/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:4:p:1599-:d:1338804

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().