NDLI: A Hybrid Mobility Prediction (HMP) strategy for PCS networks

Content Provider	Springer Nature Link
Author	Saleh, Ahmed Ibrahim Mohammed
Copyright Year	2015
Abstract	Recently, several high-speed applications have been emerged for personal communication service (PCS) networks. Therefore, locating mobile terminals (MTs) as they move from one place to another is the key to provide continuous services with unrestricted mobility. To maintain the continuity of service during handoffs, PCS involves the need to predict MTs’ movements to cope with resource limitations and heterogeneity. Predicting MT’s next location in PCS networks has become an inherently interesting and challenging problem. If we can correctly predict the next cell to which MTs are going, the performance of wireless applications, such as call admission control, QoS, and mobility management can be improved as well. Mobility prediction based on MTs’ movement history has become an effective mean not only to decrease call-dropping probability, but also to shorten the handover latency. However, applying various basic prediction schemes to realistic environments has reported several limitations. Moreover, the randomness of MT’s motion prevents the accurate prediction. In this paper, a Hybrid Mobility Prediction (HMP) strategy has been proposed. HMP provides flexible usage of the limited resources of PCS networks. It combines evidence from three different predictors, which are; probabilistic predictor (PP), group-based predictor (GP), and spatial predictor (SP). PP relies on Bayes theorem, GP uses ant colony optimization (ACO), while SP tries to detect the topological architecture of the current registration area to enhance the prediction process. HMP provides high accurate prediction of the next cell to which an MT is going, which avoids over reservation resulting in a waste of resources. Experimental results have shown that HMP outperforms recent prediction strategies as the amount of reserved resources is dynamically adjusted based on the predicted cell.
Starting Page	173
Ending Page	206
Page Count	34
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	19
Issue Number	1
e-ISSN	1433755X
Language	English
Publisher	Springer London
Publisher Date	2015-05-13
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	PCS Resource reservation Handoff Location prediction Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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