Accurate Mobility Modeling and Location Prediction Based on Pattern Analysis of Handover Series in Mobile Networks

Abstract

The efficient dimensioning of cellular wireless access networks depends highly on the accuracy of the underlying mathematical models of user distribution and traffic estimations. Mobility prediction also considered as an effective method contributing to the accuracy of IP multicast based multimedia transmissions, and ad hoc routing algorithms. In this paper we focus on the tradeoff between the accuracy and the complexity of the mathematical models used to describe user movements in the network. We propose mobility model extension, in order to utilize user's movement history thus providing more accurate results than other widely used models in the literature. The new models are applicable in real-life scenarios, because these rely on additional information effectively available in cellular networks (e.g. handover history), too. The complexity of the proposed models is analyzed, and the accuracy is justified by means of simulation.

Mitigation of Redundant Handovers to Femtocells by Estimation of throughput Gain

“…Prediction-based algorithms can be exploited for handover to improve its efficiency (see, e.g., [22][23][24]). The prediction-based approaches reach high efficiency in determination of the target MBS.…”

A Handover Security Mechanism Employing the Diffie-Hellman Key Exchange Approach for the IEEE802.16e Wireless Networks

“…Several fast handover schemes have been proposed. Mobility prediction proposed by Fülöp et al [13] and the Client-based Mobility Frame System also introduced by Fülöp et al [14] are two examples. Yang et al [25] considered self-similarity in data traffic, handover, and frequency reuse to estimate the spectrum requirements of mobile networks so as to speed up communication and shorten SDT.…”

Performance Evaluation of Acoustic Underwater Data Broadcasting Exploiting the Bandwidth-Distance Relationship

“…Since the server a) possesses knowledge of its position, b) it broadcasts data items that concern the environmental surroundings at certain distances and c) it knows which items contain information that concern specific positions at specific distances, it will be able to broadcast data items to different areas at different channels that are determined from the server's distance to that area. The underwater clients on the other hand can pinpoint their position via their on-board location mechanism [3][4][5]22] and since the position of the server is known, each client can compute its distance from the server and thus determine the channel (center frequency and bandwidth) over which it will receive data from the server.…”