中文摘要：

当前基于数据挖掘的MIPv6切换算法通过对移动轨迹的关联性挖掘进行有效的移动预测从而实现平滑切换。考虑到移动终端有限的计算能力和存储容量决定了传统的数据挖掘算法并不适用的情况,提出一种低消耗的基于数据挖掘的FMIPv6切换算法（LCTWP-FMIPv6）。通过减少对移动轨迹数据集的扫描范围从而减小了数据挖掘过程的计算量与存储空间占用,同时将LCTWP-FMIPv6切换算法在Android移动终端上进行实现。对比实验结果表明,LCTWP-FMIPv6切换算法在保证移动切换过程平滑与高效的同时在数据挖掘过程中的耗时也比传统数据挖掘算法有明显的减少。

英文摘要：

Current MIPv6 handover algorithms based on data mining can predict mobility effective by mining association in moving trajectory database in order to achieve a smooth handover. The limited computing power and storage capacity of mobile terminals determined the case of traditional data mining algorithms did not apply,this paper proposed a low consumption FMIPv6 handover algorithm which named LCTWP-FMIPv6 based on data mining algorithms. The amount of compute and storage space occupied by the data mining process decreases by reducing the number of scanning to the movement trajectory data sets and the LCTWP-FMIPv6 algorithm transplanted to a mobile terminal based on Android operating system. Comparing experimental results show that,the time-consuming aspects of LCTWP-FMIPv6 handover algorithm in data mining process have significantly reduced than the traditional data mining algorithms while ensuring the smoothness and efficiency of mobile handover process.