This paper studies the location management problem in cellular wireless networks. We propose a Handoff-Velocity Prediction (HVP) scheme to minimize the paging cost in searching a mobile terminal. HVP is based on the assumptions that the movement behavior of mobile terminals has temporal and spatial properties. Based on handoff statistics, the system maintains a handoff graph to describe the movement probabilities of a mobile terminal in a cell to the neighboring cells within a location area. Combining with the velocity information of a mobile terminal, we calculate the probabilities of finding the mobile terminal in the cells within the paging area. Then, the paging of the mobile terminal follows the cell probabilities to minimize the paging cost of a mobile terminal. Analysis on HVP has been performed to calculate the optimal threshold for update generation to minimize the total cost in location management. A group-paging scheme based on non-linear programming technique is suggested to limit the paging delay within the quality of services (QoS) requirement in call connection delay and at the same time to minimize the paging cost. In 3G networks and the next generation wireless networks, different connection requests may have different QoS requirements in connection delay. Extensive experiments have been performed to investigate the performance characteristics of HVP as comparing with the adaptive distance-based (ADB) method, the direction-based location update (DBLU) method and the basic velocity paging (BVP) method under different system settings. The results have shown that HVP gives a better performance comparing with ADB, DBLU and BVP for different CMR values and update cost to paging cost ratios.

Sequential pattern mining is a well-studied problem. In the context of mobile computing, a special sequential pattern, moving sequential pattern that reflects the moving behavior of mobile users attracted researchers ’ interests recently. Moving sequential patterns can be viewed as a special type of conventional sequential pattern with the extension of support. Mining moving sequential patterns has great significance for effective and efficient location management in wireless communication systems. In this paper a novel and efficient technique is proposed to mine moving sequential patterns. Firstly the idea of clustering is introduced to process the original moving histories into moving sequences as a preprocessing step. And then an efficient algorithm, called PrefixTree, is presented to mine the moving sequences. Performance study shows that PrefixTree outperforms LM algorithm, which is revised to mine moving sequences, in mining large moving sequence databases.

In this paper, we devise data allocation algorithms that can utilize the knowledge of user moving patterns for proper allocation of shared data in a mobile computing system. By employing the data allocation algorithms devised, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. The data allocation algorithms for shared data, which are able to achieve local optimization and global optimization, are developed. Local optimization refers to the optimization that the likelihood of local data access by an individual mobile user is maximized whereas global optimization refers to the optimization that the likelihood of local data access by all mobile users is maximized. Specifically, by exploring the features of local optimization and global optimization, we devise algorithm SD-local and algorithm SD-global to achieve local optimization and global optimization, respectively. In general, the mobile users are divided into two types, namely, frequently moving users and infrequently moving users. A measurement, called closeness measure which corresponds to the amount of the intersection between the set of frequently moving user patterns and that of infrequently moving user patterns, is derived to assess the quality of solutions provided by SD-local and SD-global. Performance of these data allocation algorithms is comparatively analyzed. From the analysis of SD-local and SD-global, it is shown that SD-local favors infrequently moving users whereas SD-global is good for frequently moving users. The simulation results show that the knowledge obtained from the user moving patterns is very important in devising effective data allocation algorithms which can lead to prominent performance improvement in a mobile computing system.

A location management scheme in wireless networks must effectively handle both user location update and search operations. Replication and forwarding are two well-known techniques to reduce user search and update costs, respectively, with replication being most effective when the call to mobility ratio (CMR) of the user is high, while forwarding is most effective when the CMR value is low. Thus, based on the user's CMR, the system can adopt a CMR threshold-based scheme such that if the user's CMR is lower than a threshold, then the system applies the forwarding scheme; otherwise, it applies the replication scheme. Applying different location management schemes based on per-user CMR values introduces undesirable high complexity in managing and maintaining location-related information stored in the system as different system support mechanisms must be applied to different users. In this paper, we quantitatively analyze a hybrid replication with forwarding scheme that can be uniformly applied to all users.

In mobile environments, mobile device users access and transfer a great deal of data through the online servers. In order to enhance users ’ access speed in a wireless network, decentralizing replicated servers appropriately in the network is required. Previous work regarding this issue had focused on the placement of replicated servers along with the moving paths of the users to maximize the hit ratio. When a miss occurs, they simply ignored the file request. Therefore, we suggest a solution to take care of such a miss by sending a file request to a replicated server nearby in the network. This paper is to propose new cost-effective wireless access algorithms incorporating a present replicated server allocation algorithm with more keen analysis of the moving patterns of mobile device users. We propose four different algorithms that allocate available replicated servers in the network so as to minimize the communication costs. The experimental results show that, among the proposed algorithms, the replicated server clustering algorithm allocated replicated servers with near optimal communication costs.