Collaborative Data Scheduling With Joint Forward and Backward Induction in Small Satellite Networks

Zhou, Di; Sheng, Min; Luo, Jie; Liu, Runzi; Li, Jiandong; Han, Zhu

doi:10.1109/tcomm.2019.2900316

Cited by 56 publications

(26 citation statements)

References 35 publications

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“…In the development process of cloud computing, because different enterprises have different understandings of cloud, the business and service objects are also different. Therefore, the adopted cloud computing solution architecture will be different, and there is no unified standard for the corresponding architecture [ 11 , 12 ]. A comparative study is made on the cloud computing solution architectures of different enterprises.…”

Section: Cloud Computingmentioning

confidence: 99%

Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video

Zhang

Feng

Wang

2022

Computational Intelligence and Neuroscience

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With the rapid development of image video and tourism economy, tourism economic data are gradually becoming big data. Therefore, how to schedule between data has become a hot topic. This paper first summarizes the research results on image video, cloud computing, tourism economy, and data scheduling algorithms. Secondly, the origin, structure, development, and service types of cloud computing are expounded in detail. And in order to solve the problem of tourism economic data scheduling, this paper regards the completion time and cross-node transmission delay as the constraints of tourism economic data scheduling. The constraint model of data scheduling is established, the fitness function is improved on the basis of an artificial immune algorithm combined with the constraint model, and the directional recombination of excellent antibodies is carried out by using the advantages of gene recombination so as to obtain the optimal solution to the problem more appropriately. When the resource node scale is 100, the response time of EDSA is 107.92 seconds.

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Section: Cloud Computingmentioning

confidence: 99%

Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video

Zhang

Feng

Wang

2022

Computational Intelligence and Neuroscience

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“…Researchers [18] studied the scheduling of Earth observation satellite tasks with specific time requirements, proposed an automatic scheduling algorithm for state equations based on linear temporal logic (LTL, linear temporal logic), and introduced LTL semantics to automate constraints and time specifications and to formulate the parameters of the task specification appropriately. The research [19] on small satellite networks expands the traditional dynamic programming algorithm based on it. It proposes a finite embedded infinite two-layer dynamic programming framework, transforming the scheduling optimization problem into a discrete Markov decision process (MDP).…”

Section: Resource Scheduling Methods Based On Machine Learningmentioning

confidence: 99%

“…In that case, the gurobi solver (a widely used solver, we have obtained a license) is used to solve the optimal resource allocation route, and then the current unallocated resources are updated (lines 11-13). If no new resource requests are added, the optimal resource allocation plan is resolved using gurobi, then the currently unallocated resources are updated and returned to the current minimum time resource allocation plan (lines [18][19][20].…”

Section: Resources Type Sources Numbermentioning

confidence: 99%

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

Fan

Yang

et al. 2021

Mathematics

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To overcome the low timeliness of resource scheduling problems in spatial information networks, we propose a method based on a dynamic reconstruction of resource request queues and the autonomous coordinated scheduling of resources. First, we construct a small satellite network and combine the graph maximum flow theory to solve the link resource planning problem during inter-satellite data transmission. In addition, we design a multi-satellite resource scheduling algorithm with minimal time consumption based on graph theory. The algorithm is based on graph theory to reallocate the resource request queue to satellites with idle processing resources. Finally, we simulate the efficient resource scheduling capability in the spatial information network and empirically compare our approaches against two representative swarm intelligence baseline approaches and show that our approach has significant advantages in terms of performance and time consumption during resource scheduling.

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“…Therefore, data scheduling is necessary to employ the available transceivers efficiently. Hence, in [143], Zhou et al proposed a finite-embedded-infinite twolevel programming technique to schedule the data for CubeSats optimally. This technique considers stochastic data arrival and takes into account the joint considerations of battery management, buffer management, and contact selection.…”

Section: B Schedulingmentioning

confidence: 99%

CubeSat Communications: Recent Advances and Future Challenges

Saeed¹,

Elzanaty²,

Almorad³

et al. 2020

IEEE Commun. Surv. Tutorials

233

142

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Given the increasing number of space-related applications, research in the emerging space industry is becoming more and more attractive. One compelling area of current space research is the design of miniaturized satellites, known as Cube-Sats, which are enticing because of their numerous applications and low design-and-deployment cost. The new paradigm of connected space through CubeSats makes possible a wide range of applications, such as Earth remote sensing, space exploration, and rural connectivity. CubeSats further provide a complementary connectivity solution to the pervasive Internet of Things (IoT) networks, leading to a globally connected cyber-physical system. This paper presents a holistic overview of various aspects of CubeSat missions and provides a thorough review of the topic from both academic and industrial perspectives. We further present recent advances in the area of CubeSat communications, with an emphasis on constellation-and-coverage issues, channel modeling, modulation and coding, and networking. Finally, we identify several future research directions for CubeSat communications, including Internet of space things, low-power long-range networks, and machine learning for CubeSat resource allocation.

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Collaborative Data Scheduling With Joint Forward and Backward Induction in Small Satellite Networks

Cited by 56 publications

References 35 publications

Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video

Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

CubeSat Communications: Recent Advances and Future Challenges

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