Abstract: In the case of the sparse distribution of users in the perception area, predicting the user's location in advance is the key to improve the task completion rate of the crowd sensing system. This paper presents a user location prediction model based on the gated recurrent unit. Firstly, a model of the crowd sensing system is constructed, and the application of the participatory sensing based on location is realized. Secondly, the data set of the user's location is normalized, and by combining the multidimensional characteristics of the user's historical location data, a gated recurrent unit structure is constructed. Finally, the actual trajectory data set in the vehicles networks is used to train the model, and the Adam algorithm is used to optimize the performance parameters of the user position prediction model based on the gated recurrent unit. The simulation results show that, compared with the RNN model and the LSTM model, the prediction mean square error of the proposed model is reduced by 22% and 18% respectively, and has the advantage of strong implementability in processing sequence data.

Key words: crowd sensing, gated recurrent unit, location prediction, Adam algorithm