Future Locations Of Mobile Users Research Articles

Fuzzy-Neural based Cost Effective Handover Prediction Technique for 5G-IoT networks

Most of the existing works related to handover prediction in 5G networks, depends on huge mobility patterns collected over several periods of time, which will be tedious and complex to classify and analyze these patterns to predict the future locations of mobile users. Hence the main objective is to design a HO prediction technique which accurately predicts the next cell location with least amount of mobility history or patterns. In this paper, we design handoff prediction and target network selection scheme for 5G-IoT networks. For VHO triggering condition, Multi-layer Feed Forward Network (MFNN) is applied which will predict the user mobility based on distance, RSS, mobile speed and direction parameters. For target cell selection, Fuzzy decision model is applied based on the network level metrics such as traffic load, handover latency, battery power and user level metrics such as security and cost. The proposed approach will be implemented in NS3 and the performance is measured in terms of network throughput, handoff delay, handoff cost and prediction accuracy.

Services and Applications Based on Mobile User’s Location Detection and Prediction

Many wireless applications are deployed and available to customers via their mobile phones. Variety of these applications and services are based on determination of the current or future location of mobile user. Location based services (LBS) are one of the vital applications which are subdivided into two main categories: economical category and public category. Economic applications include mobile marketing, entertainment and tracking applications. Whereas, emergency cases, safety, traffic management, Muslims’ applications and public information applications are sort of public applications. The first part of the paper presents a new proposed system with developed procedure to recreate public and economic applications with high positioning accuracy and good authentication of users’ data. The developed system is created to enhance both location based services and network allocation resources within mobile network platform using either normal or GPS supported mobile equipment. The second part of the paper introduces future location prediction of mobile user dependent applications. New algorithm is developed depending on utilizing both intra-cell Movement Pattern algorithm (ICMP) [1] and hybrid uplink time Difference of Arrival and Assisted GPS technique (UTDOA_AGPS) [2]. It has been noticed that ICMP algorithm outperforms other future location prediction algorithms with high precision and within suitable time (less than 220) msec. However, UTDOA_AGPS guarantees high precession of mobile user independent of the surrounding environment. The proposed technique is used to enhance reliability and efficiency of location based services using cellular network platform.

Mobility Prediction in Dynamic Grids

Mobile grids are one of the emerging grid types. They help to pool the resources of several cooperative mobile devices to resolve a computationally intensive task. Mobility management is a key challenge in mobile grids which includes mechanisms to track and maintain the locations of mobile nodes. Mobility prediction is one of the most important components of mobility management, which defined as proactively tracking the future locations of mobile users. In this paper we introduce a straight forward mobility predictor that can predict the future locations of mobile users and the duration for which the users will remain in those locations.

A method for predicting future location of mobile user for location-based services system

Convergence of location-aware devices, wireless communications, and geographic information system (GIS) functionalities has been enabling the deployment of a new generation of selective information disseminating services and location-based services (LBSs). Current LBSs use information about current locations of users to provide services, such as nearest features of interest, they request. Although the common computing strategy in LBSs benefits the users, there are additional benefits when future locations are predicted. One major advantage of location prediction is that it provides LBSs with extended resources, mainly time, to improve system reliability which in turn increases the users’ confidence and the demand for LBSs. In this study, we propose a movement Rule-based Location Prediction method (RLP), to guess the user’s future location for LBSs. Its performance is assessed with respect to precision and recall. In comparison with the previous technique, the prediction accuracy of ours is higher. With the proposed approach, we give an efficient support to the LBSs provider in monitoring user intelligently and sending information to user in a push-driven fashion. Apart from the support of timely and desired services and enhanced automation, the technique helps overcome some existing issues such as network flooding due to the massive tracking of users, the latencies of the positioning systems in providing and information delivery. Accordingly, the positioning is more reliable, which enables the service provider to effectively and efficiently offer location-based services with high frequency.

The Predictive User Mobility Profile Framework for Wireless Multimedia Networks

User mobility profile (UMP) is a combination of historic records and predictive patterns of mobile terminals, which serve as fundamental information for mobility management and enhancement of quality of service (QoS) in wireless multimedia networks. In this paper, a UMP framework is developed for estimating service patterns and tracking mobile users, including descriptions of location, mobility, and service requirements. For each mobile user, the service requirement is estimated using a mean-square error method. Moreover, a new mobility model is designed to characterize not only stochastic behaviors, but historical records and predictive future locations of mobile users as well. Therefore, our approach incorporates aggregate history and current system parameters to acquire UMP. In particular, an adaptive algorithm is designed to predict the future positions of mobile terminals in terms of location probabilities based on moving directions and residence time in a cell. Simulation results are shown to indicate that the proposed schemes are effective on mobility and resource management by evaluating blocking/dropping probabilities and location tracking costs in wireless networks.