An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network

摘要: One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient technique particularly needed these networks. The incurs overheads on transmission process. These affect properties network such as delay, denial services, manual filtering bandwidth. main goal this research to enhance a scheme in through three phases. Firstly, current techniques will be investigated. Secondly, innovation examination new based hypothesises that are suitable mobile user (MU) resources with low computation cost high success rate without using MU Thirdly, generated different levels prediction. In thesis, LBSs proposed. It could considered combination cell routing area (RA) levels. For level prediction, most location focused generalized models, where geographic extent divided into regularshape cells. models not certain objectives compute present on-road services. Such New MarkovBased Mobility Prediction (NMMP) Model (PLM) deal inner structure respectively. NMMP PLM suffer from complex computation, accuracy regression insufficient accuracy. Sector Snapshot (LPSS) introduced, which Novel Cell Splitting Algorithm (NCPA). This algorithm implemented micro parallel technique. LPSS technique, compared two classic experimental results, shows effectiveness robustness splitting