Open Access

ARTICLE

Sensor Network Structure Recognition Based on P-law

Chuiju You¹, Guanjun Lin^1,*, Jinming Qiu¹, Ning Cao¹, Yundong Sun², Russell Higgs³

1 School of Information Engineering, Sanming University, Sanming, 365004, China
2 School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China
3 School of Mathematics and Statistics, University College Dublin, Dublin, Dublin 4, Ireland

* Corresponding Author: Guanjun Lin. Email:

Computer Systems Science and Engineering 2023, 46(2), 1277-1292. https://doi.org/10.32604/csse.2023.026150

Received 16 December 2021; Accepted 24 January 2022; Issue published 09 February 2023

Abstract

A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node. This method which builds on unit patulousness and core node signal propagation (called p-law) can rapidly and effectively achieve the global structure identification of a sensor graph network. Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering, the proposed method reveals the natural evolution process and law of graph network subgroup generation. Experimental results confirm the effectiveness, accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.

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Keywords

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