User location prediction with energy efficiency model in the Long Term‐Evolution network. (25th January 2015) - Qiao, Yuanyuan; Yang, Jie; He, Haiyang; Cheng, Yihang; Ma, Zhanyu
Summary: Predicting users' next location/place allows us to anticipate their future movement. It provides additional time to be ready for that movement and react consequently. Furthermore, many industries, including Internet Service Providers, are still requiring low cost and simple location/place prediction methods that can be implemented on mobile device. This paper studies domain‐independent prediction algorithms and spatio‐temporal based prediction method using 20‐day‐long records in Long Term‐Evolution(LTE) network, which captures the mobility patterns of 3474 individuals. After examining the prediction accuracy and resource consumption of domain‐independent prediction algorithms, we find Markov provides the best tradeoff. Furthermore, Active LeZi outperforms Markov if enough consecutive parsed patterns of users' history movement are captured. In addition, we further group users according to their spatio‐temporal entropy profiles in order to predict not only user's future locations but also the place he or she most likely to appear within a specific period. By applying the simple spatio‐temporal based method to each group of user, 83.3% accuracy can be achieved for some users. Yet Markov and Active LeZi algorithms perform better for some other users. This implies that we should consider applying different prediction methods to users with distinct spatio‐temporal characteristics. Copyright © 2015 John Wiley & Sons, Ltd. Abstract : This paper studies domain‐independent prediction algorithms and spatio‐temporal based prediction method using 20‐day‐long records in Long Term‐Evolution network, which captures the mobility patterns of 3474 individuals. We find Markov provides the best tradeoff between prediction accuracy and resource consumption among other domain‐independent prediction algorithms. In addition, simple spatio‐temporal based method achieved 83.3% accuracy for some users. As a result, we should consider applying different prediction methods to users with distinct spatio‐temporal characteristics.