Design of data analysis means for IoT monitoring systems of the road surface condition




ІoT, SmartCity, monitoring system, road surface, STM32, Convolutional Neural Network


The article is devoted to the development of data processing tools for road surface condition monitoring and maintenance systems based on Internet of Things (IoT) technologies. Data processing processes are complicated by the need to collect an excessive amount of data from IoT devices and implement algorithms for their processing with high computational complexity in real time.

The method of classifying indicators of linear acceleration of the accelerometer using CNN convolutional neural networks To identify road irregularities is proposed. The use of a trained neural network for the classification of road irregularities made it possible to increase the accuracy of information about the condition of the road surface by 6% and ensure the implementation of analytical algorithms for processing IoT data in real time. Experiments shown that the developed tools allow quick detection and reliable identification of road surface irregularities on arbitrary terrain. The proposed tools can be used in maintenance systems by smart cities, as well as to improve the quality of life of drivers and prevent critical situations related to poor road surfaces.


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