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Location-Aware Deep Neural Network Review

· 3 min read
Gracefullight
Gracefullight
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Architecture​

Wall Detection​

Wall Detection Process

  1. Pre-process floor images to gray scale.
  2. Apply Otsu's threshold and thinned method.
  3. Enhance the contrast of wall lines using Canny edge detection.
  4. Employ Hough Transform to detect and map wall lines.

3D Visualization​

Limitations​

  • Building structural details are unavailable or drone operations are restricted.
  • Highly irregular floor plans or buildings constructed with unique materials not extensively represented in the training data.
  • Real-time data integration
  • Refined deep learning architectures, and validation across varied building materials and layouts
  • Enhance the framework’s scalability and practical utility.

Spider GAN​

Sample

{
  "nodes": [
    { "id": 0, "name": "Drone",   "type": "source",   "features": [1, 0, 0, 0, 0.0] },
    { "id": 1, "name": "Room1",   "type": "room",     "features": [0, 1, 0, 0, -70.0] },
    { "id": 2, "name": "Room2",   "type": "room",     "features": [0, 1, 0, 0, -75.0] },
    { "id": 3, "name": "Room3",   "type": "room",     "features": [0, 1, 0, 0, -80.0] },
    { "id": 4, "name": "Corridor","type": "corridor", "features": [0, 0, 1, 0, -72.0] },
    { "id": 5, "name": "Wall_R1_R2","type": "wall",  "features": [0, 0, 0, 1, 0.0] },
    { "id": 6, "name": "Wall_R2_R3","type": "wall",  "features": [0, 0, 0, 1, 0.0] }
  ],

  "edges": [
    { "source": 0, "target": 1, "attenuation_db": 5.0  },
    { "source": 1, "target": 5, "attenuation_db": 8.0  },
    { "source": 5, "target": 2, "attenuation_db": 8.0  },
    { "source": 2, "target": 6, "attenuation_db": 10.0 },
    { "source": 6, "target": 3, "attenuation_db": 10.0 },
    { "source": 2, "target": 4, "attenuation_db": 3.0  }
  ]
}

Ref​

  • Hason Rudd, D., Sanin, C., En, K. M., Gao, X., Islam, M. R., Hasan, M., Wang, X., Huo, A., & Xu, G. (2025). Location-Aware Deep Neural Network for Predicting Indoor 5G RSSI and CQI Using Drone-Based External RF Sensing. Procedia Computer Science, 270, 4765–4775. https://doi.org/10.1016/j.procs.2025.09.602