Abstract
In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction.
Highlights
Sensing technology plays an increasingly important role in many fields, especially in the field of public security
It is proposed in this paper that Convolutional Neural Network (CNN) structures can be applied in a novel way in Computer Vision (CV) sensing technology to process maps with terrain profiles or shadow fading information, so that the related pathloss model or statistical properties of shadow fading can be obtained directly, which can be used in a forest safety monitoring system
CNNs with multiple outputs are used to predict a batch of diffraction losses at multiple locations in a map with very high efficiency
Summary
Sensing technology plays an increasingly important role in many fields, especially in the field of public security. Forest fires have the characteristics of being sudden, random, and can cause huge losses in a short time. For this reason, video acquisition and coding are carried out at the monitoring points in important forestry areas by placing cameras, and the monitoring system server of the monitoring center is connected through wireless channels, while the video monitoring system software is used for centralized display and unified management. In the construction process of forestry security monitoring system, ensuring that the cameras are always in the signal coverage range of the central base station and other wireless communication facilities is the basis of guaranteeing the video transmission back. The location of the camera can be supported by the propagation range of the wireless signal centered on the base station where the location is determined
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