Optimal prediction of user mobility based on spatio-temporal matching

Abstract

Position tracking has become a critical key component for a huge variety of devices, ranging from mobile telephone location tracking to biodiversity monitoring. The majority of location-based services rely mostly on the user’s ongoing and prospective position, indicating a growing need of forecasting the user’s future location. Together with position prediction, forecasting the trajectories between two terminals is beneficial, because it enables to optimize the travel direction between them. This study tackles the problem of increasing prediction accuracy to its maximum level. The proposed work undergoes two major phases: feature extraction and prediction. Initially, antecedent and consequent features, spatio-temporal matching based features, and matching users based features can all be generated from the raw input data. For more precise prediction the most relevant features are extracted. The features will then be fed into the prediction algorithm, which will forecast user mobility. The prediction phase is constructed with an optimized convolutional neural network (CNN). Moreover, the weight of CNN is fine-tuned via a new improved butterfly optimization algorithm (IBOA), which is a conceptual improvement of standard BOA. At last, the supremacy of the presented approach is proved over other models with respect to varied measures. The accuracy of the proposed work is 18.33%, 26.67%, 33.33%, 55.2%, and 61.67% better than the existing models like HS–EH, GAF-WO, CNN, and GSTF.