Adaptive Mobility Prediction For Location Management Using Mobile Positioning


In this paper, an adaptive location management scheme for mobile wireless networks is presented based on the Gauss-Makov mobility model proposed in [LIA99]. The scheme exploits the mobility information gathered from mobile positioning devices (e.g., GPS) to help predict the future position of the mobile. A conventional dead-reckoning approach is used to determine the appropriate time to perform location updates; the mobile periodically compares its current location (obtained by the location positioning device) with the predicted location and sends an autonomous location update whenever the prediction error exceeds a certain threshold. The scheme adapts well to the changes of the user's mobility model since it uses the latest measured mobility information. A simulation model has been used to evaluate the prediction scheme. The performance results show that the simple average velocity based prediction algorithm gives improvement over the non-predictive distanced-based scheme and achieves a good performance on less random mobility models. It has a good balance of low paging cost and acceptable location update overhead. Moreover, we give an approximation method to estimate the user's mobility parameters and evaluate the performance of the algorithm with these parameters. With the availability of GPS mobility measurements, the system can disable the predictive scheme and revert to the traditional distance based scheme when the degree of movement randomness exceeds certain level. On the long run, this feature reduces the computational overhead and power consumption of the location management scheme.

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Proceedings - IEEE International Conference on Third Generation Wireless Communications

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Article; Proceedings Paper

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23844466695 (Scopus)

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