Handoff Management in Wireless Data Networks Using Topography-Aware Mobility Prediction

摘要

Recently, there has been a rapid growth of efforts in research and development to provide mobile users the means of seamless communications through wireless media. Wireless service providers are currently offering new data services such as image transfer, videoconferencing, data transfer, and access to an increasing number of content providers. These new wireless data services are noticeably suffering from poor quality compared to wired versions. This can be attributed to the fact that the infrastructure is inherently uncertain and resource poor. The uncertainty about user mobility is the root behind an evident waste of wireless resources, which are already limited, due to unnecessary resource reservations. In this paper, we introduce a handoff prediction and enhancement scheme (HOPES) that aims to explore the use of prediction techniques in mobility managements in order to improve the end-to-end traffic quality. The fundamental difference between HOPES and other predictive mobility-management techniques is that HOPES uses a topography-aware predictive approach that combines the mobile host's movement history, current state, and the topography of the surrounding area. The proposed architecture provides the network with timely information necessary to proactively respond to user movements instead of passively handling it after it happens. The value of this work is demonstrated through a comparative study that includes other mobility-prediction models. The comparison involved monitoring a simulated transmission control protocol/Internet protocol (TCP/IP) session where traffic flows from a correspondent host to a mobile host while it is moving. Several performance metrics were used to assess the end-to-end traffic improvement achieved from the proposed model. These metrics include packet loss, data delivery ratio, reserved bandwidth, retransmitted packets per received packet, and TCP session duration. The results obtained for HOPES show a significant improvement in traffic quality without sacrificing the utilization of network resources. This can be attributed to the fact that the topography-aware mobility modeling and prediction suggested by HOPES is more accurate in predicting mobility patterns.