Much research which has shown the usage of social ties could improve the location predictive performance, but as the strength of social ties is varying constantly with time, using the movement data of user's close friends at different times could obtain a better predictive performance. A hybrid Markov location prediction algorithm based on dynamic social ties is presented. The time is divided by the absolute time (week) to mine the long-term changing trend of users' social ties, and then the movements of each week are projected to the workdays and weekends to find the changes of the social circle in different time slices. The segmented friends' movements are compared to the history of the user with our modified cross-sample entropy to discover the individuals who have the relatively high similarity with the user in different time intervals. Finally, the user's historical movement data and his friends' movements at different times which are assigned with the similarity weights are combined to build the hybrid Markov model. The experiments based on a real location-based social network dataset show the hybrid Markov location prediction algorithm could improve 15% predictive accuracy compared with the location prediction algorithms that consider the global strength of social ties.

In this Letter, we investigate the correlation rate of a random sequence data set which is collected by RFID (Radio Frequency IDentification) readers in an indoor location. Using a passive RFID tag introduces reading error, which causes a loss of original data. From the question of how sensing errors of RFID readers affect the location prediction algorithm used for context awareness services at home, we analyze the correlation rate of a collected data set with respect to RFID reader-sensing error rate. Through our analysis, we conclude that the prediction accuracy can be better or worse than the one of the original data streams according to the error rate. We suggest that the reader specification has to be satisfied by the error boundary which is found in this work for the tolerant location prediction.

Over the last decade there has been a rapid growth of wireless communication technology. Among numerous wireless network architectures, the personal communication services (PCS) networks and wireless local area networks (WLAN) have attracted lots of attention. One of the core functionalities in wireless networks is the location service that provides location information for subscriber services, emergency services, and various mobile networks internal operations. In this paper, an integrated location management mechanism is proposed for heterogeneous wireless networks that combine PCS networks and WLAN. Three major functionalities in the integrated location management scheme are the determination of the WLAN connectivity for a mobile terminal, the development of a local area location scheme for WLAN, and the location prediction module for PCS networks. This mechanism not only determines the location of a mobile client more precisely, but also reduces the cost of locating. The performance evaluation is conducted to demonstrate the effectiveness of the proposed mechanism for heterogeneous wireless networks.