Sparse Subsystem Discovery for Intelligent Sensor Networks

Sun, Heli; Liu, Xuechun; Sun, Miaomiao; Cao, Ruichen; Xing, Bin; He, Liang; He, Hui

doi:10.3390/s26010288

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Open AccessArticle

Sparse Subsystem Discovery for Intelligent Sensor Networks

by

Heli Sun

^1,2,

Xuechun Liu

²,

Miaomiao Sun

²,

Ruichen Cao

²

,

Bin Xing

²,

Liang He

² and

Hui He

^2,*

¹

State Key Laboratory of Communication Content Cognition, Beijing 100733, China

²

Faculty of Electronic and Information Engineering, School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China

^*

Author to whom correspondence should be addressed.

Sensors 2026, 26(1), 288; https://doi.org/10.3390/s26010288

Submission received: 10 November 2025 / Revised: 24 December 2025 / Accepted: 30 December 2025 / Published: 2 January 2026

(This article belongs to the Special Issue New Technologies in Wireless Communication System)

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Abstract

The Sparse Subgraph Finding (SGF) problem addresses the challenge of identifying sub–graphs with weak social interactions and sparse connections within a graph, which can be effectively modeled as discovering sparse subsystems in intelligent sensor networks. Traditional methods often rely on manually designed heuristics, which are computationally expensive and lack scalability, especially when dealing with complex sensor network systems. In this paper, we propose RL–SGF, a novel framework that integrates deep reinforcement learning and graph embedding through joint optimization to overcome these limitations. By simultaneously optimizing subsystem sparsity and representation learning within a unified framework, RL–SGF enhances both the effectiveness and robustness of the model in sensor network applications. Experimental results on synthetic and real-world datasets, including social networks, citation networks, and sensor network simulations, demonstrate that RL–SGF outperforms existing algorithms in terms of efficiency and solution quality, making it highly applicable to real-world sparse subsystem discovery scenarios in intelligent sensor networks.

Keywords: sparse subsystem discovery; reinforcement learning; graph neural network

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MDPI and ACS Style

Sun, H.; Liu, X.; Sun, M.; Cao, R.; Xing, B.; He, L.; He, H. Sparse Subsystem Discovery for Intelligent Sensor Networks. Sensors 2026, 26, 288. https://doi.org/10.3390/s26010288

AMA Style

Sun H, Liu X, Sun M, Cao R, Xing B, He L, He H. Sparse Subsystem Discovery for Intelligent Sensor Networks. Sensors. 2026; 26(1):288. https://doi.org/10.3390/s26010288

Chicago/Turabian Style

Sun, Heli, Xuechun Liu, Miaomiao Sun, Ruichen Cao, Bin Xing, Liang He, and Hui He. 2026. "Sparse Subsystem Discovery for Intelligent Sensor Networks" Sensors 26, no. 1: 288. https://doi.org/10.3390/s26010288

APA Style

Sun, H., Liu, X., Sun, M., Cao, R., Xing, B., He, L., & He, H. (2026). Sparse Subsystem Discovery for Intelligent Sensor Networks. Sensors, 26(1), 288. https://doi.org/10.3390/s26010288

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