A data transmission scheduling algorithm for rapid-response earth-observing operations

Li, Jun; Chen, Hao; Jing, Ning

doi:10.1016/j.cja.2014.02.014

Cited by 34 publications

(16 citation statements)

References 18 publications

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“…In coordinate planning [5,24,25], the observation tasks and the transmission tasks are scheduled together. Such studies are not much and still in the exploring stage.…”

Section: Relate Workmentioning

confidence: 99%

“…Such studies are not much and still in the exploring stage. Li et al [24] proposed a -shortest path genetic algorithm to balance the response time and resource utilization. Wang et al [25] studied the scheduling problem of four optical satellites and four SAR satellites and presented a prioritybased heuristic method to maximize the rewards of the images taken and transmitted.…”

Section: Relate Workmentioning

confidence: 99%

“…Constraint (23) defines the energy level at the end of each subgraph. Constraint (24) defines the data level at the end of each subgraph. Constraint (25) ensures that the energy level cannot be less than the lower bound of energy in each subgraph.…”

Section: Nondominated Subpaths Selectionmentioning

confidence: 99%

“…. , num( ) do (17) if ≲ then (18) ← \ ; (19) break; (20) else if ≳ then (21) ← \ ; (22) end if (23) end for (24) end for (25) end for (26) end for (27) * ← maxbenefit( ); / * * is the path with the maximum benefit in . * / Algorithm 2: Improved label-setting algorithm.…”

Section: Computational Experimentsmentioning

confidence: 99%

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Approximate Path Searching Method for Single‐Satellite Observation and Transmission Task Planning Problem

Peng

Chen

Zhang

et al. 2017

Mathematical Problems in Engineering

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Satellite task planning not only plans the observation tasks to collect images of the earth surface, but also schedules the transmission tasks to download images to the ground station for users’ using, which plays an important role in improving the efficiency of the satellite observation system. However, most of the work to our knowledge, scheduling the observation and transmission tasks separately, ignores the correlation between them in resource (e.g., energy and memory) consumption and acquisition. In this paper, we study the single-satellite observation and transmission task planning problem under a more accurate resource usage model. Two preprocessing strategies including graph partition and nondominated subpaths selection are used to decompose the problem, and an improved label-setting algorithm with the lower bound cutting strategy is proposed to maximize the total benefit. Finally, we compare the proposed method with other three algorithms based on three data sets, and the experimental result shows that our method can find the near-optimal solution in much less time.

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“…In coordinate planning [5,24,25], the observation tasks and the transmission tasks are scheduled together. Such studies are not much and still in the exploring stage.…”

Section: Relate Workmentioning

confidence: 99%

Section: Relate Workmentioning

confidence: 99%

Section: Nondominated Subpaths Selectionmentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Approximate Path Searching Method for Single‐Satellite Observation and Transmission Task Planning Problem

Peng

Chen

Zhang

et al. 2017

Mathematical Problems in Engineering

Self Cite

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“…Each paper has its own specific conditions. These conditions include types of satellite's operation (imaging, downloading or both) [1], maneuverability of satellites (not agile or agile) [2,3,4], number of tasks (single or multi) [2], number of satellites (single or constellation) [5,6,7,8,9,10,11,12,13], and number of ground stations (single or multi) [6,7].…”

Section: Introductionmentioning

confidence: 99%

Earth Observation Satellites Optimization; Survey and Analysis

Abbas

Meselhey

Mogheth

et al. 2018

The International Conference on Applied Mechanics and Mechanica

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The problem of earth observation satellites planning and scheduling contains many objectives and constraints. The objectives include downloaded data, profit of scheduled tasks and quality (weight) values. The constraints are related to energy, data, targets (tasks) and ground stations. Many optimization methods have been used to optimize this problem such as dynamic programming (DP), simulated annealing (SA), ant colony optimization (ACO), genetic algorithm (GA) and constraint programming approach (CPA). Each reported research used a harmonized combination of objective(s), constrains, and optimization techniques. The presented work investigates the anatomy of this optimization problem. It is also analyzes the findings of the previous relation between the optimization problem elements and gaps in past research. Furthermore shows that there is a gap between data's objectives (maximizing the total amount of downloaded data) and the targets' constraints (observation of tasks, scheduling and consecutive observation).The most widely used optimization methods are dynamic programming and heuristic algorithms, respectively. Moreover, the most widely objectives are those relative to profit. Few discussions are also presented concerning the constraints which involve data rate, range of ground stations and capacity of ground stations.

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