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

As the e-coupon group-buying is getting popular, there are many sellers using e-coupon group-buying as an advertising strategy by providing e-coupon with big discount. However, there were many complaints from consumers saying that they couldn¡¦t reserve the services on peak time as they wished. It turns out that the sellers could not achieve the advertising effect but the negative image. We found the good time for sellers doing advertising by selling e-coupon is on the off-peak time. Based on above observation, this study tried to explore whether the service industry can enhance the impulse buying behavior and thus improve yield management performance by selling e-coupons through mobile devices. An online experiment with questionnaire was implemented to collect research data. The research results include: (1) The quantity of e-coupon provided by the sellers will not have significant impact on time pressure perceived by the consumers directly. However, if the limited quantity lets consumer recognize its scarcity, it will increase consumer perceived time pressure and perceived value as well. (2) In addition to the perceived scarcity, the e-coupon discount also has positive influence on consumer perceived value. (3) The shorter distance the location of the store is, the better perceived location will be. (4) The consumer impulse buying intention will be impacted by the perceived time pressure, perceived value, perceived location and impulse characteristic of the consumer. Among them, the perceived value has highest influence and the second is consumer impulse characteristic followed by the perceived location.

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