Multi objective reinforcement learning driven task offloading algorithm for satellite edge computing networks.

Sai Xu, Jun Liu, Jiawei Tang, Xiangjun Liu, Zhi Li

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Sai XuSchool of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China. xu_s@neusoft.com.

Jun LiuSchool of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China. liujun@cse.neu.edu.cn.

Jiawei TangSchool of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China.

Xiangjun LiuCS&S Information System Engineering Co.,Ltd, Beijing, 100081, China.

Zhi LiSchool of Information Science and Engineering, Shenyang Ligong University, Shenyang, China.

Funding

National Natural Science Foundation of China 61701100Shenyang Science and Technology Bureau Young and Middle-aged Talent Support Program RC230142

6 · The paper itself

Abstract

Satellite edge computing (SEC) has become a revolutionary paradigm to improve the quality of service, reduce the pressure on satellite-terrestrial backhaul bandwidth and reduce the average response delay of task requests. In this paper, we propose a task offloading algorithm based on K-D3QN to meet the rapidly growing demand of ground users. This algorithm improves the DQN algorithm by incorporating a satellite resource clustering module, a DDQN algorithm, and a competitive network mechanism module. The offloading decision-making process comprehensively considers three optimization objectives: task latency, resource utilization, and load-balancing degree, to achieve dynamic multi-objective optimization. Experimental results shown that the algorithm significantly reduces task latency, improves resource utilization and load-balancing degree.

Indexed as

DQNReinforcement learningSatellite edge computingSpace-based information networkTask offloading

Identifiers

PMID40617977

DOI10.1038/s41598-025-10553-6

PMCPMC12228679

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.