The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 237–242, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-237-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 237–242, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-237-2021

  30 Jun 2021

30 Jun 2021

ANALYSIS OF THE ATTACKER AND DEFENDER GAN MODELS FOR THE INDOOR NAVIGATION NETWORK

L. Niu1, Y. Song2, J. Chu1, and S. Li1 L. Niu et al.
  • 1School of Geomatics, 730072, Anningxilu, Lanzhou, China
  • 2School of Geographic and Environmental Science, Normal University of Tianjin, West Bin Shui Avenue, 300387 Tianjin, China

Keywords: Deep learning, Generative-Adversarial-Neural network, attacker, defender, indoor navigation network

Abstract. Evacuation research relies heavily on the efficiency analysis of the study navigation networks, and this principle also applies to indoor scenarios. One crucial type of these scenarios is the attacker and defender topic, which discusses the paralyzing and recovering operations for a specific indoor navigation network. Our approach is to apply the Generative-Adversarial-Neural network (GAN) model to optimize both reduction and increase operations for a specific indoor navigation network. In other words, the proposed model utilizes GAN both in the attacking behavior efficiency analysis and the recovering behavior efficiency analysis. To this purpose, we design a black box of training the generative model and adversarial model to construct the hidden neural networks to mimic the human selection of choosing the critical nodes in the studying navigation networks. The experiment shows that the proposed model could alleviate the selection of nodes that significantly influence network transportation efficiency. Therefore, we could apply this model to disaster responding scenarios like fire evacuation and communication network recovery operations.