Yingru Jiang’s research while affiliated with Dalian Ocean University and other places

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Publications (4)


Schematic of the satellite edge network architecture
Task average delay vs. Satellite computing resources
Task average delay vs. Local computing resources
Task average delay vs. Number of DAG tasks
Task average delay vs. Number of sub-tasks
Offload Strategy for Edge Computing in Satellite Networks Based on Software Defined Network
  • Article
  • Full-text available

January 2025

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5 Reads

Zhiguo Liu

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Yuqing Gui

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Lin Wang

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Yingru Jiang

Satellite edge computing has garnered significant attention from researchers; however, processing a large volume of tasks within multi-node satellite networks still poses considerable challenges. The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers, making it necessary to implement effective task offloading scheduling to enhance user experience. In this paper, we propose a priority-based task scheduling strategy based on a Software-Defined Network (SDN) framework for satellite-terrestrial integrated networks, which clarifies the execution order of tasks based on their priority. Subsequently, we apply a Dueling-Double Deep Q-Network (DDQN) algorithm enhanced with prioritized experience replay to derive a computation offloading strategy, improving the experience replay mechanism within the Dueling-DDQN framework. Next, we utilize the Deep Deterministic Policy Gradient (DDPG) algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks. Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches, effectively reducing task processing latency and thus improving user experience and system efficiency.

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Research on spectrum sensing method of space-ground integrated networks based on two-level cooperative cognition

December 2023

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27 Reads

Wireless Networks

In an unknown network environment, satellites are easily disturbed by various noises, and the interference signals change sharply, resulting in a decline in the accuracy of the cooperative spectrum sensing. Thus, a hybrid two-stage cooperative spectrum sensing scheme is proposed in this paper. The geostationary earth orbit (GEO)/low earth orbit (LEO) two-layer constellation network architecture is adopted, secondary LEO satellites randomly form several coalitions. In the coalition, the Dempster–Shafer (D–S) evidence theory distributed cooperative spectrum sensing algorithm based on information entropy is used to perform a first-level fusion of sensing information, and a 1bit fusion result is generated. A centralized fusion method is adopted between coalitions, the 1bit fusion result is sent to the GEO fusion center, and the secondary fusion is performed through K-out-of-N (K-N) fusion rules, and the final perception result is obtained and broadcast to all secondary users in the coalition. The simulation results show that the proposed method is superior to the traditional fusion rules in terms of detection probability and total error rate in a low-Signal-to-noise ratio (low-SNR) environment, and improves the cooperative sensing performance.


Figure 9. The effect of transmission delay in increasing the data packet.
Experimental simulation parameters.
Satellite Network Security Routing Technology Based on Deep Learning and Trust Management

October 2023

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53 Reads

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3 Citations

Sensors

The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D–S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module’s training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput.


Figure 1. Architecture diagram of satellite edge network.
Figure 3. DAG completion rate versus satellite computing resources.
Comparison of the existing literature.
Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency

September 2023

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142 Reads

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1 Citation

Applied Sciences

Satellite edge computing has attracted the attention of many scholars, but the limited resources of satellite networks bring great difficulties to the processing of edge-computing-dependent tasks. Therefore, under the system model of the satellite-terrestrial joint network architecture, this paper proposes an efficient scheduling strategy based on task degrees and a resource allocation strategy based on the improved sparrow search algorithm, aiming at the low success rate of application processing caused by the dependency between tasks, limited resources, and unreasonable resource allocation in the satellite edge network, which leads to the decline in user experience. The scheduling strategy determines the processing order of tasks by selecting subtasks with an in-degree of 0 each time. The improved sparrow search algorithm incorporates opposition-based learning, random search mechanisms, and Cauchy mutation to enhance search capability and improve global convergence. By utilizing the improved sparrow search algorithm, an optimal resource allocation strategy is derived, resulting in reduced processing latency for subtasks. The simulation results show that the performance of the proposed algorithm is better than other baseline schemes and can improve the processing success rate of applications.

Citations (1)


... Liu et al. [25] presented a deep learning and trust managementbased secure satellite routing network system. Every satellite network in this study has anomaly recognition and trust management components implemented in order to assess the security of neighboring nodes because the methodology is based on the concept of global trust management. ...

Reference:

Traffic Prevention and Security Enhancement in VANET Using Deep Learning With Trusted Routing Aided Blockchain Technology
Satellite Network Security Routing Technology Based on Deep Learning and Trust Management

Sensors